In real-world applications of mobile robots, collision avoidance is of critical importance. Typically, global motion planning in constrained environments is addressed through high-level control schemes. However, additionally integrating local collision avoidance into robot motion control offers significant advantages. For instance, it reduces the reliance on heuristics and conservatism that can arise from a two-stage approach separating local collision avoidance and control. Moreover, using model predictive control (MPC), a robot's full potential can be harnessed by considering jointly local collision avoidance, the robot's dynamics, and actuation constraints. In this context, the present paper focuses on obstacle avoidance for wheeled mobile robots, where both the robot's and obstacles' occupied volumes are modeled as ellipsoids. To this end, a computationally efficient overlap test, that works for arbitrary ellipsoids, is conducted and novelly integrated into the MPC framework. We propose a particularly efficient implementation tailored to robots moving in the plane. The functionality of the proposed obstacle-avoiding MPC is demonstrated for two exemplary types of kinematics by means of simulations. A hardware experiment using a real-world wheeled mobile robot shows transferability to reality and real-time applicability. The general computational approach to ellipsoidal obstacle avoidance can also be applied to other robotic systems and vehicles as well as three-dimensional scenarios.

翻译：在移动机器人的实际应用中，碰撞规避至关重要。通常，受限环境中的全局运动规划通过高层控制方案解决。然而，将局部碰撞规避额外集成到机器人运动控制中具有显著优势。例如，这降低了对启发式方法和保守性的依赖，这些依赖可能源于将局部碰撞规避与控制分离的两阶段方法。此外，通过使用模型预测控制（MPC），可以联合考虑局部碰撞规避、机器人动力学及驱动约束，从而充分发挥机器人的潜力。在此背景下，本文聚焦于轮式移动机器人的障碍物规避，其中机器人与障碍物的占用体积均建模为椭球体。为此，我们提出了一种适用于任意椭球体的计算高效的重叠检测方法，并将其创新性地集成到MPC框架中。我们针对平面移动机器人提出了一种特别高效的实现方案。通过仿真实验，在两种典型运动学模型上验证了所提出的避障MPC的功能。利用真实轮式移动机器人进行的硬件实验证明了该方法向现实场景的可迁移性及实时适用性。这种针对椭球障碍物规避的通用计算方法同样适用于其他机器人系统、车辆以及三维场景。