Reactive motion generation in dynamic and unstructured scenarios is typically subject to essentially static perception and system dynamics. Reliably modeling dynamic obstacles and optimizing collision-free trajectories under perceptive and control uncertainty are challenging. This article focuses on revealing tight connection between reactive planning and dynamic mapping for manipulators from a model-based perspective. To enable efficient particle-based perception with expressively dynamic property, we present a tensorized particle weight update scheme that explicitly maintains obstacle velocities and covariance meanwhile. Building upon this dynamic representation, we propose an obstacle-aware MPPI-based planning formulation that jointly propagates robot-obstacle dynamics, allowing future system motion to be predicted and evaluated under uncertainty. The model predictive method is shown to significantly improve safety and reactivity with dynamic surroundings. By applying our complete framework in simulated and noisy real-world environments, we demonstrate that explicit modeling of robot-obstacle dynamics consistently enhances performance over state-of-the-art MPPI-based perception-planning baselines avoiding multiple static and dynamic obstacles.

翻译：在动态和非结构化场景中，反应式运动生成通常受限于本质上静态的感知与系统动力学。在感知和控制不确定性的条件下，可靠地建模动态障碍物并优化无碰撞轨迹具有挑战性。本文从模型化视角出发，着重揭示机械臂反应式规划与动态建图之间的紧密联系。为实现具有显式动态特性的高效粒子感知，我们提出了一种张量化粒子权重更新方案，该方案同时显式地维护障碍物速度与协方差。基于此动态表征，我们提出了一种基于障碍物感知的MPPI规划框架，该框架联合传播机器人-障碍物动力学，使得未来系统运动能够在不确定性下被预测与评估。该模型预测方法被证明能显著提升动态环境中的安全性与反应能力。通过在仿真及含噪声的真实世界环境中应用我们的完整框架，我们证明：相较于当前最先进的基于MPPI的感知-规划基线方法，显式建模机器人-障碍物动力学能持续提升系统在规避多个静态与动态障碍物时的性能表现。