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Abstract / Description of output
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.
|Title of host publication||2021 IEEE International Conference on Robotics and Automation (ICRA)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||7|
|Publication status||Published - 18 Oct 2021|
|Event||2021 IEEE International Conference on Robotics and Automation - Xi'an, China|
Duration: 30 May 2021 → 5 Jun 2021
|Conference||2021 IEEE International Conference on Robotics and Automation|
|Abbreviated title||ICRA 2021|
|Period||30/05/21 → 5/06/21|
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