Trend-aware motion planning for wheeled mobile robots operating in dynamic environments. (24th August 2020)

• Record Type: Journal Article
• Authors: Liu, Sheng; Dai, Fengji; Zhang, Shaobo; Wang, Yangqing; Wang, Zhenhua
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 4(2020:Jul./Aug.)
• Publication Date: 2020-08-24
• Subjects: Local motion planning -- dynamic obstacle avoidance -- collision-free trajectory generation -- trajectory optimization -- wheeled mobile robots; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420925292
• Languages: English
• ISSNs: 1729-8806

Abstract

Planning collision-free trajectories is essential for wheeled mobile robots operating in dynamic environments safely and efficiently. Most current trajectory generation methods focus on achieving optimal trajectories in static maps and considering dynamic obstacles as static depending on the precise motion estimation of the obstacles. However, in realistic applications, dealing with dynamic obstacles that have low reliable motion estimation is a common situation. Furthermore, inaccurate motion estimation leads to poor quality of motion prediction. To generate safe and smooth trajectories in such a dynamic environment, we propose a motion planning algorithm called trend-aware motion planning (TAMP) for dynamic obstacle avoidance, which combines with timed-elastic band . Instead of considering dynamic obstacles as static, our planning approach predicts the moving trends of the obstacles based on the given estimation. Subsequently, the approach generates a trajectory away from dynamic obstacles, meanwhile, avoiding the moving trends of the obstacles. To cope with multiple constraints, an optimization approach is adopted to refine the generated trajectory and minimize the cost. A comparison of our approach against other state-of-the-art methods is conducted. Results show that trajectories generated by TAMP are robust to handle the poor quality of obstacles' motion prediction and have better efficiency and performance.