  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.
