Multi-sensor fusion-based mapping and path planning system for autonomous navigation of a unicycle-type mobile robot
DOI:
https://doi.org/10.37636/recit.v9n3e465Keywords:
Path planning, WMR, A*, Ray casting, Occupancy mapsAbstract
This paper presents a mapping and path planning system for a unicycle-type wheeled mobile robot (WMR), implemented on the AmigoBot platform, aimed at enabling autonomous navigation from a starting position to a defined destination within a structured environment. The system comprises three main components: occupancy map generation and visualization, trajectory planning, and motion control with obstacle avoidance. Implementation was carried out using the Robot Operating System (ROS) and the Advanced Robotics Interface for Applications (ARIA) architecture. The environmental representation is constructed by fusing data from an ultrasonic sensor array and a Kinect depth camera. Data fusion is achieved through the intersection of binary maps, complemented by neighborhood-based spatial filtering to smooth out local inconsistencies in the occupancy grid. Path planning relies on the A* algorithm, followed by a geometric simplification stage using ray casting to reduce the number of nodes in the trajectory. Path following is implemented via a proportional controller based on pose error (position and orientation), incorporating a reactive obstacle avoidance scheme based on repulsive potential fields. Experimental results demonstrate that the system achieves a 92% reduction in the number of nodes from the original path and a 62.5% reduction in inflection points, thanks to the geometric simplification process. In real-time autonomous navigation tests, the robot successfully completed the trajectory in 1 minute and 32 seconds, maintaining the tracking error relative to control points below 0.25 m during transitions. In the presence of additional unmapped obstacles, the reactive module ensured a 100% success rate in collision avoidance, recording a minimum safety distance of 3.6 cm in the environment's most spatially constrained areas. These results demonstrate that the proposed software architecture is capable of generating consistent occupancy maps, planning efficient trajectories with low geometric complexity, and executing safe autonomous navigation within the experimental environment.
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