To achieve optimal robot behavior in dynamic scenarios we need to consider complex dynamics in a predictive manner. In the vehicle dynamics community, it is well know that to achieve time-optimal driving on low surface, the vehicle should utilize drifting. Hence many authors have devised rules to split circuits and employ drifting on some segments. These rules are suboptimal and do not generalize to arbitrary circuit shapes (e.g., S-like curves). So, the question "When to go into which mode and how to drive in it?" remains unanswered. To choose the suitable mode (discrete decision), the algorithm needs information about the feasibility of the continuous motion in that mode. This makes it a class of Task and Motion Planning (TAMP) problems, which are known to be hard to solve optimally in real-time. In the AI planning community, search methods are commonly used. However, they cannot be directly applied to TAMP problems due to the continuous component. Here, we present a search-based method that effectively solves this problem and efficiently searches in a highly dimensional state space with nonlinear and unstable dynamics. The space of the possible trajectories is explored by sampling different combinations of motion primitives guided by the search. Our approach allows to use multiple locally approximated models to generate motion primitives (e.g., learned models of drifting) and effectively simplify the problem without losing accuracy. The algorithm performance is evaluated in simulated driving on a mixed-track with segments of different curvatures (right and left). Our code is available at https://git.io/JenvB
翻译:为了在动态情景中实现最佳机器人行为, 我们需要以预测的方式考虑复杂的动态。 在车辆动态界中, 众所周知, 要在低表面实现时间最优化的驾驶, 飞行器应该使用漂移。 因此, 许多作者已经设计了分解电路和在某些部分使用漂移的规则。 这些规则不优化, 并且不普遍适用于任意电路形状( 例如, S类曲线 ) 。 因此, “ 何时进入哪个模式以及如何在其中驱动? ” 的问题仍然没有得到解答。 要选择合适的模式( 分解决定), 算法需要关于该模式中连续运动的可行性的信息。 这使得它成为任务和运动规划( TAMMP) 的问题类别, 众所周知, 很难在实时中以最佳的方式解决。 在 AI 规划界中, 通常使用搜索方法。 但是, 由于连续模型, 无法直接应用于 TAMP 问题 。 在这里, 我们展示一种基于搜索的方法, 有效地解决这个问题, 并且有效地在高度的状态空间中搜索, 使用非线性和不稳定的流动的流动部分。 我们的流动的流动搜索空间, 以原始的流到可能的流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到的流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到流到