Human-Aware Trajectory Optimization for Enhancing D* Algorithm for Autonomous Robot Navigation

This research focuses on modifying the D $^{\ast }$ algorithm for path optimization of autonomous robots moving on sidewalks. The existing D $^{\ast }$ algorithm is designed to make the autonomous robots recognize and avoid obstacles. However, in real-world pedestrian settings, observations indicate that passersby on sidewalks tend to notice robots and avoid them themselves. By analyzing people’s trajectory data collected through lidar sensors, this study identified the average distance and angle of avoidance at which people start to avoid autonomous robots. Based on this, we proposed a modified D $^{\ast }$ algorithm that allows the robot to maintain the existing optimal path when people are willing to maneuver around while adopting an avoidance path only when they are not. Experimental results showed that the autonomous robot using the modified D $^{\ast }$ algorithm outperformed the conventional method regarding driving efficiency and time. This research is expected to contribute to optimizing autonomous robots’ walking paths by enabling efficient driving even under limited battery capacity.