From ea3bff4cec9f7f2982103fe698b6b1f392cf920f Mon Sep 17 00:00:00 2001 From: fsae computer <> Date: Wed, 24 Apr 2024 19:42:29 +1200 Subject: [PATCH 1/9] fixing less cone problem --- .../path_planning/path_planning/trajectory_generation.py | 4 ++-- .../path_planning/path_planning/trajectory_optimisation_CS.py | 4 ++-- src/planning/path_planning/setup.py | 3 ++- 3 files changed, 6 insertions(+), 5 deletions(-) diff --git a/src/planning/path_planning/path_planning/trajectory_generation.py b/src/planning/path_planning/path_planning/trajectory_generation.py index 44ea781..08fb491 100644 --- a/src/planning/path_planning/path_planning/trajectory_generation.py +++ b/src/planning/path_planning/path_planning/trajectory_generation.py @@ -159,10 +159,10 @@ def get_position_of_cart(self, cone_map): return x, y, theta def get_transformation_matrix(self, position_and_orientation): - # theta = position_and_orientation[2] - np.pi/2 + theta = position_and_orientation[2] - np.pi/2 cart_x = position_and_orientation[0] cart_y = position_and_orientation[1] - theta = position_and_orientation[2] + # theta = position_and_orientation[2] # 2d trasformation matrix rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]]) position_vector = np.array([[cart_x], [cart_y]]) diff --git a/src/planning/path_planning/path_planning/trajectory_optimisation_CS.py b/src/planning/path_planning/path_planning/trajectory_optimisation_CS.py index 14487d6..4a5bfef 100644 --- a/src/planning/path_planning/path_planning/trajectory_optimisation_CS.py +++ b/src/planning/path_planning/path_planning/trajectory_optimisation_CS.py @@ -76,8 +76,8 @@ def set_boundaries(self, msg: ConeMap): rightboundary.append([x,y]) # interpolate - funcL = interpolate.interp1d([P[0] for P in leftboundary], [P[1] for P in leftboundary], kind='slinear') - funcR = interpolate.interp1d([P[0] for P in rightboundary], [P[1] for P in rightboundary], kind='slinear') + funcL = interpolate.interp1d([P[0] for P in leftboundary], [P[1] for P in leftboundary], kind='cubic') + funcR = interpolate.interp1d([P[0] for P in rightboundary], [P[1] for P in rightboundary], kind='cubic') xlrange = np.linspace(min([P[0] for P in leftboundary]), max([P[0] for P in leftboundary])) xrrange = np.linspace(min([P[0] for P in rightboundary]), max([P[0] for P in rightboundary])) diff --git a/src/planning/path_planning/setup.py b/src/planning/path_planning/setup.py index fd707dd..4e4fcb2 100644 --- a/src/planning/path_planning/setup.py +++ b/src/planning/path_planning/setup.py @@ -28,7 +28,8 @@ 'trajectory_optimisation = path_planning.trajectory_optimisation_CS:main', 'center_line = path_planning.simple_centerline_planner:main', 'shortest_path = path_planning.trajectory_shortest_path:main', - 'shortest_path_viz = path_planning.shortest_path_viz:main' + 'shortest_path_viz = path_planning.shortest_path_viz:main', + 'trajectory_generation = path_planning.trajectory_generation:main' ], }, ) From 1ca93e945ba3fbeda8e8040178688cba16726c45 Mon Sep 17 00:00:00 2001 From: Adrian Date: Wed, 24 Apr 2024 21:28:08 +1200 Subject: [PATCH 2/9] trying cone fix for center line --- src/moa/moa_bringup/launch/sim_launch.py | 14 ++++++++ .../trajectory_follower_p_controller.py | 4 +-- .../path_planning/trajectory_generation.py | 4 +-- .../trajectory_optimisation_CS.py | 32 +++++++++---------- 4 files changed, 34 insertions(+), 20 deletions(-) diff --git a/src/moa/moa_bringup/launch/sim_launch.py b/src/moa/moa_bringup/launch/sim_launch.py index 125c7bd..72cc465 100644 --- a/src/moa/moa_bringup/launch/sim_launch.py +++ b/src/moa/moa_bringup/launch/sim_launch.py @@ -4,6 +4,13 @@ def generate_launch_description(): return launch.LaunchDescription([ # cone detect + launch_ros.actions.Node( + package='aruco_detection', + executable='aruco_detection', + name='aruco_detection' + ), + + # cone mapping launch_ros.actions.Node( package='cone_mapping', executable='dbscan', @@ -47,4 +54,11 @@ def generate_launch_description(): executable='visualizer', name='track_viz', ), + + # launch_ros.actions.Node( + # package='foxglove_bridge', + # executable='foxglove_bridge', + # name='foxglove_bridge', + # parameters=[{'port':8765}] + # ), ]) \ No newline at end of file diff --git a/src/moa/moa_controllers/moa_controllers/trajectory_follower_p_controller.py b/src/moa/moa_controllers/moa_controllers/trajectory_follower_p_controller.py index ea4c43b..23f261f 100644 --- a/src/moa/moa_controllers/moa_controllers/trajectory_follower_p_controller.py +++ b/src/moa/moa_controllers/moa_controllers/trajectory_follower_p_controller.py @@ -24,7 +24,7 @@ def __init__(self): # ('debug', False) # ] # ) - self._distance_to_front = 3.0 + self._distance_to_front = 0.9 qos_profile = QoSProfile( reliability=QoSReliabilityPolicy.BEST_EFFORT, @@ -84,7 +84,7 @@ def callback(self, msg:ConeMap): # publish msgs args = {"steering_angle": float(steering_angle_deg), "steering_angle_velocity": 0.0, - "speed": 7.0, + "speed": 3.0, "acceleration": 0.0, "jerk": 0.0} diff --git a/src/planning/path_planning/path_planning/trajectory_generation.py b/src/planning/path_planning/path_planning/trajectory_generation.py index 08fb491..44ea781 100644 --- a/src/planning/path_planning/path_planning/trajectory_generation.py +++ b/src/planning/path_planning/path_planning/trajectory_generation.py @@ -159,10 +159,10 @@ def get_position_of_cart(self, cone_map): return x, y, theta def get_transformation_matrix(self, position_and_orientation): - theta = position_and_orientation[2] - np.pi/2 + # theta = position_and_orientation[2] - np.pi/2 cart_x = position_and_orientation[0] cart_y = position_and_orientation[1] - # theta = position_and_orientation[2] + theta = position_and_orientation[2] # 2d trasformation matrix rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]]) position_vector = np.array([[cart_x], [cart_y]]) diff --git a/src/planning/path_planning/path_planning/trajectory_optimisation_CS.py b/src/planning/path_planning/path_planning/trajectory_optimisation_CS.py index 4a5bfef..9110aa2 100644 --- a/src/planning/path_planning/path_planning/trajectory_optimisation_CS.py +++ b/src/planning/path_planning/path_planning/trajectory_optimisation_CS.py @@ -75,20 +75,20 @@ def set_boundaries(self, msg: ConeMap): elif cones[i].colour == 2: rightboundary.append([x,y]) - # interpolate - funcL = interpolate.interp1d([P[0] for P in leftboundary], [P[1] for P in leftboundary], kind='cubic') - funcR = interpolate.interp1d([P[0] for P in rightboundary], [P[1] for P in rightboundary], kind='cubic') - - xlrange = np.linspace(min([P[0] for P in leftboundary]), max([P[0] for P in leftboundary])) - xrrange = np.linspace(min([P[0] for P in rightboundary]), max([P[0] for P in rightboundary])) - self.get_logger().info(f"length of xlrange = {len(xlrange)}") - leftboundary = [] - rightboundary = [] - for P in xlrange: - leftboundary.append([P,funcL(P)]) - - for P in xrrange: - rightboundary.append([P,funcR(P)]) + # # interpolate + # funcL = interpolate.interp1d([P[0] for P in leftboundary], [P[1] for P in leftboundary], kind='slinear') + # funcR = interpolate.interp1d([P[0] for P in rightboundary], [P[1] for P in rightboundary], kind='slinear') + + # xlrange = np.linspace([P[0] for P in leftboundary][0], [P[0] for P in leftboundary][-1], 50) + # xrrange = np.linspace([P[0] for P in rightboundary][0], [P[0] for P in rightboundary][-1], 50) + # self.get_logger().info(f"length of xlrange = {len(xlrange)}") + # leftboundary = [] + # rightboundary = [] + # for P in xlrange: + # leftboundary.append([P,funcL(P)]) + + # for P in xrrange: + # rightboundary.append([P,funcR(P)]) # get correspnoding right boundary based on left boundary x1, y1, x2, y2 = leftboundary[0][0], leftboundary[0][1], rightboundary[0][0], rightboundary[0][1] @@ -507,10 +507,10 @@ def get_center_line(self): coods.append(self.get_avg_point(x1,y1,x2,y2)) # # perform extrapolation to extend line - # func = interpolate.interp1d([P[0] for P in coods], [P[1] for P in coods], kind='cubic', fill_value='extrapolate') + # func = interpolate.interp1d([P[0] for P in coods], [P[1] for P in coods], kind='quadratic') # into_future_points = 0 - # into_future_distance = 0 + # into_future_distance = 0.1 # # track direction # if coods[-1][0] > coods[-2][0]: From 272ef3661934a1d04f86fe065f7655dec4306f65 Mon Sep 17 00:00:00 2001 From: fsae computer <> Date: Mon, 29 Apr 2024 18:34:02 +1200 Subject: [PATCH 3/9] fixing new path planning --- .../Race lines/$track_name optimal.png | Bin 214878 -> 412772 bytes .../shortest_path/Track images/nodes.png | Bin 320945 -> 330661 bytes .../shortest_path/TrackMethods.py | 16 +++--- .../path_planning/shortest_path/main.py | 49 ++++++++++++++---- .../path_planning/trajectory_shortest_path.py | 9 ++-- 5 files changed, 54 insertions(+), 20 deletions(-) diff --git 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zlCBG@(7j3V1@&KIc{E(R+|f4=$%Avz483)yGxHl)N4Y8Tf;z{Ys`&_$hesgMWGN;9 zllQ8Fwy=-zGy4cq7m@KWOu#gfp+M!H3(PzM#;B5{1rFK_xG2ksA`e)(W}Ze~C5)U_ zc~S7A{Y!a-?;{acxotrN0LdFk!^gwL$|EESGq}1+&YkNa*{EB=r_cnZUPGh4Jb`UmyOV`w~7A!DDimB}!X-0Z~g5%!W>c z-9$#dt-LvlPC3cCPHhtSeT0H(SK$85Raa^JoB*PVX6e-f`1c__2T{|+IUumIQ`F!? zw`Mwb#lR~QL>;{9kQfvT13gw9aExqP^p#~c6Ih556r?d;6o*djG8-h{jR)u815ai@um}4(=mm{@ zE!_C2-|E9z&|m*Q*8}w>aJ#>KeR{&b9>=HC_;eb>C-BJ Date: Thu, 2 May 2024 00:28:58 +1200 Subject: [PATCH 4/9] fixed starting node to 0 cost --- .../path_planning/shortest_path/CoreModels.py | 4 +- .../shortest_path/Track images/nodes.png | Bin 330661 -> 257517 bytes .../shortest_path/TrackMethods.py | 80 +++++++++++------- .../path_planning/shortest_path/main.py | 22 +++-- .../path_planning/trajectory_shortest_path.py | 3 +- 5 files changed, 61 insertions(+), 48 deletions(-) diff --git a/src/planning/path_planning/path_planning/shortest_path/CoreModels.py b/src/planning/path_planning/path_planning/shortest_path/CoreModels.py index f9ebc95..0dccc8a 100644 --- a/src/planning/path_planning/path_planning/shortest_path/CoreModels.py +++ b/src/planning/path_planning/path_planning/shortest_path/CoreModels.py @@ -10,8 +10,8 @@ def __init__(self, node, entryVector, velocity=0.0, cost=np.inf, previousNode=Fa self._cost = cost self._previousNode = previousNode self._nextState = nextState - self._max = Imax - self._min = Imin + # self._max = Imax + # self._min = Imin class Node(): def __init__(self, bid, xy, innerdistance, outerdistance) -> None: diff --git a/src/planning/path_planning/path_planning/shortest_path/Track images/nodes.png b/src/planning/path_planning/path_planning/shortest_path/Track images/nodes.png index d54bb60d181b208448da05c127b1eedc6e7d2a69..688e77a9ac25e7a0c154f55b9d437c94241440ff 100644 GIT binary patch literal 257517 zcmeFad0ftU_dkA_MrKBq#!@MjW$a5ts}@tlpb$dZ5mISWS})V6D8+=37DBR=M5UcJ zi3(A)Tojc`N&EhNp7(V{&2oRgfBYVg-#>GGKKDIy)Ae3nuk$+Rd7kHaPDj^kYK;16 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d_start)[0] + + df = pd.DataFrame({ + "outer": df.outer[df_range], + "inner": df.inner[df_range], + "cline": df.cline[df_range], + "p_vector": df.p_vector[df_range], + "lap_distance": df.lap_distance[df_range], + }) + brackets = brackets[df_range] + + return df, brackets + # def belman_ford_path(df, velocity_range, brackets, start_node, track_name=None, plot:bool=False): # # Initialise first set of paths from first bracket # for node in brackets[-1]._nodeList: @@ -356,8 +371,10 @@ def getBrackets(df:pd.DataFrame, n_nodes, plot:bool=False): # return start_node -def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.array, n_vel, μ, mass, α, α_d, max_steer_angle, tire_width, wheelbase, max_velocity, plot): +def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.array, n_vel, plot:bool, CAR:dict): """keeps ALL state from each pair of node state combination""" + mass, μ, α, α_d, max_steer_angle, max_velocity, tire_width, wheelbase = CAR.values() + traction_force = PathHelpers.getMaxTractionForce(μ, mass) velocity_range = np.linspace(0, max_velocity, n_vel) min_steer_rad= wheelbase/np.sin(np.deg2rad(max_steer_angle)) + 0.5* tire_width @@ -366,7 +383,7 @@ def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.a for velocity in velocity_range: for previous_node in brackets[-2]._nodeList: entry_vector = TrackHelpers.getVector(previous_node._xy, node._xy, True) - state = State(node, entry_vector, velocity, 0.0, previous_node, False) + state = State(node, entry_vector, velocity, 0.0, previous_node) node._stateList.append(state) for i in range(len(brackets)-1,0,-1): @@ -389,8 +406,7 @@ def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.a # Initialising statelist for the current node for previous_node in previous_node_list: entry_vector = TrackHelpers.getVector(previous_node._xy, current_node._xy, True) - current_node._stateList.append(State(current_node, entry_vector, 0.0, np.inf, previous_node, False, True)) - current_node._stateList.append(State(current_node, entry_vector, 0.0, np.inf, previous_node, False, False, True)) + current_node._stateList.append(State(current_node, entry_vector, 0.0, np.inf, previous_node, False)) for velocity in velocity_range: state = State(current_node, entry_vector, velocity, np.inf, previous_node) current_node._stateList.append(state) @@ -398,43 +414,44 @@ def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.a for next_node in brackets[i]._nodeList: distance_between_nodes = TrackHelpers.getDistance(current_node._xy, next_node._xy) for current_state in current_node._stateList: - prev_xy = current_state._xy-np.array(current_state._entryVector)*distance_between_nodes if i == 1 else current_state._previousNode._xy - traction_velocity, radius = PathHelpers.getTractionVelocity3p(prev_xy, current_node._xy,next_node._xy,traction_force, mass) + prev_xy = current_state._previousNode._xy if i != 1 else current_state._xy-np.array(current_state._entryVector)*distance_between_nodes + traction_velocity, radius = PathHelpers.getTractionVelocity3p(prev_xy, current_node._xy, next_node._xy, traction_force, mass) + # print(traction_velocity, radius) if radius < min_steer_rad: continue # skip current current state for next_node_state in next_node._stateList: if next_node_state._previousNode == current_node: - min_va, max_va = PathHelpers.minmaxAccelerationVelocity(next_node_state._velocity, distance_between_nodes,α,α_d) + min_va, max_va = PathHelpers.minmaxAccelerationVelocity(next_node_state._velocity, distance_between_nodes, α, α_d) if traction_velocity < min_va: continue # skip next state - if current_state._min: - # accelerating, optimal minimum speed state - ideal_velocity = min_va - traverse_time = ((2*distance_between_nodes)/(ideal_velocity+next_node_state._velocity)) + next_node_state._cost - if traverse_time < current_state._cost: - current_state._velocity = ideal_velocity - current_state._cost = traverse_time - current_state._nextState = next_node_state - elif current_state._max: - # braking, optimum maximum speed state - ideal_velocity = min(traction_velocity, max_va, max_velocity) - traverse_time = ((2*distance_between_nodes)/(ideal_velocity+next_node_state._velocity)) + next_node_state._cost + # if current_state._min: + # # accelerating, optimal minimum speed state + # ideal_velocity = min_va + # traverse_time = ((2*distance_between_nodes)/(ideal_velocity+next_node_state._velocity)) + next_node_state._cost + # if traverse_time < current_state._cost: + # current_state._velocity = ideal_velocity + # current_state._cost = traverse_time + # current_state._nextState = next_node_state + # elif current_state._max: + # # braking, optimum maximum speed state + # ideal_velocity = min(traction_velocity, max_va, max_velocity) + # traverse_time = ((2*distance_between_nodes)/(ideal_velocity+next_node_state._velocity)) + next_node_state._cost + # if traverse_time < current_state._cost: + # current_state._velocity = ideal_velocity + # current_state._cost = traverse_time + # current_state._nextState = next_node_state + + # non ideal state + if min_va <= current_state._velocity <= min(traction_velocity, max_va, max_velocity): + traverse_time = ((2*distance_between_nodes)/(current_state._velocity+next_node_state._velocity)) + next_node_state._cost if traverse_time < current_state._cost: - current_state._velocity = ideal_velocity + # print('called') current_state._cost = traverse_time current_state._nextState = next_node_state - # non ideal state - else: - if min_va <= current_state._velocity <= min(traction_velocity, max_va, max_velocity): - traverse_time = ((2*distance_between_nodes)/(current_state._velocity+next_node_state._velocity)) + next_node_state._cost - if traverse_time < current_state._cost: - current_state._cost = traverse_time - current_state._nextState = next_node_state - # for state in current_node._stateList # if state._cost == Inf; deleteState!(state); end @@ -442,7 +459,7 @@ def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.a # get optimal/best path by iterating through EVERY SINGLE state LOL print("getting best path") best_xy, best_velocities, cost = getBestStates(start_node) - print(best_xy, "\n", best_velocities,"\n") + print(best_xy, "\n\n", best_velocities,"\n") if plot: # plotting @@ -467,10 +484,9 @@ def getBestStates(start_node:Node): velocities = [] for state in start_node._stateList: current_state = state - tmp_cost = current_state._cost while current_state: best_xy.append(current_state._xy) - velocities.append(current_state._velocity) + velocities.append(current_state._velocity * 3.6) current_state = current_state._nextState return best_xy, velocities, cost diff --git a/src/planning/path_planning/path_planning/shortest_path/main.py b/src/planning/path_planning/path_planning/shortest_path/main.py index bc15028..28642fa 100644 --- a/src/planning/path_planning/path_planning/shortest_path/main.py +++ b/src/planning/path_planning/path_planning/shortest_path/main.py @@ -33,13 +33,18 @@ def main(): # create brackets print("CREATING BRACKETS") - brackets = TrackMethods.getBrackets(df, 4, False) + brackets = TrackMethods.getBrackets(df, 10, True) + d_start = 1900 + d_end = 2000 + section_of_track = False + if section_of_track: + df, brackets = TrackMethods.getSectionofTrack(df, np.array(brackets), d_start, d_end) print("COMPUTING OPTIMAL PATH") # mass = 50.0 # μ = 0.7 - start_node = brackets[0]._nodeList[0] - start_node._stateList.append(State(start_node, [np.cos(0), np.sin(0)], 0.0)) + start_node = brackets[0]._nodeList[4] + start_node._stateList.append(State(start_node, -1/df.p_vector.tolist()[0], 0.0, np.Inf)) print("starting inner distance: ",start_node._innerDistance) print("starting outer distance: ", start_node._outerDistance) # start_node = TrackMethods.belman_ford_path(df, velocity_range, brackets, start_node, plot=self._plot) @@ -50,15 +55,8 @@ def main(): start_node, brackets, n_vel, - CAR["μ"], - CAR["mass"], - CAR["α"], - CAR["α_d"], - CAR["max steer angle"], - CAR["tire width"], - CAR["wheelbase"], - CAR["max velocity"], - True + True, + CAR ) # start_node = TrackMethods.optimise_path(track_name, df, brackets, start_node, mass, μ) print("\nOPTIMAL PATH COMPUTED") diff --git a/src/planning/path_planning/path_planning/trajectory_shortest_path.py b/src/planning/path_planning/path_planning/trajectory_shortest_path.py index 0f075e5..a7c4b83 100644 --- a/src/planning/path_planning/path_planning/trajectory_shortest_path.py +++ b/src/planning/path_planning/path_planning/trajectory_shortest_path.py @@ -102,8 +102,7 @@ def set_boundaries(self, msg:ConeMap): print("starting outer distance: ", start_node._outerDistance) # start_node = TrackMethods.belman_ford_path(df, velocity_range, brackets, start_node, plot=self._plot) n_vel = 5 - start_node, brackets, optimal_cost = - velocity_range = [0.01, 8, 16, 24, 32, 40 ]# velocities in meters per secondTrackMethods.optimal_path( + start_node, brackets, optimal_cost = TrackMethods.optimal_path( "$track_name optimal", df, start_node, From edd7a083983da03061473d61d13f10a1dd873f12 Mon Sep 17 00:00:00 2001 From: Tanish Bhatt Date: Tue, 7 May 2024 01:24:04 +1200 Subject: [PATCH 5/9] i think new path planning works now... --- .../path_planning/shortest_path/CoreModels.py | 2 +- .../Race lines/$track_name optimal.png | Bin 412772 -> 254596 bytes .../shortest_path/Track images/nodes.png | Bin 257517 -> 306570 bytes .../shortest_path/TrackHelpers.py | 11 +++-- .../shortest_path/TrackMethods.py | 45 ++++++++++-------- .../path_planning/shortest_path/main.py | 13 ++--- .../path_planning/trajectory_shortest_path.py | 40 +++++++--------- 7 files changed, 57 insertions(+), 54 deletions(-) diff --git a/src/planning/path_planning/path_planning/shortest_path/CoreModels.py b/src/planning/path_planning/path_planning/shortest_path/CoreModels.py index 0dccc8a..e9e8def 100644 --- a/src/planning/path_planning/path_planning/shortest_path/CoreModels.py +++ b/src/planning/path_planning/path_planning/shortest_path/CoreModels.py @@ -10,7 +10,7 @@ def __init__(self, node, entryVector, velocity=0.0, cost=np.inf, previousNode=Fa self._cost = cost self._previousNode = previousNode self._nextState = nextState - # self._max = Imax + self._max = Imax # self._min = Imin class Node(): diff --git a/src/planning/path_planning/path_planning/shortest_path/Race lines/$track_name optimal.png b/src/planning/path_planning/path_planning/shortest_path/Race lines/$track_name optimal.png index 89b4d0926f6c886f4b6ddcfcdeb4d91b1445b444..51997f14faf24e10280c111a8c5c4d46e6ca0152 100644 GIT binary patch literal 254596 zcmeFZXH?Z^w>@g!yh%=qF^UM%j13hO+|mRoN$fP0Dm{XLbZH`@;G4t(QdFe3sMJjh zC{m;t-LR!cV1pnfQl(4paOc|Qod3zV_v5`^?ucU~DpK}-eyhwi=Uk8PNzJ3HzYzFh z)v8sij~_dvwQAKG{PD~0{_9ixmax0=2mB`QcKEcLwv&~c#~ByPRT^j9&f7b=+1r?Z 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z_TT;R`J%VK7JuWMBXLA^)z)zvo2w_wM{_3WR^nqwudO_}3H& r|C)k-O@Z*QDG>fO1^*wKg2f&;{co*4=GDl2Wzu-Fac6#5^y~itn$#Ak diff --git a/src/planning/path_planning/path_planning/shortest_path/TrackHelpers.py b/src/planning/path_planning/path_planning/shortest_path/TrackHelpers.py index 9894cc0..d71ea5e 100644 --- a/src/planning/path_planning/path_planning/shortest_path/TrackHelpers.py +++ b/src/planning/path_planning/path_planning/shortest_path/TrackHelpers.py @@ -151,10 +151,13 @@ def Plot(nodes:bool, vector_list:np.array, label:str,col=None): def plotOptimal(vector_list:np.array, velocities:np.array, label:str): all_x = [P[0] for P in vector_list] all_y = [P[1] for P in vector_list] - col = cm.jet((velocities-np.min(velocities))/(np.max(velocities)-np.min(velocities))) - ax = plt.gca() + # col = cm.jet((velocities-np.min(velocities))/(np.max(velocities)-np.min(velocities))) + cmap = plt.cm.get_cmap("autumn_r") + ax = plt.subplot() for i in range(len(all_x)-1): - ax.plot([all_x[i], all_x[i+1]], [all_y[i], all_y[i+1]], c=col[i], label=label) - im = ax.scatter(all_x, all_y, c=velocities, s=0, cmap=cm.jet) + ax.plot([all_x[i], all_x[i+1]], [all_y[i], all_y[i+1]], c=cmap(velocities[i]/np.max(velocities)), label=label) + im = ax.scatter(all_x, all_y, c=velocities, s=0, cmap=cmap) + # im = cm.ScalarMappable(cmap=cmap) + # im = im.set_array(velocities) return im diff --git a/src/planning/path_planning/path_planning/shortest_path/TrackMethods.py b/src/planning/path_planning/path_planning/shortest_path/TrackMethods.py index 8584339..3787878 100644 --- a/src/planning/path_planning/path_planning/shortest_path/TrackMethods.py +++ b/src/planning/path_planning/path_planning/shortest_path/TrackMethods.py @@ -371,13 +371,14 @@ def getSectionofTrack(df, brackets, d_start, d_end): # return start_node -def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.array, n_vel, plot:bool, CAR:dict): +def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.array, n_vel, CAR:dict, plot:bool): """keeps ALL state from each pair of node state combination""" - mass, μ, α, α_d, max_steer_angle, max_velocity, tire_width, wheelbase = CAR.values() + attributes = ["mass", "μ", "α", "α_d", "max steer angle", "max velocity", "tire width", "wheelbase"] + mass, μ, α, α_d, max_steer_angle, max_velocity, tire_width, wheelbase = [CAR[key] for key in attributes] traction_force = PathHelpers.getMaxTractionForce(μ, mass) velocity_range = np.linspace(0, max_velocity, n_vel) - min_steer_rad= wheelbase/np.sin(np.deg2rad(max_steer_angle)) + 0.5* tire_width + min_steer_rad = wheelbase/np.sin(np.deg2rad(max_steer_angle)) + 0.5*tire_width for node in brackets[-1]._nodeList: for velocity in velocity_range: @@ -385,6 +386,8 @@ def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.a entry_vector = TrackHelpers.getVector(previous_node._xy, node._xy, True) state = State(node, entry_vector, velocity, 0.0, previous_node) node._stateList.append(state) + + start_node._stateList[0]._previousNode = Node(0,start_node._xy - 2*wheelbase*start_node._stateList[0]._entryVector, 0, 0) for i in range(len(brackets)-1,0,-1): # tmp = rclpyNode("tmp") @@ -406,7 +409,7 @@ def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.a # Initialising statelist for the current node for previous_node in previous_node_list: entry_vector = TrackHelpers.getVector(previous_node._xy, current_node._xy, True) - current_node._stateList.append(State(current_node, entry_vector, 0.0, np.inf, previous_node, False)) + current_node._stateList.append(State(current_node, entry_vector, 0.0, np.inf, previous_node, False, True)) for velocity in velocity_range: state = State(current_node, entry_vector, velocity, np.inf, previous_node) current_node._stateList.append(state) @@ -414,8 +417,8 @@ def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.a for next_node in brackets[i]._nodeList: distance_between_nodes = TrackHelpers.getDistance(current_node._xy, next_node._xy) for current_state in current_node._stateList: - prev_xy = current_state._previousNode._xy if i != 1 else current_state._xy-np.array(current_state._entryVector)*distance_between_nodes - traction_velocity, radius = PathHelpers.getTractionVelocity3p(prev_xy, current_node._xy, next_node._xy, traction_force, mass) + # prev_xy = current_state._previousNode._xy if i != 1 else current_state._xy-np.array(current_state._entryVector)*distance_between_nodes + traction_velocity, radius = PathHelpers.getTractionVelocity3p(current_state._previousNode._xy, current_node._xy, next_node._xy, traction_force, mass) # print(traction_velocity, radius) if radius < min_steer_rad: continue # skip current current state @@ -435,27 +438,28 @@ def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.a # current_state._velocity = ideal_velocity # current_state._cost = traverse_time # current_state._nextState = next_node_state - # elif current_state._max: - # # braking, optimum maximum speed state - # ideal_velocity = min(traction_velocity, max_va, max_velocity) - # traverse_time = ((2*distance_between_nodes)/(ideal_velocity+next_node_state._velocity)) + next_node_state._cost - # if traverse_time < current_state._cost: - # current_state._velocity = ideal_velocity - # current_state._cost = traverse_time - # current_state._nextState = next_node_state + if current_state._max: + # braking, optimum maximum speed state + ideal_velocity = min(traction_velocity, max_va, max_velocity) + traverse_time = ((2*distance_between_nodes)/(ideal_velocity+next_node_state._velocity)) + next_node_state._cost + if traverse_time < current_state._cost: + current_state._velocity = ideal_velocity + current_state._cost = traverse_time + current_state._nextState = next_node_state # non ideal state - if min_va <= current_state._velocity <= min(traction_velocity, max_va, max_velocity): + if min_va <= current_state._velocity < min(traction_velocity, max_va, max_velocity): traverse_time = ((2*distance_between_nodes)/(current_state._velocity+next_node_state._velocity)) + next_node_state._cost if traverse_time < current_state._cost: # print('called') current_state._cost = traverse_time current_state._nextState = next_node_state - + # for state in current_node._stateList # if state._cost == Inf; deleteState!(state); end - - + # executor = concurrent.futures.ProcessPoolExecutor(8) + # futures = [executor.submit(process_current_node, current_node) for current_node in current_node_list] + # concurrent.futures.wait(futures) # get optimal/best path by iterating through EVERY SINGLE state LOL print("getting best path") best_xy, best_velocities, cost = getBestStates(start_node) @@ -471,7 +475,8 @@ def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.a TrackHelpers.Plot(False, df.outer, "outer boundary") # optimal path im = TrackHelpers.plotOptimal(best_xy, best_velocities, "optimal race line") - plt.colorbar(im) + plt.colorbar(im, label='velocity') + plt.title("optimal line colored by velocity") p.savefig(f"{os.path.dirname(__file__)}/Race lines/{track_name}.png", dpi=600) plt.show() @@ -486,7 +491,7 @@ def getBestStates(start_node:Node): current_state = state while current_state: best_xy.append(current_state._xy) - velocities.append(current_state._velocity * 3.6) + velocities.append(current_state._velocity) current_state = current_state._nextState return best_xy, velocities, cost diff --git a/src/planning/path_planning/path_planning/shortest_path/main.py b/src/planning/path_planning/path_planning/shortest_path/main.py index 28642fa..16368d5 100644 --- a/src/planning/path_planning/path_planning/shortest_path/main.py +++ b/src/planning/path_planning/path_planning/shortest_path/main.py @@ -11,9 +11,9 @@ def main(): import numpy as np CAR = { - "mass": 200.0, # kg - "μ": 0.9, # static friction coefficient - dimensionless - "α": PathHelpers.noughtTo60(2.1), + "mass": 795.0, # kg + "μ": 1.6*0.8, # static friction coefficient - dimensionless + "α": PathHelpers.noughtTo60(1.7), "α_d": 39.0, "max steer angle": 16.0, # degrees "max velocity": 80.0, # m/s @@ -34,9 +34,9 @@ def main(): # create brackets print("CREATING BRACKETS") brackets = TrackMethods.getBrackets(df, 10, True) - d_start = 1900 + d_start = 1950 d_end = 2000 - section_of_track = False + section_of_track = True if section_of_track: df, brackets = TrackMethods.getSectionofTrack(df, np.array(brackets), d_start, d_end) @@ -44,6 +44,7 @@ def main(): # mass = 50.0 # μ = 0.7 start_node = brackets[0]._nodeList[4] + print(-1/df.p_vector.tolist()[0]) start_node._stateList.append(State(start_node, -1/df.p_vector.tolist()[0], 0.0, np.Inf)) print("starting inner distance: ",start_node._innerDistance) print("starting outer distance: ", start_node._outerDistance) @@ -55,8 +56,8 @@ def main(): start_node, brackets, n_vel, + CAR, True, - CAR ) # start_node = TrackMethods.optimise_path(track_name, df, brackets, start_node, mass, μ) print("\nOPTIMAL PATH COMPUTED") diff --git a/src/planning/path_planning/path_planning/trajectory_shortest_path.py b/src/planning/path_planning/path_planning/trajectory_shortest_path.py index a7c4b83..4457b70 100644 --- a/src/planning/path_planning/path_planning/trajectory_shortest_path.py +++ b/src/planning/path_planning/path_planning/trajectory_shortest_path.py @@ -28,7 +28,7 @@ def __init__(self): self.declare_parameters( namespace='', parameters=[ - ('plot', False), + ('plot', True), ('save_track', False), ] ) @@ -38,9 +38,9 @@ def __init__(self): self._save_track = self.get_parameter("save_track").get_parameter_value().bool_value # car properties self._CAR = { - "mass": 200.0, # kg - "μ": 0.9, # static friction coefficient - dimensionless - "α": PathHelpers.noughtTo60(2.1), + "mass": 795.0, # kg + "μ": 1.6*0.8, # static friction coefficient - dimensionless + "α": PathHelpers.noughtTo60(1.7), "α_d": 39.0, "max steer angle": 16.0, # degrees "max velocity": 80.0, # m/s @@ -90,43 +90,37 @@ def set_boundaries(self, msg:ConeMap): # create brackets print("CREATING BRACKETS") - brackets = TrackMethods.getBrackets(df, 10, plot=self._plot) + brackets = TrackMethods.getBrackets(df, 5, plot=self._plot) # velocity_range = [0.01, 8, 16, 24, 32, 40]# velocities in meters per second # compute optimal path print("COMPUTING OPTIMAL PATH") start_node = self.get_start_node(starting_point=car_position) # transformed car position - start_node._stateList.append(State(start_node, [np.cos(0), np.sin(0)], 0.0)) + # start_node = brackets[0]._nodeList[4] + start_node._stateList.append(State(start_node, np.array([np.cos(np.pi/2),np.sin(0)]), 0.0, np.Inf)) print("starting inner distance: ", start_node._innerDistance) print("starting outer distance: ", start_node._outerDistance) # start_node = TrackMethods.belman_ford_path(df, velocity_range, brackets, start_node, plot=self._plot) - n_vel = 5 + n_vel = 10 start_node, brackets, optimal_cost = TrackMethods.optimal_path( "$track_name optimal", df, start_node, brackets, n_vel, - self._CAR["μ"], - self._CAR["mass"], - self._CAR["α"], - self._CAR["α_d"], - self._CAR["max steer angle"], - self._CAR["tire width"], - self._CAR["wheelbase"], - self._CAR["max velocity"], + self._CAR, self._plot ) print("\nOPTIMAL PATH COMPUTED") # get steering angle based on current and next point - # p1 = start_node._xy # relative to global + p1 = start_node._xy # relative to global # self.get_logger().info(f"{p1}") # self.get_logger().info(f"{start_node._nextNode._xy}") # # p2 = self.get_transformed_point(msg, start_node._nextNode._xy) # relative to local - # p2 = start_node._nextNode._xy + p2 = start_node._stateList[0]._nextState._xy # self.get_logger().info(f"{p2}") # # p2 = start_node._nextNode._xy + start_node._xy # steering_angle = TrackHelpers.getAngleRotation(np.array(p1), np.array(p2)) @@ -136,12 +130,12 @@ def set_boundaries(self, msg:ConeMap): # publish msgs # self.steering_angle.publish(Float32(data=steering_angle)) - # points = [p1, p2] - # msg = PoseArray() - # for P in points: - # args = {"position": Point(x=P[0], y=P[1], z=0.0)} - # msg.poses.append(Pose(**args)) - # self.best_trajectory_publisher.publish(msg) + points = [p1, p2] + msg = PoseArray() + for P in points: + args = {"position": Point(x=P[0], y=P[1], z=0.0)} + msg.poses.append(Pose(**args)) + self.best_trajectory_publisher.publish(msg) # self.get_logger().info(f"steering angle published: {steering_angle}") From 9b70cf5e2f2d4457f7abee1494bc4234cd3d45cc Mon Sep 17 00:00:00 2001 From: Tanish Bhatt Date: Tue, 7 May 2024 13:00:02 +1200 Subject: [PATCH 6/9] bug fixed - ideal conemap used but sim not working --- src/moa/moa_bringup/launch/sim_launch.py | 10 ++--- .../shortest_path/PathHelpers.py | 10 +++++ .../Race lines/$track_name optimal.png | Bin 254596 -> 697576 bytes .../Track images/imported track.png | Bin 234709 -> 199682 bytes .../shortest_path/Track images/nodes.png | Bin 306570 -> 123 bytes .../shortest_path/TrackMethods.py | 29 ++++++++++--- .../path_planning/shortest_path/main.py | 22 +++++----- .../path_planning/trajectory_shortest_path.py | 39 ++++++++++++------ 8 files changed, 76 insertions(+), 34 deletions(-) diff --git a/src/moa/moa_bringup/launch/sim_launch.py b/src/moa/moa_bringup/launch/sim_launch.py index 8ef2727..d34163a 100644 --- a/src/moa/moa_bringup/launch/sim_launch.py +++ b/src/moa/moa_bringup/launch/sim_launch.py @@ -4,11 +4,11 @@ def generate_launch_description(): return launch.LaunchDescription([ # cone map - # launch_ros.actions.Node( - # package='cone_mapping', - # executable='dbscan', - # name='dbscan', - # ), + launch_ros.actions.Node( + package='cone_mapping', + executable='dbscan', + name='dbscan', + ), # path optimization launch_ros.actions.Node( diff --git a/src/planning/path_planning/path_planning/shortest_path/PathHelpers.py b/src/planning/path_planning/path_planning/shortest_path/PathHelpers.py index 4f3b0c3..2b0e800 100644 --- a/src/planning/path_planning/path_planning/shortest_path/PathHelpers.py +++ b/src/planning/path_planning/path_planning/shortest_path/PathHelpers.py @@ -35,3 +35,13 @@ def noughtTo60(nought_to_60): Converts nought to 60 to linear acceleration """ return 60/nought_to_60 * 0.44704 + +def getTraverseTime(distance_between_nodes, current_state, next_node_state): + dist = distance_between_nodes + c_vel = current_state._velocity # current state velocity + n_vel = next_node_state._velocity # next state velocity + n_cost = next_node_state._cost # next state cost + dist_bound = max(next_node_state._node._innerDistance, next_node_state._node._outerDistance) + + objective = ((2*dist)/(c_vel+n_vel)) + n_cost + np.sqrt(dist_bound) + return objective diff --git a/src/planning/path_planning/path_planning/shortest_path/Race lines/$track_name optimal.png b/src/planning/path_planning/path_planning/shortest_path/Race lines/$track_name optimal.png index 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Bin 123 -> 306569 bytes .../shortest_path/TrackMethods.py | 18 +++-- .../path_planning/shortest_path/main.py | 40 ++++++++--- .../path_planning/trajectory_shortest_path.py | 63 +++++++++++++----- 6 files changed, 90 insertions(+), 35 deletions(-) diff --git a/src/moa/moa_bringup/launch/sim_launch.py b/src/moa/moa_bringup/launch/sim_launch.py index d34163a..8d6fe79 100644 --- a/src/moa/moa_bringup/launch/sim_launch.py +++ b/src/moa/moa_bringup/launch/sim_launch.py @@ -19,8 +19,8 @@ def generate_launch_description(): # path viz launch_ros.actions.Node( - package='path_planning_visualization', - executable='visualize2', + package='path_planning', + executable='shortest_path_viz', name='path_viz', ), diff --git a/src/planning/path_planning/path_planning/shortest_path/Race lines/$track_name optimal.png b/src/planning/path_planning/path_planning/shortest_path/Race lines/$track_name optimal.png index 7767742c46f6ee9dd5755d581fbf284a90608566..499223f1c0c5a17ef5120dd25c7e837e939b6bf6 100644 GIT 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zB$*I3T@!%;4zw*u3FJNhWfPq|BKlsmR2;4zJ{bYvHZRbbTD+vA>mWzNT63WZo!dXNIP5~loYvSCnUL244t9w|+j#{&W@-#lkH80g@p8%m+(wCX zYiyyfvx~;v3-D`5xcW0%(8PR_Q419kps}|i%G^YK!i9_YR;=$-v?7Fqg<~9XDcagm zQ%=m?ByulDJhz~wMuG5W6ra?oA@eA-6ut-L=!zbY2M+yv0lnP^KxICNf)}WAyeDR+ zZzQIb($nfpus)`bPW2__!Imgx_22J=0MdW|iCz6S%lO}4D>0{P>c9W6FHkoB?>}F@ z^5tp#{RFEE&4g9o>$OM O?bu?m`OddT{`EgTci1BU delta 6 NcmeCYEmS?B8UPB{0{s90 diff --git a/src/planning/path_planning/path_planning/shortest_path/TrackMethods.py b/src/planning/path_planning/path_planning/shortest_path/TrackMethods.py index ddde497..312ea0d 100644 --- a/src/planning/path_planning/path_planning/shortest_path/TrackMethods.py +++ b/src/planning/path_planning/path_planning/shortest_path/TrackMethods.py @@ -371,7 +371,7 @@ def getSectionofTrack(df, brackets, d_start, d_end): # return start_node -def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.array, n_vel, CAR:dict, plot:bool): +def optimal_path(track_name:str, car_position, df:pd.DataFrame, start_node:Node, brackets:np.array, n_vel, CAR:dict, plot:bool): """keeps ALL state from each pair of node state combination""" attributes = ["mass", "μ", "α", "α_d", "max steer angle", "max velocity", "tire width", "wheelbase"] mass, μ, α, α_d, max_steer_angle, max_velocity, tire_width, wheelbase = [CAR[key] for key in attributes] @@ -390,8 +390,8 @@ def optimal_path(track_name:str, df:pd.DataFrame, start_node:Node, brackets:np.a start_node._stateList[0]._previousNode = Node(0,start_node._xy - 2*wheelbase*start_node._stateList[0]._entryVector, 0, 0) for i in range(len(brackets)-1,0,-1): - # tmp = rclpyNode("tmp") - # tmp.get_logger().info(f"Bracket: {i}\n") + tmp = rclpyNode("tmp") + tmp.get_logger().info(f"Bracket: {i}\n") print(f"Bracket: {i}\n") if i == 2: @@ -481,6 +481,13 @@ def process_current_node(current_node): TrackHelpers.Plot(False, df.inner, "inner boundary") # outer boundary TrackHelpers.Plot(False, df.outer, "outer boundary") + # actual car position + plt.plot(car_position[0], car_position[1], "or", label="actual car position") + # optimal path + im = TrackHelpers.plotOptimal(best_xy, best_velocities, "optimal race line") + plt.colorbar(im, label='velocity (m/s)') + plt.title("Optimal Trajectory Colored by Velocity") + # nodes for i,B in enumerate(brackets): all_nodes = B._nodeList @@ -490,12 +497,9 @@ def process_current_node(current_node): col = "black" TrackHelpers.Plot(True, all_nodes, "nodes", col) - # optimal path - im = TrackHelpers.plotOptimal(best_xy, best_velocities, "optimal race line") - plt.colorbar(im, label='velocity (m/s)') - plt.title("Optimal Trajectory Colored by Velocity") p.savefig(f"{os.path.dirname(__file__)}/Race lines/{track_name}.png", dpi=600) + plt.legend(["left boundary", "right boundary", "actual car position", "optimal race line"]) plt.show() return start_node, brackets, cost/60 diff --git a/src/planning/path_planning/path_planning/shortest_path/main.py b/src/planning/path_planning/path_planning/shortest_path/main.py index b061153..de8707c 100644 --- a/src/planning/path_planning/path_planning/shortest_path/main.py +++ b/src/planning/path_planning/path_planning/shortest_path/main.py @@ -1,18 +1,18 @@ # main file -def main(): - # imports - # import CoreModels - # import PathHelpers - from path_planning.shortest_path.CoreModels import Node, State - import path_planning.shortest_path.TrackMethods as TrackMethods - import path_planning.shortest_path.TrackHelpers as TrackHelpers - import path_planning.shortest_path.PathHelpers as PathHelpers +# imports +# import CoreModels +# import PathHelpers +from path_planning.shortest_path.CoreModels import Node, State +import path_planning.shortest_path.TrackMethods as TrackMethods +import path_planning.shortest_path.TrackHelpers as TrackHelpers +import path_planning.shortest_path.PathHelpers as PathHelpers - import numpy as np +import numpy as np +def main(): CAR = { "mass": 84.5, # kg - "μ": 0.6, # static friction coefficient - dimensionless + "μ": 0.3, # static friction coefficient - dimensionless "α": PathHelpers.noughtTo60(3.0), "α_d": 15.0, # max decel in m/s^2 "max steer angle": 25.0, # degrees @@ -43,6 +43,9 @@ def main(): print("COMPUTING OPTIMAL PATH") # mass = 50.0 # μ = 0.7 + # car_position = brackets[8]._nodeList[4]._xy + [0, -5] + # current_position, brackets = getStartingPosition(car_position, brackets) + # start_node = get_start_node(starting_point=current_position) start_node = brackets[0]._nodeList[4] print(-1/df.p_vector.tolist()[0]) start_node._stateList.append(State(start_node, -1/df.p_vector.tolist()[0], 0.0, np.Inf)) @@ -62,6 +65,23 @@ def main(): # start_node = TrackMethods.optimise_path(track_name, df, brackets, start_node, mass, μ) print("\nOPTIMAL PATH COMPUTED") +def getStartingPosition(car_position, brackets): + best_dist = np.Inf + best_bracket_idx = 0 + for i, B in enumerate(brackets): + dists = [TrackHelpers.getDistance(car_position, node._xy) for node in B._nodeList] + if min(dists) < best_dist: + best_dist = min(dists) + best_bracket_idx = i + starting_point = brackets[i]._nodeList[np.argmin(dists)]._xy + # delete brackets before the starting position + brackets = brackets[best_bracket_idx:] + + return starting_point, brackets + +def get_start_node(starting_point): + return Node(1, np.array(starting_point), None, None) + if __name__ == "__main__": main() \ No newline at end of file diff --git a/src/planning/path_planning/path_planning/trajectory_shortest_path.py b/src/planning/path_planning/path_planning/trajectory_shortest_path.py index e4674c4..301cd0c 100644 --- a/src/planning/path_planning/path_planning/trajectory_shortest_path.py +++ b/src/planning/path_planning/path_planning/trajectory_shortest_path.py @@ -9,6 +9,7 @@ # from shapely import LineString, MultiPoint # from shapely import Point as shapelyPoint import os +import matplotlib.pyplot as plt import rclpy from rclpy.node import Node as NODE @@ -36,6 +37,8 @@ def __init__(self): # attributes self._plot = self.get_parameter("plot").get_parameter_value().bool_value self._save_track = self.get_parameter("save_track").get_parameter_value().bool_value + self._sim = False + # car properties self._CAR = { "mass": 84.5, # kg @@ -49,26 +52,48 @@ def __init__(self): } # subscribers - self.create_subscription(ConeMap, "cone_map", self.set_boundaries, 10) + self.create_subscription(ConeMap, "cone_map", self.callback, 10) # publishers self.steering_angle = self.create_publisher(Float32, "/test/cmd_steering", 10) self.best_trajectory_publisher = self.create_publisher(PoseArray, "moa/selected_trajectory", 10) - def set_boundaries(self, msg:ConeMap): - innerboundary, outerboundary, car_position, oc, ic = self.get_boundaries(msg.cones) + def callback(self, msg:ConeMap): + innerboundary, outerboundary, car_position, w, oc, ic = self.get_boundaries(msg.cones) + self.get_logger().info(f"car orientation = {w}") # saving track if self._save_track: self.save_track(innerboundary, outerboundary) - # innerboundary.reverse() - # outerboundary.reverse() + + if self._sim: + # FOR SOME REASON THE CONE LIST IN THE SIM COMES OUT AS OPPOSITE WITH CONES FURTHEST AWAY AT FIRST INDEX + # DOEST HAPPEN WITH FAKE CONE DATA + innerboundary.reverse() + outerboundary.reverse() # smaller (incl. negative) x, y come before innerboundary = np.array(innerboundary) outerboundary = np.array(outerboundary) # self.get_logger().info(f"bound: {innerboundary}") + # you want pairs of cones for path planning to work - HAPPENDS IN SIM & FAKE CONE + if len(innerboundary) > len(outerboundary): + innerboundary = innerboundary[:len(outerboundary)] + elif len(outerboundary) > len(innerboundary): + outerboundary = outerboundary[:len(innerboundary)] + + if self._plot: + xl = [P[0] for P in innerboundary] + yl = [P[1] for P in innerboundary] + xr = [P[0] for P in outerboundary] + yr = [P[1] for P in outerboundary] + plt.plot(xl, yl, "ob", label="left boundary") + plt.plot(xr, yr, "og", label="right boundary") + plt.plot(car_position[0],car_position[1], "or", label="car position") + plt.legend() + plt.show() + # if ic > oc: innerboundary = innerboundary[:(oc-ic)] # if oc > ic: outerboundary = outerboundary[:(ic-oc)] @@ -86,25 +111,29 @@ def set_boundaries(self, msg:ConeMap): print("IMPORTING TRACK") # df = TrackMethods.importTrack(track_info=track_info, plot=self._plot) - df = self.create_track_dataframe(innerboundary, outerboundary) + df = self.create_track_dataframe(innerboundary, outerboundary) # both boundaries should be equal length # create brackets print("CREATING BRACKETS") - brackets = TrackMethods.getBrackets(df, 6, plot=self._plot) + brackets = TrackMethods.getBrackets(df, 5, plot=self._plot) # compute optimal path print("COMPUTING OPTIMAL PATH") - # car_position = np.array(car_position) + [0,10] + # car_position = np.array(car_position) + (np.random.rand(2)*2) + start_node = brackets[int(np.random.randint(0,5))]._nodeList[int(np.random.randint(0,6))] + car_position = start_node._xy + current_position, brackets = self.getStartingPosition(car_position, brackets) start_node = self.get_start_node(starting_point=current_position) # transformed car position - # start_node = brackets[0]._nodeList[4] - start_node._stateList.append(State(start_node, np.array([np.cos(np.pi/2),np.sin(0)]), 0.0, np.Inf)) + angle = np.pi / 2 + start_node._stateList.append(State(start_node, np.array([np.cos(angle), np.sin(angle)]), 0.0, np.Inf)) print("starting inner distance: ", start_node._innerDistance) print("starting outer distance: ", start_node._outerDistance) # start_node = TrackMethods.belman_ford_path(df, velocity_range, brackets, start_node, plot=self._plot) n_vel = 10 start_node, brackets, optimal_cost = TrackMethods.optimal_path( "$track_name optimal", + car_position, df, start_node, brackets, @@ -129,8 +158,8 @@ def set_boundaries(self, msg:ConeMap): # publish msgs # self.steering_angle.publish(Float32(data=steering_angle)) - - points = [p1, p2] + p3 = start_node._stateList[0]._nextState._nextState._xy + points = [p1, p2, p3] msg = PoseArray() for P in points: args = {"position": Point(x=P[0], y=P[1], z=0.0)} @@ -151,6 +180,7 @@ def getStartingPosition(self, car_position, brackets): best_bracket_idx = i starting_point = brackets[i]._nodeList[np.argmin(dists)]._xy # delete brackets before the starting position + # brackets = brackets[best_bracket_idx:best_bracket_idx+5] brackets = brackets[best_bracket_idx:] return starting_point, brackets @@ -167,6 +197,7 @@ def get_boundaries(self, cones): y = cones[i].pose.pose.position.y if i == 0: car_position = [x,y] + w = cones[i].pose.pose.orientation.w else: # blue - left if cones[i].colour == 0: @@ -176,7 +207,7 @@ def get_boundaries(self, cones): outerboundary.append([x,y]) oc += 1 - return innerboundary, outerboundary, car_position, oc, ic + return innerboundary, outerboundary, car_position, w, oc, ic def save_track(self, innerboundary, outerboundary): with open(f'/{os.path.dirname(__file__)}/bound_coods', 'w') as fh: @@ -200,10 +231,10 @@ def save_track(self, innerboundary, outerboundary): def create_track_dataframe(self, innerboundary:np.array, outerboundary:np.array): - up_to = min(len(innerboundary), len(outerboundary)) + # up_to = min(len(innerboundary), len(outerboundary)) return pd.DataFrame({ - "inner": list(innerboundary[:up_to]), - "outer": list(outerboundary[:up_to]), + "inner": list(innerboundary), + "outer": list(outerboundary), }) From f43065350ab626a7cbacca4d0440ff3d0494121b Mon Sep 17 00:00:00 2001 From: YachtChen Date: Mon, 20 May 2024 02:02:35 +1200 Subject: [PATCH 8/9] Acceleration Algorithm --- .../acceleration/acceleration/__init__.py | 0 .../acceleration/acceleration/controller.py | 75 +++++++++++++++++++ src/action/acceleration/package.xml | 18 +++++ src/action/acceleration/resource/acceleration | 0 src/action/acceleration/setup.cfg | 4 + src/action/acceleration/setup.py | 26 +++++++ .../acceleration/test/test_copyright.py | 25 +++++++ src/action/acceleration/test/test_flake8.py | 25 +++++++ src/action/acceleration/test/test_pep257.py | 23 ++++++ 9 files changed, 196 insertions(+) create mode 100644 src/action/acceleration/acceleration/__init__.py create mode 100644 src/action/acceleration/acceleration/controller.py create mode 100644 src/action/acceleration/package.xml create mode 100644 src/action/acceleration/resource/acceleration create mode 100644 src/action/acceleration/setup.cfg create mode 100644 src/action/acceleration/setup.py create mode 100644 src/action/acceleration/test/test_copyright.py create mode 100644 src/action/acceleration/test/test_flake8.py create mode 100644 src/action/acceleration/test/test_pep257.py diff --git a/src/action/acceleration/acceleration/__init__.py b/src/action/acceleration/acceleration/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/action/acceleration/acceleration/controller.py b/src/action/acceleration/acceleration/controller.py new file mode 100644 index 0000000..244c3fa --- /dev/null +++ b/src/action/acceleration/acceleration/controller.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python3 +# Python imports +import rclpy +from rclpy.node import Node +from moa_msgs.msg import ConeMap +from ackermann_msgs.msg import AckermannDrive, AckermannDriveStamped +from std_msgs.msg import Header + + +class Acceleration_algorithm(Node): + def __init__(self): + super().__init__("Acceleration") + self.get_logger().info("Acceleration Started") + + ## Current speed setting + self.current_speed = 20/3.6 #In m/s + + # subscribe to Cone detection result + self.create_subscription(ConeMap, "cone_detection", self.main_hearback, 5) + + self.drive_pub = self.create_publisher(AckermannDrive, "/drive", 5) + self.drive_vis_pub = self.create_publisher(AckermannDrive, "/drive_vis", 5) + self.cmd_vel_pub = self.create_publisher(AckermannDriveStamped, "cmd_vel", 5) + + #Start the car + self.publish_ackermann() + + + def main_hearback(self, msg: ConeMap): + #If there are no cones, make throttle to 0 + if len(msg.cones) == 0: + self.current_speed = 0.0 + self.publish_ackermann() + + + + def publish_ackermann(self): + + args1 = {"steering_angle": 0.0, + "steering_angle_velocity": 0.0, + "speed": float(self.current_speed), + "acceleration": 0.0, + "jerk": 0.0} + msg1 = AckermannDrive(**args1) + + msg_cmd_vel = self.convert_to_stamped(msg1) + self.cmd_vel_pub.publish(msg_cmd_vel) + self.drive_pub.publish(msg1) + self.drive_vis_pub.publish(msg1) + + def convert_to_stamped(self, ackermann_msgs): + stamped_msg = AckermannDriveStamped() + stamped_msg.header = Header() + stamped_msg.header.stamp = self.get_clock().now().to_msg() # Set the current timestamp + stamped_msg.header.frame_id = '0' # Set the appropriate frame ID + stamped_msg.drive = ackermann_msgs + return stamped_msg + + +def main(args=None): + rclpy.init(args=args) + + accelrator = Acceleration_algorithm() + + rclpy.spin(accelrator) + + # Destroy the node explicitly + # (optional - otherwise it will be done automatically + # when the garbage collector destroys the node object) + accelrator.destroy_node() + rclpy.shutdown() + + +if __name__ == '__main__': + main() diff --git a/src/action/acceleration/package.xml b/src/action/acceleration/package.xml new file mode 100644 index 0000000..27f0061 --- /dev/null +++ b/src/action/acceleration/package.xml @@ -0,0 +1,18 @@ + + + + acceleration + 0.0.0 + TODO: Package description + Yiyang Chen + TODO: License declaration + + ament_copyright + ament_flake8 + ament_pep257 + python3-pytest + + + ament_python + + diff --git a/src/action/acceleration/resource/acceleration b/src/action/acceleration/resource/acceleration new file mode 100644 index 0000000..e69de29 diff --git a/src/action/acceleration/setup.cfg b/src/action/acceleration/setup.cfg new file mode 100644 index 0000000..3427a7d --- /dev/null +++ b/src/action/acceleration/setup.cfg @@ -0,0 +1,4 @@ +[develop] +script_dir=$base/lib/acceleration +[install] +install_scripts=$base/lib/acceleration diff --git a/src/action/acceleration/setup.py b/src/action/acceleration/setup.py new file mode 100644 index 0000000..c33358f --- /dev/null +++ b/src/action/acceleration/setup.py @@ -0,0 +1,26 @@ +from setuptools import find_packages, setup + +package_name = 'acceleration' + +setup( + name=package_name, + version='0.0.0', + packages=find_packages(exclude=['test']), + data_files=[ + ('share/ament_index/resource_index/packages', + ['resource/' + package_name]), + ('share/' + package_name, ['package.xml']), + ], + install_requires=['setuptools'], + zip_safe=True, + maintainer='Yiyang Chen', + maintainer_email='yiyang030903@gmail.com', + description='TODO: Package description', + license='TODO: License declaration', + tests_require=['pytest'], + entry_points={ + 'console_scripts': [ + 'controller = acceleration.controller:main', + ], + }, +) diff --git a/src/action/acceleration/test/test_copyright.py b/src/action/acceleration/test/test_copyright.py new file mode 100644 index 0000000..97a3919 --- /dev/null +++ b/src/action/acceleration/test/test_copyright.py @@ -0,0 +1,25 @@ +# Copyright 2015 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_copyright.main import main +import pytest + + +# Remove the `skip` decorator once the source file(s) have a copyright header +@pytest.mark.skip(reason='No copyright header has been placed in the generated source file.') +@pytest.mark.copyright +@pytest.mark.linter +def test_copyright(): + rc = main(argv=['.', 'test']) + assert rc == 0, 'Found errors' diff --git a/src/action/acceleration/test/test_flake8.py b/src/action/acceleration/test/test_flake8.py new file mode 100644 index 0000000..27ee107 --- /dev/null +++ b/src/action/acceleration/test/test_flake8.py @@ -0,0 +1,25 @@ +# Copyright 2017 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_flake8.main import main_with_errors +import pytest + + +@pytest.mark.flake8 +@pytest.mark.linter +def test_flake8(): + rc, errors = main_with_errors(argv=[]) + assert rc == 0, \ + 'Found %d code style errors / warnings:\n' % len(errors) + \ + '\n'.join(errors) diff --git a/src/action/acceleration/test/test_pep257.py b/src/action/acceleration/test/test_pep257.py new file mode 100644 index 0000000..b234a38 --- /dev/null +++ b/src/action/acceleration/test/test_pep257.py @@ -0,0 +1,23 @@ +# Copyright 2015 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_pep257.main import main +import pytest + + +@pytest.mark.linter +@pytest.mark.pep257 +def test_pep257(): + rc = main(argv=['.', 'test']) + assert rc == 0, 'Found code style errors / warnings' From f6f819d2ea1dc84a3b17d00edb71a99760767676 Mon Sep 17 00:00:00 2001 From: YachtChen Date: Sat, 25 May 2024 16:56:12 +1200 Subject: [PATCH 9/9] Safer Acceleration --- src/action/acceleration/acceleration/controller.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/src/action/acceleration/acceleration/controller.py b/src/action/acceleration/acceleration/controller.py index 244c3fa..bf2d4f7 100644 --- a/src/action/acceleration/acceleration/controller.py +++ b/src/action/acceleration/acceleration/controller.py @@ -12,9 +12,6 @@ def __init__(self): super().__init__("Acceleration") self.get_logger().info("Acceleration Started") - ## Current speed setting - self.current_speed = 20/3.6 #In m/s - # subscribe to Cone detection result self.create_subscription(ConeMap, "cone_detection", self.main_hearback, 5) @@ -22,15 +19,16 @@ def __init__(self): self.drive_vis_pub = self.create_publisher(AckermannDrive, "/drive_vis", 5) self.cmd_vel_pub = self.create_publisher(AckermannDriveStamped, "cmd_vel", 5) - #Start the car - self.publish_ackermann() - def main_hearback(self, msg: ConeMap): - #If there are no cones, make throttle to 0 if len(msg.cones) == 0: + #No cones, make throttle to 0 self.current_speed = 0.0 self.publish_ackermann() + else: + #There is cones, adjust throttle to target speed + self.current_speed = 20/3.6 #In m/s + self.publish_ackermann()

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