A Passive Navigation Planning Algorithm for Collision-free Control of Mobile Robots
This work addresses collision-free control for mobile robots in dynamic environments, offering an incremental improvement by integrating existing control methods to reduce computational demands.
The authors tackled the problem of path planning and collision avoidance for mobile robots in complex environments by proposing a passive navigation planning algorithm that combines a fractal impedance controller with elastic bands and regions of finite time invariance, resulting in smooth trajectories validated in simulation with 11 agents and hardware experiments on a holonomic wheeled platform, showing robustness and low computational complexity enabling deployment on low-power micro-controllers.
Path planning and collision avoidance are challenging in complex and highly variable environments due to the limited horizon of events. In literature, there are multiple model- and learning-based approaches that require significant computational resources to be effectively deployed and they may have limited generality. We propose a planning algorithm based on a globally stable passive controller that can plan smooth trajectories using limited computational resources in challenging environmental conditions. The architecture combines the recently proposed fractal impedance controller with elastic bands and regions of finite time invariance. As the method is based on an impedance controller, it can also be used directly as a force/torque controller. We validated our method in simulation to analyse the ability of interactive navigation in challenging concave domains via the issuing of via-points, and its robustness to low bandwidth feedback. A swarm simulation using 11 agents validated the scalability of the proposed method. We have performed hardware experiments on a holonomic wheeled platform validating smoothness and robustness of interaction with dynamic agents (i.e., humans and robots). The computational complexity of the proposed local planner enables deployment with low-power micro-controllers lowering the energy consumption compared to other methods that rely upon numeric optimisation.