Ground robots navigating in complex, dynamic environments must compute collision-free trajectories to avoid obstacles safely and efficiently. Nonconvex optimization is a popular method to compute a trajectory in real-time. However, these methods often converge to locally optimal solutions and frequently switch between different local minima, leading to inefficient and unsafe robot motion. In this work, We propose a novel topology-driven trajectory optimization strategy for dynamic environments that plans multiple distinct evasive trajectories to enhance the robot's behavior and efficiency. A global planner iteratively generates trajectories in distinct homotopy classes. These trajectories are then optimized by local planners working in parallel. While each planner shares the same navigation objectives, they are locally constrained to a specific homotopy class, meaning each local planner attempts a different evasive maneuver. The robot then executes the feasible trajectory with the lowest cost in a receding horizon manner. We demonstrate, on a mobile robot navigating among pedestrians, that our approach leads to faster and safer trajectories than existing planners.

翻译：地面机器人在复杂动态环境中导航时，必须计算无碰撞轨迹以安全高效地避开障碍物。非凸优化是一种实时计算轨迹的常用方法，然而这些方法常收敛于局部最优解，并频繁在不同局部极小值间切换，导致机器人运动低效且不安全。本文针对动态环境提出一种新颖的拓扑驱动轨迹优化策略，通过规划多条不同的避障轨迹来提升机器人行为表现与效率。全局规划器迭代生成不同同伦类中的轨迹，这些轨迹随后由并行工作的局部规划器进行优化。尽管每个规划器共享相同的导航目标，它们被局部约束于特定同伦类，意味着各局部规划器尝试不同的避障策略。随后，机器人以滚动时域方式执行成本最低的可行轨迹。我们通过在行人环境中导航的移动机器人平台验证，该方法相比现有规划器能获得更快且更安全的轨迹。