This article addresses the obstacle avoidance problem for setpoint stabilization and path-following tasks in complex dynamic 2-D 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 soft 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的设定点镇定与路径跟踪控制任务，解决了超越传统孤立凸障碍物场景的避障问题。提出一种面向设定点镇定任务的组合运动规划与控制器，该方案将闭式运动规划技术的优良收敛特性与模型预测控制对系统约束的直观表达相结合。该方法在软约束条件下通过理论分析证明了避障与收敛的达成性，并进一步扩展至路径跟踪控制。通过采用非完整独轮型机器人的多种仿真场景，验证了所提控制律的有效性，并展示了相比标准带避障的路径跟踪模型预测控制方法在收敛性能上的提升。