Potential Field Exploring Tree Path Planning for Intelligent Vehicle in Off-road Environment

doi:10.12382/bgxb.2023.0132

Abstract

Abstract:

Path planning is a key technology for intelligent vehicles. The traditional vehicle path planning method takes the shortest traveling distance or minimum traveling time as the optimization goal, ignoring the risk of vehicle motion. A potential field based rapidly-exploring random tree (RRT) algorithm is proposed. The potential field model is used to quantitatively evaluate the driving risk, and a low-risk initial vehicle driving trajectory is obtained efficiently using the RRT algorithm. And then a trajectory reconstruction optimization method is adopted to continuously optimize the vehicle driving trajectory based on the driving safety, traveling distance and turning angle. The scenario simulation is used to verify the performance of the planning solution. The simulated results show that the proposed algorithm can balance the path planning efficiency and safety performance, while avoiding the obstacles and environmental threats in off-road environment. The planned trajectories conform to the vehicle kinematics features, good safety and high traveling efficiency.

Key words: intelligent vehicle, off-road environment, potential field model, risk evaluation, rapidly-exploring random tree, path planning

CLC Number:

TJ810.2

TIAN Hongqing, MA Mingtao, ZHANG Bo, ZHENG Xunjia. Potential Field Exploring Tree Path Planning for Intelligent Vehicle in Off-road Environment[J]. Acta Armamentarii, 2024, 45(7): 2110-2127.

Figures/Tables 35

Fig.1 PFT* algorithm framework

Fig.2 Scenario potential field map

Fig.3 PFT* path planning algorithm flowchart

Fig.4 Comparison between PFT* and RRTs

Fig.5 Comparison of searching directions of intelligent sampling and general sampling methods

Fig.6 Intelligent sampling flowchart

Fig.7 Vehicle trajectory generation flowchart

Fig.8 Vehicle path risk evaluation

Fig.9 RRT reconstruction

Fig.10 RRT parent node reconstruction

Fig.11 Flowchart of RRT child node reconstruction

Table 1 Path planning simulation parameters

车辆参数	数值
外形尺寸(长×宽×高)/mm	4955×2137×1920
轴距/mm	3300
整车质量/kg	3200
行驶车速/(km·h^-1)	30

Fig.12 Simulation experiment scenarios

Fig.13 Scenario simulation experiment process

Fig.14 Comparison of simulation experimental trajectories (Scenario1)

Table 2 Comparisonof simulated results (Scenario 1)

算法	距离/m	势场值	最近障碍距离/m	通行代价
FMT^*	1999	161	2.17	2160
Q-RRT^*	1876	265	1.62	2141
A-PRM	2102	60	12.22	2162
A^*	1946	415	5.0	2361
PFT^*	2018	33	22.80	2051

Fig.15 Comparison of simulated trajectories comparison (Scenario 1)

Fig.16 Comparison of simulated trajectories (Scenarios 2)

Table 3 Comparison of simulated results (Scenario 2)

算法	距离/m	势场值	越野距离/m	通行代价
FMT^*	702	503	560	1 205
Q-RRT^*	683	498	565	1 181
A-PRM	1 160	98	60	1 258
A^*	1 457	267	0	1 724
PFT^*	908	240	308	1 148

Fig.17 Comparison of simulated trajectories (Scenario 2)

Fig.18 Comparison of simulated trajectories (Scenario 3)

Table 4 Comparison of simulated results (Scenario 3)

算法	距离/m	势场值	最近障碍距离/m	越野距离/m	通行代价
FMT^*	1417	971	2.95	235	2389
Q-RRT^*	1341	676	1.82	200	2017
A-PRM	1628	318	17.04	70	1946
A^*	1616	326	13.41	0	1942
PFT^*	1629	109	23.30	42	1738

Fig.19 Comparison of simulated trajectories (Scenario 3)

Fig.20 Comparison of simulated trajectories (Scenario 4)

Table 5 Comparison of simulated results (Scenario 4)

算法	距离/ m	势场值	最近障碍距离/m	越野距离/m	最近威胁距离/m	通行代价
FMT^*	1138	230	3.41	0	105	1368
Q-RRT^*	1046	158	1.76	0	150	1204
A-PRM	1147	326	1.94	98	139	1473
A^*	1102	126	13.44	0	123	1228
PFT^*	1128	41	18.21	0	110	1169

Fig.21 Comparison of simulated trajectories (Scenario 3)

Fig.22 Comparison of simulated trajectories (Scenario 5)

Table 6 Comparison of simulated results (Scenario 5)

算法	距离/m	势场值	最近障碍距离/m	越野距离/m	通行代价
FMT^*	1 302	668	3.30	80	1 970
Q-RRT^*	1 244	530	1.96	0	1 774
A-PRM	1 276	236	11.11	0	1 512
A^*	1 262	186	5.01	0	1 448
PFT^*	1 338	97	18.12	0	1 435

Fig.23 Comparison of simulated trajectories (Scenario 5)

Fig.24 Comparison of simulated trajectories (Scenario 6)

Table 7 Comparison of simulated results (Scenario 6)

算法	距离/m	势场值	最近障碍距离/m	越野距离/m	最近威胁距离/m	通行代价
FMT^*	868	514	4.89	185	49	1382
Q-RRT^*	838	438	9.89	125	50	1276
A-PRM	967	232	29.31	70	82	1199
A^*	915	490	13	0	0.45	1405
PFT^*	914	132	34.15	60	89	1046

Fig.25 Comparison of simulated trajectories(Scenario 6)

Fig.26 Trajectory comparison in elevation map (Scenario7)

Table 8 Planning data comparison in elevation map (Scenario 7)

算法	距离/m	势场值	最近障碍距离/m	通行代价
FMT^*	2 218	410	2.9	2 628
Q-RRT^*	2 188	144	3.3	2 332
A-PRM	2 359	180	16.1	2 539
A^*	2 403	144	13.4	2 547
PFT^*	2 274	0	44.2	2 274

Fig.27 Planning data comparison in elevation map(Scenario 7)

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