论文标题
使用非线性编程的随机抓地力的界限机器人的风险感知运动计划
Risk-Aware Motion Planning for a Limbed Robot with Stochastic Gripping Forces Using Nonlinear Programming
论文作者
论文摘要
我们提出了一种运动计划算法，并具有概率保证，可用于具有随机抓地力的肢体机器人。基于最坏情况不确定性的确定模型的规划师可能会保守且僵化，以考虑接触的随机行为，尤其是在安装抓手时。我们提出的规划师使机器人能够同时计划其姿势和接触力轨迹，同时考虑与抓紧力相关的风险。我们的计划者被认为是与机会限制的非线性编程问题，该问题允许机器人根据不同的风险范围产生各种动议。为了将抓地力建模为随机变量，我们采用高斯过程回归。我们在11.5千克六个限制的机器人上验证了我们提出的运动计划算法，以进行两壁攀爬。我们的结果表明，我们提出的计划者会产生各种轨迹（例如，避免在低风险约束下避免低摩擦地形，通过改变基于各种规格的风险概率，选择不稳定但更快的步态）。
We present a motion planning algorithm with probabilistic guarantees for limbed robots with stochastic gripping forces. Planners based on deterministic models with a worst-case uncertainty can be conservative and inflexible to consider the stochastic behavior of the contact, especially when a gripper is installed. Our proposed planner enables the robot to simultaneously plan its pose and contact force trajectories while considering the risk associated with the gripping forces. Our planner is formulated as a nonlinear programming problem with chance constraints, which allows the robot to generate a variety of motions based on different risk bounds. To model the gripping forces as random variables, we employ Gaussian Process regression. We validate our proposed motion planning algorithm on an 11.5 kg six-limbed robot for two-wall climbing. Our results show that our proposed planner generates various trajectories (e.g., avoiding low friction terrain under the low risk bound, choosing an unstable but faster gait under the high risk bound) by changing the probability of risk based on various specifications.
