Stochastic Optimization for Autonomous Vehicles with Limited Control Authority
Stochastic Optimization for Autonomous Vehicles with Limited Control Authority
复制标题
控制权限有限的自动驾驶车辆的随机优化
DOI：
10.1109/iros.2018.8594020
复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
Satyandra K. Gupta
中科院分区：
文献类型：
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作者：
Dylan Jones;Geoffrey A. Hollinger;Michael J. Kuhlman;D. Sofge;Satyandra K. Gupta
在这项工作中，我们提出了一种随机梯度上升(SGA)算法用于多车辆信息收集，该算法考虑了外力对车辆控制权限的限制。通过使用新的动作空间表示而不是状态空间表示来表示车辆路径，我们消除了对车辆路径执行可行性计算的需要。我们的算法使用一种随机优化方案，通过采样当前最好的已知序列周围的扰动动作序列来估计状态空间信息函数相对于动作序列的梯度。此外，我们使用顺序贪婪分配来对多辆车进行计划。结果使用墨西哥湾(GOM)的海军海岸海洋模式(NCOM)显示。SGA显示，在贪婪的基线上，获得的信息量有所改善。此外，我们还与蒙特卡罗树搜索(MCTS)方法进行了比较，MCTS方法能够收集大量有竞争力的信息，但比我们的方法计算量更大。
In this work, we present a Stochastic Gradient Ascent (SGA) algorithm for multi-vehicle information gathering that accounts for limitations on a vehicle's control authority caused by external forces. By representing vehicle paths using a novel action space representation, rather than a state space representation, we remove the need to perform feasibility calculations on the vehicle's path. Our algorithm uses a stochastic optimization scheme by sampling perturbed action sequences around the current best known sequence to estimate the gradient of a state space information function with respect to the action sequence. Additionally, we use sequential greedy allocation to plan for multiple vehicles. Results are shown using a Navy Coastal Ocean Model (NCOM) for the Gulf of Mexico (GoM). SGA shows improvement in the amount of information gained over a greedy baseline. Additionally, we compare to Monte Carlo Tree Search (MCTS) Method, which is able to gather competitive amounts of information but is more computationally intensive than our approach.