Path Planning of Tidal Flat Tracked Vehicle Based on CB-RRT* Algorithm

doi:10.12382/bgxb.2022.1089

Abstract

Abstract:

Cauchy Bessel rapidly-exploring random tree star (CB-RRT^*) algorithm is proposed for the path planning of tracked vehicle for long-term surveying operations in tidal flat environments. In order to plan a safe path, a tidal flat prediction model is constructed based on the global map. The tidal data and the distance from the tidal flat tracked vehicle to the demarcation area. Using the Cauchy probability density function to sample the key tree nodes of the initial path can reduce the sampling range and improve the utilization of nodes, which increases the speed of multiple path planning of tidal flat tracked vehicle moving to a target point. Considering the maximum corner constraint to set the corresponding coefficient in the process of reselecting the parent node and using the continuous quadratic Bezier curve to splicing to generate a path, the smoothness of the path can be improved and the safety problem caused by the excessive deviation between the smoothed path and the original path can be also solved. The simulated results show that CB-RRT^* algorithm can greatly improve the convergence of the algorithm and the smoothness of the path in the static and dynamic tidal flat environments, and ensures the optimal path length. The proposed method can ensure the long-term safe operation of tidal flat tracked vehicle in various tidal flat environments.

Key words: tidal flat tracked vehicle, path planning, Cauchy probability density function, maximum corner constraint, Bezier curve

CLC Number:

TP242

PAN Zuodong, ZHOU Yue, GUO Wei, XU Gaofei, SUN Yu. Path Planning of Tidal Flat Tracked Vehicle Based on CB-RRT^* Algorithm[J]. Acta Armamentarii, 2024, 45(4): 1117-1128.

Figures/Tables 22

Fig.1 Tidal data

Fig.2 Tidal flat prediction model

Fig.3 CB-RRT* algorithm flowchart

Fig.4 Cauchy probability density function sampling

Fig.5 Node angle correspondence

Fig.6 Setting the parent node of new node

Fig.7 Cost function value

Fig.8 Update parent node

Fig.9 Rewiring

Fig.10 Local path model

Fig.11 Bezier curve fitting path

Fig.12 Experimental results of maximum rotation angle parameter

Fig.13 Experimental results of dense obstacle environment

Table 1 Performance comparison of four algorithms in dense obstacle environment

算法	路径长度/m	时间/s	曲率
RRT^*	56.73	58.34	0.215
Informed RRT^*	55.23	55.02	0.127
Quick-RRT^*	55.16	59.80	0.139
CB-RRT^*	54.80	56.51	0.024

Fig.14 Experimental results in a single-channel environment

Table 2 Performance comparison of four algorithms in a unique channel environment

算法	路径长度/m	时间/s	曲率
RRT^*	59.13	39.38	0.149
Informed RRT^*	61.27	37.89	0.274
Quick-RRT^*	59.22	38.86	0.116
CB-RRT^*	58.23	33.76	0.037

Fig.15 Experimental results in U-shaped obstacle environment

Tab.3 Performance comparison of four algorithms in U-shaped obstacle environment

算法	路径长度/m	时间/s	曲率
RRT^*	53.63	76.48	0.230
Informed RRT^*	55.06	69.67	0.272
Quick-RRT^*	52.54	72.75	0.174
CB-RRT^*	52.10	71.32	0.057

Fig.16 Dynamic tidal flat environment

Table 4 Performance comparison of four algorithms in dynamic tidal flat environment

算法	路径规划序号	长度/m	时间/s	曲率
RRT^*	1	4229.61	96.71	0.105
	2	1541.34	59.60	0.114
Informed RRT^*	1	4474.95	90.36	0.179
	2	1736.04	64.28	0.137
Quick-RRT^*	1	4297.38	98.47	0.162
	2	1640.25	63.91	0.130
CB-RRT^*	1	4154.26	82.04	0.047
	2	1534.79	37.44	0.037

Fig.17 Path planning results of four algorithms in tidal flat environment

Fig.18 Result of tidal flat environment path planning

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