Obstacle avoidance of quadrotors in dynamic environments is still a very open problem. Current works commonly leverage traditional static maps to represent static obstacles and the detection and tracking of moving objects (DATMO) method to model dynamic obstacles separately. The detection module requires pre-training, and the dynamic obstacles can only be modeled with certain shapes, such as cylinders or ellipsoids. This work utilizes the dual-structure particle-based (DSP) dynamic occupancy map to represent the arbitrary-shaped static obstacles and dynamic obstacles simultaneously, and proposes an efficient risk-aware sampling-based local trajectory planner to realize safe flights in this map. The trajectory is planned by sampling motion primitives generated in the state space. Each motion primitive is divided into two phases: a short-term phase with a strict risk limitation and a relatively long-term phase designed to avoid high-risk regions. The risk is evaluated with the predicted particle-form future occupancy status, considering the time dimension. With an approach to split from and merge to an arbitrary global trajectory, the planner can also be used in the tasks with preplanned global trajectories. Comparison experiments show that the obstacle avoidance system composed of the DSP map and our planner performs the best in dynamic environments. In real-world tests, our quadrotor reaches a speed of 6 m/s with the motion capture system and 2.5 m/s with everything running on a low-price single-board computer.
翻译:目前的工作通常利用传统的静态地图来代表静态障碍和探测和跟踪移动物体(DATMO)的方法来分别模拟动态障碍。探测模块需要预先训练,动态障碍只能以某些形状来模拟,如气瓶或环球体。这项工作利用基于双结构粒子的动态占用图同时代表任意形状的静态障碍和动态障碍,并提议一个高效的有风险意识的基于取样的地方轨道规划仪来实现该地图的安全飞行。轨迹是通过对在状态空间产生的原始物体进行取样来规划的。每个原始运动分为两个阶段:一个短期阶段,有严格的风险限制,一个相对长期的阶段,目的是避免高风险区域。根据预测的粒子形式(DSP)未来占用状态来评估风险,同时考虑到时间层面。在规划全球轨迹时,规划员也可以在计划前的6轨迹中进行安全飞行。比较实验显示,我们最原始运动的动作是:一个短期阶段,具有严格的风险阶段,目的是避免高风险区域;根据预测的粒子形式未来占用状况来评估风险,同时考虑到时间层面。在规划师中,也可以在规划员/轨道上使用一个最佳的全球轨迹图中进行预规划。比较实验,在现实中进行一个动态周期中进行一个障碍测试。