Energy efficient path planning for autonomous ground vehicles with ackermann steering

阿克曼函数 运动规划 计算机科学 会合 能源消耗 能量(信号处理) 规划师 无人地面车辆 弹道 路径(计算) 模拟 机器人 实时计算 航天器 人工智能 工程类 航空航天工程 数学 电气工程 反向 程序设计语言 物理 天文 统计 几何学
作者
Haojie Zhang,Yudong Zhang,Chuankai Liu,Zuoyu Zhang
出处
期刊:Robotics and Autonomous Systems [Elsevier BV]
卷期号:162: 104366-104366 被引量:18
标识
DOI:10.1016/j.robot.2023.104366
摘要

The autonomous ground vehicles have attracted a great deal of attention as viable solutions to a wide variety of military and civilian applications. However, the energy consumption plays a major role in the navigation of autonomous ground vehicles in challenging environments, especially if they are left to operate unattended under limited on-board power, such as planetary exploration, border patrol, etc. The autonomous ground vehicles are expected to perform more tasks more efficiently with limited power in these scenarios. Although plenty of research has developed an effective methodology for generating dynamically feasible and energy efficient trajectories for skid steering or differential steering vehicles, few studies on path planning for ackermann steering autonomous ground vehicles are available. In this study, an energy efficient path planning method with guarantee on completeness is proposed for autonomous ground vehicle with ackermann steering which is based on A∗ search algorithm. Firstly, the energy cost model is established for the autonomous ground vehicle using its kinematic constraints. Then, given the start and goal states, the energy-aware motion primitives are generated offline using the energy cost model to calculate the cost of each primary trajectory. Lastly, the energy efficient path planner is proposed and the analysis for completeness properties is given. The effectiveness of the proposed energy efficient path planner is verified by simulation over 150 randomly generated maps and real vehicle tests. The results show that a small increase in the distance of a path over the distance optimal path can result in a reduction of energy cost by nearly 26.9% in simulation and 21.09% in real test scenario for autonomous ground vehicles with ackermann steering.
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