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.

翻译：在腿部运动领域，在线轨迹优化技术通常依赖基于启发式的接触规划器，以实现低计算时间和高重规划频率。本文提出ContactNet——一种基于多输出回归神经网络的高效非周期接触规划器。ContactNet对离散化的步域进行排序，即使在复杂环境中也能快速选择最优可行解。其毫秒级（约1毫秒）的低计算时间使其能够以模型预测控制（MPC）方式与轨迹优化器并行运行。我们通过四足机器人Solo12在多种复杂场景下的仿真实验验证了该方法的有效性。