This article addresses the obstacle avoidance problem for setpoint stabilization and path-following tasks in complex dynamic 2D environments that go beyond conventional scenes with isolated convex obstacles. A combined motion planner and controller is proposed for setpoint stabilization that integrates the favorable convergence characteristics of closed-form motion planning techniques with the intuitive representation of system constraints through Model Predictive Control (MPC). The method is analytically proven to accomplish collision avoidance and convergence under certain conditions, and it is extended to path-following control. Various simulation scenarios using a non-holonomic unicycle robot are provided to showcase the efficacy of the control scheme and its improved convergence results compared to standard path-following MPC approaches with obstacle avoidance.

翻译：本文针对复杂动态二维环境中超出传统孤立凸障碍物场景的设定点镇定与路径跟踪任务的避障问题展开研究。提出了一种结合运动规划器与控制器的综合方案，通过设定点镇定将闭环运动规划技术的优良收敛特性与模型预测控制（MPC）对系统约束的直观表征相融合。该方法被解析证明在特定条件下能够实现避障与收敛，并进一步扩展至路径跟踪控制。基于非完整独轮车机器人的多种仿真场景展示了该控制方案的有效性，以及相较于带避障功能的标准路径跟踪MPC方法在收敛性能上的提升。