Navigating dynamic environments requires the robot to generate collision-free trajectories and actively avoid moving obstacles. Most previous works designed path planning algorithms based on one single map representation, such as the geometric, occupancy, or ESDF map. Although they have shown success in static environments, due to the limitation of map representation, those methods cannot reliably handle static and dynamic obstacles simultaneously. To address the problem, this paper proposes a gradient-based B-spline trajectory optimization algorithm utilizing the robot's onboard vision. The depth vision enables the robot to track and represent dynamic objects geometrically based on the voxel map. The proposed optimization first adopts the circle-based guide-point algorithm to approximate the costs and gradients for avoiding static obstacles. Then, with the vision-detected moving objects, our receding-horizon distance field is simultaneously used to prevent dynamic collisions. Finally, the iterative re-guide strategy is applied to generate the collision-free trajectory. The simulation and physical experiments prove that our method can run in real-time to navigate dynamic environments safely.

翻译：在动态环境中导航要求机器人生成无碰撞轨迹并主动避开移动障碍物。现有研究大多基于单一地图表示（如几何地图、占据网格地图或欧几里得符号距离场地图）设计路径规划算法。尽管这些方法在静态环境中表现优异，但由于地图表示的局限性，它们无法同时可靠地处理静态与动态障碍物。针对此问题，本文提出一种基于梯度优化的B样条轨迹算法，该算法利用机载视觉系统。深度视觉使机器人能够基于体素地图对动态物体进行几何追踪与表示。所提出的优化方法首先采用基于圆形的引导点算法近似静态障碍物的避碰代价与梯度；随后，结合视觉检测到的移动物体，同步使用递推视域距离场防止动态碰撞；最后，通过迭代重引导策略生成无碰撞轨迹。仿真与物理实验证明，该方法可实时运行并安全导航动态环境。