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:导航动态环境要求机器人生成无碰撞轨迹,并积极避免移动障碍。 大多数先前的工程设计路径规划算法都以一个地图代表法为基础,例如几何、占用或ESDF地图。 虽然这些方法在静态环境中表现出成功, 但由于地图代表法的限制, 这些方法无法同时可靠地处理静态和动态障碍。 为了解决这个问题, 本文建议使用机器人在机上的视觉, 使用基于梯度的B- spline轨迹优化算法。 深度视图使机器人能够根据 voxel 地图对动态物体进行几何跟踪和表示。 拟议的优化首先采用基于圆形的指南算法, 以近似成本和梯度, 以避免静态障碍。 然后, 有了视觉探测的移动对象, 我们的再后方位距离场被同时用于防止动态碰撞。 最后, 迭代再制导法被用于生成无碰撞轨迹。 模拟和物理实验证明, 我们的方法可以在实时运行, 以安全地导航动态环境。