Autonomously driving vehicles must be able to navigate in dynamic and unpredictable environments in a collision-free manner. So far, this has only been partially achieved in driverless cars and warehouse installations where marked structures such as roads, lanes, and traffic signs simplify the motion planning and collision avoidance problem. We are presenting a new control approach for car-like vehicles that is based on an unprecedentedly fast-paced A* implementation that allows the control cycle to run at a frequency of 30 Hz. This frequency enables us to place our A* algorithm as a low-level replanning controller that is well suited for navigation and collision avoidance in virtually any dynamic environment. Due to an efficient heuristic consisting of rotate-translate-rotate motions laid out along the shortest path to the target, our Short-Term Aborting A* (STAA*) converges fast and can be aborted early in order to guarantee a high and steady control rate. While our STAA* expands states along the shortest path, it takes care of collision checking with the environment including predicted states of moving obstacles, and returns the best solution found when the computation time runs out. Despite the bounded computation time, our STAA* does not get trapped in corners due to the following of the shortest path. In simulated and real-robot experiments, we demonstrate that our control approach eliminates collisions almost entirely and is superior to an improved version of the Dynamic Window Approach with predictive collision avoidance capabilities.
翻译:自动驾驶车辆必须能够在动态和不可预测的环境中以不发生碰撞的方式在动态和不可预测的环境中行驶,迄今为止,仅部分地在诸如道路、车道和交通标志等标志性结构简化了机动规划和避免碰撞问题的机动规划和避免碰撞问题的情况下,在无人驾驶的汽车和仓库设施中实现了这一点。我们正在对汽车类车辆提出一种新的控制办法,该办法以前所未有的快速速度A* 执行为基础,使控制周期能够以30赫兹的频率以30赫兹的频率运行。这一频率使我们能够将A* 算法定位为一个低水平的再规划控制器,这个低水平的重新规划控制器非常适合在任何动态环境中航行和避免碰撞。由于高效的超常化,包括沿目标最短的路铺开的旋转-变换罗调动议,我们短期的A* (STAA*) 短期中止A* (STA* ) 快速并可以提前中止使用,以保证高而稳定的控制率。虽然我们的STA* 沿着最短的道路扩展的状态扩展,但要注意与环境的碰撞检查,包括预测的移动障碍状态,并在任何动态时返回最佳的计算方法。