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arXiv · 2209.15566

ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion

Angelo Bratta·Avadesh Meduri·Michele Focchi·Ludovic Righetti·Claudio Semini
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Abstract

In legged logomotion, online trajectory optimization techniques generally depend on heuristic-based contact planners in order to have low computation times and achieve high replanning frequencies. In this work, we propose ContactNet, a fast acyclic contact planner based on a multi-output regression neural network. ContactNet ranks discretized stepping regions, allowing to quickly choose the best feasible solution, even in complex environments. The low computation time, in the order of 1 ms, makes possible the execution of the contact planner concurrently with a trajectory optimizer in a Model Predictive Control (MPC) fashion. We demonstrate the effectiveness of the approach in simulation in different complex scenarios with the quadruped robot Solo12.

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Angelo Bratta, Avadesh Meduri, Michele Focchi, Ludovic Righetti, Claudio Semini. 2022-09-30. ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion. https://doi.org/10.1109/ur61395.2024.10597477

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