A Novel Navigation System for an Autonomous Mobile Robot in an Uncertain Environment
This work addresses navigation challenges for autonomous mobile robots, but it appears incremental as it builds on existing techniques like clustering and decision trees with a novel algorithm combination.
The paper tackles autonomous robot navigation in uncertain environments by developing a new system that combines adaptive threshold clustering, decision tree classification, and a simplified Mophin algorithm, resulting in effective collision avoidance with optimal paths, small memory size, and reduced computing complexity compared to state-of-the-art methods.
In this paper, we developed a new navigation system, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Mophin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The experiments on simulation and a robot for eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment.