Explicit-risk-aware Path Planning with Reward Maximization
Explicit-risk-aware Path Planning with Reward Maximization
复制标题
具有奖励最大化的明确风险意识路径规划
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
R. Murphy
中科院分区：
文献类型：
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作者：
Xuesu Xiao;J. Dufek;R. Murphy
本文开发了一种路径规划器，它可以最小化风险(例如，运动执行)，同时最大化在非结构化或受限环境中由视觉辅助或跟踪场景激励的累积回报(例如，传感器视点的质量)。在这些情况下，机器人应该在移动到目标时保持最佳视点。然而，在非结构化或受限的环境中，一些路径可能会增加碰撞的风险；因此，在风险和回报之间存在权衡。传统的状态依赖风险或概率不确定性建模没有考虑路径级风险，或者难以获取。该风险回报规划器明确地将风险表示为运动计划的函数，即路径。无需人工分配风险对规划者造成的负面影响，规划者采用预先建立的视点质量地图，同时规划目标位置和通向它的路径，以便在最小化风险的同时最大化整个路径的总体回报。给出了精确算法和近似算法，并在物理系绳飞行器上进行了进一步的验证。除了视觉辅助问题，该框架还提供了一种新的规划范式，以解决动态风险和缺乏子结构最优情况下的最小风险规划，并平衡回报和风险之间的权衡。
This paper develops a path planner that minimizes risk (e.g. motion execution) while maximizing accumulated reward (e.g., quality of sensor viewpoint) motivated by visual assistance or tracking scenarios in unstructured or confined environments. In these scenarios, the robot should maintain the best viewpoint as it moves to the goal. However, in unstructured or confined environments, some paths may increase the risk of collision; therefore there is a tradeoff between risk and reward. Conventional state-dependent risk or probabilistic uncertainty modeling do not consider path-level risk or is difficult to acquire. This risk-reward planner explicitly represents risk as a function of motion plans, i.e., paths. Without manual assignment of the negative impact to the planner caused by risk, this planner takes in a pre-established viewpoint quality map and plans target location and path leading to it simultaneously, in order to maximize overall reward along the entire path while minimizing risk. Exact and approximate algorithms are presented, whose solution is further demonstrated on a physical tethered aerial vehicle. Other than the visual assistance problem, the proposed framework also provides a new planning paradigm to address minimum-risk planning under dynamical risk and absence of substructure optimality and to balance the trade-off between reward and risk.