A Passive Navigation Planning Algorithm for Collision-free Control of Mobile Robots

TL;DR

This paper introduces a passive, stable navigation algorithm for mobile robots that plans smooth, collision-free trajectories efficiently, suitable for low-power devices, and validated through simulations and real-world experiments.

Contribution

It presents a novel passive control-based planning algorithm combining fractal impedance, elastic bands, and finite-time invariance, offering robustness and low computational demands.

Findings

Successfully navigates complex environments in simulation.

Demonstrates robustness with low bandwidth feedback.

Validates smooth and safe interaction in hardware experiments.

Abstract

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.