论文标题
基于快速激增的3D碰撞避免敏捷MAVS的轨迹产生
Trajectory Generation with Fast Lidar-based 3D Collision Avoidance for Agile MAVs
论文作者
论文摘要
在搜索和救援任务期间，经常使用微型航空车（MAV）进行勘探，检查和监视。在压力条件下，手动驾驶这些机器人会引起飞行员错误，并可能导致造成灾难性后果的崩溃。同样，在完全自主的飞行过程中，计划的高级轨迹可能是错误的，并将机器人引导到障碍物中。 在这项工作中，我们提出了一种方法来有效计算避免障碍的平稳，时间优势的轨迹MAV。我们的方法首先从开始到任意目标状态（包括位置，速度和加速度）计算轨迹。它尊重输入和状态约束，因此在动态上是可行的。之后，我们有效地检查了3D点云中碰撞的轨迹，并记录在板上的激光雷达。我们利用轨迹的分段多项式公式来分析计算轴对准边界框（AABB），以加快碰撞检查过程。如果发生冲突，我们将实时生成一组替代轨迹。替代轨迹使MAV处于安全状态，同时仍在追求最初的目标。随后，我们根据距离度量选择并执行最佳的无碰撞替代轨迹。 在模拟和实际消防练习中的评估显示了我们方法的能力。
Micro aerial vehicles (MAVs), are frequently used for exploration, examination, and surveillance during search and rescue missions. Manually piloting these robots under stressful conditions provokes pilot errors and can result in crashes with disastrous consequences. Also, during fully autonomous flight, planned high-level trajectories can be erroneous and steer the robot into obstacles. In this work, we propose an approach to efficiently compute smooth, time-optimal trajectories MAVs that avoid obstacles. Our method first computes a trajectory from the start to an arbitrary target state, including position, velocity, and acceleration. It respects input- and state-constraints and is thus dynamically feasible. Afterward, we efficiently check the trajectory for collisions in the 3D-point cloud, recorded with the onboard lidar. We exploit the piecewise polynomial formulation of our trajectories to analytically compute axis-aligned bounding boxes (AABB) to speed up the collision checking process. If collisions occur, we generate a set of alternative trajectories in real-time. Alternative trajectories bring the MAV in a safe state, while still pursuing the original goal. Subsequently, we choose and execute the best collision-free alternative trajectory based on a distance metric. The evaluation in simulation and during a real firefighting exercise shows the capability of our method.
