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. Our software is available on GitHub as an open-source package.

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规避移动障碍物。现有工作大多基于单一地图表示（如几何地图、占据栅格地图或ESDF地图）设计路径规划算法。尽管这些方法在静态环境中取得了成功，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。针对该问题，本文提出一种基于梯度优化的B样条轨迹算法，利用机器人机载视觉实现动态环境导航。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跟踪与表示。该优化首先采用基于圆形的引导点算法，近似计算规避静态障碍物的代价与梯度；随后结合视觉检测到的运动物体，通过滚动时域距离场同时防止动态碰撞；最后应用迭代重引导策略生成无碰撞轨迹。仿真与物理实验表明，该方法能实时运行并安全导航动态环境。相关开源软件已发布在GitHub上。