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

This research focuses on modifying the D-& lowast; algorithm for path optimization of autonomousrobots moving on sidewalks. The existing D(& lowast; )algorithm is designed to make the autonomous robots recognizeand avoid obstacles. However, in real-world pedestrian settings, observations indicate that passersby onsidewalks tend to notice robots and avoid them themselves. By analyzing people's trajectory data collectedthrough lidar sensors, this study identified the average distance and angle of avoidance at which people startto avoid autonomous robots. Based on this, we proposed a modified D-& lowast; algorithm that allows the robot tomaintain the existing optimal path when people are willing to maneuver around while adopting an avoidancepath only when they are not. Experimental results showed that the autonomous robot using the modified D(& lowast;)algorithm outperformed the conventional method regarding driving efficiency and time. This research isexpected to contribute to optimizing autonomous robots' walking paths by enabling efficient driving evenunder limited battery capacity.