论文标题
Contactnet：无环机器人运动的在线多接触计划
ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion
论文作者
论文摘要
在腿部徽标中，在线轨迹优化技术通常取决于基于启发式的联系计划者，以便具有较低的计算时间并实现高重型频率。在这项工作中，我们提出了基于多输出回归神经网络的快速无环接触计划者的Contactnet。 ContactNet对离散的步进区域进行排名，即使在复杂的环境中，也可以快速选择最佳的可行解决方案。低计算时间（按1 ms的顺序）使联系人计划者与模型预测控制（MPC）方式同时执行联系人。我们证明了该方法在与四倍的机器人Solo12的不同复杂情况下模拟中的有效性。
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.
