A Motion Planner for Unmanned Tracked Vehicles in Multi-storey Buildings Considering the Characteristics of Vehicles

doi:10.12382/bgxb.2021.0800

Abstract

Abstract:

To solve the navigation problem of the unmanned vehicles in multi-storey buildings, a motion planning framework considering the characteristics of vehicles is proposed. Based on the characteristics of tracked vehicles, the primitive scheme of zero-radius steering is adopted and the Voronoi Path is introduced, which improves the flexibility and safety of the global planner in a narrow environment. Then, the smooth global path is obtained through piecewise cubic Hermite interpolation. Based on model prediction with respecting to the kinematic model of tracked vehicles, the Wavefront Value is introduced to improve the robustness of the local planning algorithm in the case of inaccurate obstacle positioning, and combined with the Finite State Machine to implement the autonomous navigation task between multiple floors. Finally, the simulation and real vehicle experiment are performed. The results show that the proposed algorithm can better adapt to the characteristics of narrow indoor space and also prove its feasibility in the actual environment.

Key words: unmanned ground vehicles, multi-storey buildings, motion plan, autonomous navigation

XIONG Guangming, YU Quanfu, HU Xiuzhong, ZHOU Zijie, XU Jiahui. A Motion Planner for Unmanned Tracked Vehicles in Multi-storey Buildings Considering the Characteristics of Vehicles[J]. Acta Armamentarii, 2023, 44(3): 841-850.

Figures/Tables 21

Fig. 1 Schematic diagram of Finite State Machine decision model

Fig. 2 Schematic diagram of motion planning framework

Fig. 3 Schematic diagram of the multi-storey building hierarchical map

Fig. 4 Grid map of an indoor environment and its Voronoi map

Fig. 5 Schematic diagram of traditional motion primitives

Fig. 6 Schematic diagram of zero-radius motion primitives

Fig. 7 Schematic diagram of path smoothing

Fig. 8 Schematic diagram of the kinematic geometric relationship of the tracked unmanned platform

Fig. 9 Schematic diagram of trajectory prediction

Fig. 10 Schematic diagram of Wavefront Value avoiding local minimum

Fig. 11 Schematic diagram of the simulation environment

Fig. 12 Comparison of the global planner’s simulation results

Table 1 Simulation results of the global planner

算法	扩展基元数量/个	路径长度/m	搜索耗时/ms
传统基元A*	452	18.0	36.8
本文算法	466	12.4	39.2

Fig. 13 Comparison of the local planner’s simulation results (left picture shows encountering obstacles, right picture shows bypassing obstacles)

Fig. 14 Appearance of the unmanned platform in the experiment

Fig. 15 Starting point and target point in the experiment

Fig. 16 Experimental results of the first floor (the left is the planned path, and the right is the real scene)

Fig. 17 Experimental results of the stair area

Fig. 18 Experimental results of the platform area

Fig.19 Experimental results on the third floor (the left is the planned path, right is the real scene)

Table 2 Results of the experiment

阶段	路径长度/m	实际运动时间/s	平均速度/(m·s^-1)	最大速度/(m·s^-1)
第1层区域	15.2	77	0.20	0.4
楼梯区域	40.6	463	0.10	0.2
第3层区域	23.7	152	0.16	0.4

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