Sun Xuehao, Deng Shuchao, Tong Baohong, Wang Shuang, Zhang Chenyang, Jiang Yuxiang
School of Mechanical Engineering, Anhui University of Technology, Ma'anshan 243032, China.
School of Mechanical Engineering, Anhui University of Technology, Ma'anshan 243032, China; Anhui Province Key Laboratory of Special Heavy Load Robot, Ma'anshan 243032, China.
ISA Trans. 2023 Mar;134:1-15. doi: 10.1016/j.isatra.2022.09.005. Epub 2022 Sep 6.
Achieving efficient and safe autonomous exploration in unknown environments is an urgent challenge to be overcome in the field of robotics. Existing exploration methods based on random and greedy strategies cannot ensure that the robot moves to the unknown area as much as possible, and the exploration efficiency is not high. In addition, because the robot is located in an unknown environment, the robot cannot obtain enough information to process the surrounding environment and cannot guarantee absolute safety. To improve the efficiency and safety of exploring unknown environments, we propose an autonomous exploration motion planning framework that is divided into the exploration and obstacle avoidance levels. The two levels are independent and interconnected. The exploration level finds the optimal frontier target point in the global scope based on the forward filtering angle and cost function, attracting the robot to move to the unknown area as much as possible, and improving the exploration efficiency; the obstacle avoidance level establishes a scenario-speed conversion mechanism, and the target point and obstacle information are weighed to realise dynamic motion planning and completes obstacle avoidance control, and ensures the safety of exploration. Experiments in different simulation scenarios and real environments verify the superiority of the method. Results show that our method is superior to the existing methods.
在未知环境中实现高效且安全的自主探索是机器人领域亟待克服的挑战。现有的基于随机和贪婪策略的探索方法无法确保机器人尽可能多地移动到未知区域，且探索效率不高。此外，由于机器人位于未知环境中，它无法获取足够的信息来处理周围环境，也无法保证绝对安全。为提高未知环境探索的效率和安全性，我们提出了一种自主探索运动规划框架，该框架分为探索和避障两个层次。这两个层次相互独立又相互关联。探索层基于前向滤波角度和代价函数在全局范围内找到最优前沿目标点，吸引机器人尽可能多地移动到未知区域，提高探索效率；避障层建立场景 - 速度转换机制，权衡目标点和障碍物信息以实现动态运动规划并完成避障控制，确保探索安全。在不同模拟场景和真实环境中的实验验证了该方法的优越性。结果表明，我们的方法优于现有方法。
