New RPCS strategy lets mobile robots navigate cluttered obstacles safely
Partial info only? No problem: adaptive tracking avoids collisions dynamically.
Navigating a mobile robot through an obstacle-cluttered environment with only partial information is a classic challenge in robotics. A new paper from researchers Li Tan, Junlin Xiong, Yan Wang, and Wei Ren (arXiv:2605.14232) introduces a Reactive Planning based Control Strategy (RPCS) that directly addresses this problem. The method first generates a straight-line reference trajectory from the robot's initial position to the target. As the robot moves, it continuously senses its immediate surroundings—without requiring a full map—and a Reactive Planning Strategy (RPS) locally modifies the reference path to avoid newly detected obstacles. This local replanning ensures the robot never collides with unforeseen objects.
To maintain accurate motion along the sometimes-changed trajectory, the authors pair RPS with an Adaptive Tracking Control Strategy (ATCS). ATCS uses discretization techniques to break the continuous path into manageable segments, allowing the robot to correct its course in real time even as the reference shifts. The combined RPCS framework is validated through numerical simulations that show smooth, collision-free navigation in dense obstacle fields. The work is particularly relevant for warehouse robots, autonomous vacuum cleaners, or any system where full environmental knowledge is unavailable.
- RPCS combines a Reactive Planning Strategy (RPS) and Adaptive Tracking Control Strategy (ATCS) to handle partial environment information
- The approach modifies a reference trajectory locally based on real-time sensor data, supporting collision avoidance without a global map
- Numerical examples demonstrate that RPCS enables stable, collision-free navigation in obstacle-cluttered environments
Why It Matters
Makes mobile robot navigation practical in real-world scenarios where full maps are unavailable—saving time and improving safety.