论文标题
基于实时感测的快速避免障碍
Fast Obstacle Avoidance Based on Real-Time Sensing
论文作者
论文摘要
人类在导航和穿越动态和复杂的空间（例如拥挤的街道）方面非常出色。对于机器人进行同样的操作，至关重要的是，它们具有高度反应性的避免障碍物对部分和不良感应的稳健性。我们解决了基于稀疏和异步感知的避免障碍的问题。拟议的控制方案结合了计划者或人类操作员提供的高级输入命令，具有快速反应性障碍物。基于采样的传感器数据可以与避免实时碰撞的障碍的分析重建结合。我们可以确保当障碍之间存在可行的路径时不会卡住。通过从混乱的室内办公环境中对静态激光数据进行实验评估该算法。此外，它在洛桑中心的动态且复杂的室外环境中用于共享控制模式。在两种情况下，拟议的控制方案成功地避免了碰撞。在实验过程中，板载计算机上的控制器花了1毫秒来评估30000多个数据点。
Humans are remarkable at navigating and moving through dynamic and complex spaces, such as crowded streets. For robots to do the same, it is crucial that they are endowed with highly reactive obstacle avoidance robust to partial and poor sensing. We address the issue of enabling obstacle avoidance based on sparse and asynchronous perception. The proposed control scheme combines a high-level input command provided by either a planner or a human operator with fast reactive obstacle avoidance. The sampling-based sensor data can be combined with an analytical reconstruction of the obstacles for real-time collision avoidance. We can ensure that the agent does not get stuck when a feasible path exists between obstacles. The algorithm was evaluated experimentally on static laser data from cluttered, indoor office environments. Additionally, it was used in a shared control mode in a dynamic and complex outdoor environment in the center of Lausanne. The proposed control scheme successfully avoided collisions in both scenarios. During the experiments, the controller on the onboard computer took 1 millisecond to evaluate over 30000 data points.
