Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field
Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field
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DOI：
10.1109/tro.2017.2705034
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发表时间：
2017-06
影响因子：
7.8
通讯作者：
Nick Malone;H. Chiang;Kendra Lesser;Meeko Oishi;Lydia Tapia
中科院分区：
文献类型：
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作者：
Nick Malone;H. Chiang;Kendra Lesser;Meeko Oishi;Lydia Tapia
自主机器人在不确定和动态环境中的主要挑战之一是规划和执行无碰撞路径。混合动态障碍物带来了更大的挑战，因为障碍物可以在没有警告的情况下改变动态，并可能使路径失效。基于人工势场（APF）的路径规划技术由于其运行时成本低，在高动态环境下的路径规划中显示出巨大的前景。我们利用APF框架进行运行时规划，但利用正式的验证方法，随机可达（SR）集，为移动障碍物生成准确的势场。少量的SR集被先验地计算出来，然后用来生成一个表示障碍物随机运动的势场，用于在线路径规划。与其他传统的高斯APF方法相比，我们的方法新颖，并且可以很好地随障碍物数量的变化而扩展，保持相对较高的达到目标而不发生碰撞的概率。在这里，我们用多达900个混合动态障碍物演示了我们的方法，并表明它在完整情况下优于传统的高斯APF方法高达60%，在独轮车情况下优于传统的高斯APF方法高达20%。
One of the primary challenges for autonomous robotics in uncertain and dynamic environments is planning and executing a collision-free path. Hybrid dynamic obstacles present an even greater challenge as the obstacles can change dynamics without warning and potentially invalidate paths. Artificial potential field (APF)-based techniques have shown great promise in successful path planning in highly dynamic environments due to their low cost at runtime. We utilize the APF framework for runtime planning but leverage a formal validation method, Stochastic Reachable (SR) sets, to generate accurate potential fields for moving obstacles. A small number of SR sets are computed a priori, then used to generate a potential field that represents the obstacle's stochastic motion for online path planning. Our method is novel and scales well with the number of obstacles, maintaining a relatively high probability of reaching the goal without collision, as compared to other traditional Gaussian APF methods. Here, we demonstrate our method with up to 900 hybrid dynamic obstacles and show that it outperforms the traditional Gaussian APF method by up to 60% in the holonomic case and up to 20% in the unicycle case.