自主搜索中的开发与探测双重控制（DCEE）
Dual Control for Exploitation and Exploration (DCEE) in Autonomous Search
摘要：
针对未知环境中未知位置目标的自主搜索问题，提出了一种最优的自主搜索框架，即探测与利用双重控制（DCEE）。来源定位是在部分未知的环境中找到大气有害物质释放的来源。本文提出了一种控制理论的方法来解决这个自主搜索问题。为了处理未知的目标位置，在每一步，目标位置估计贝叶斯推理。然后，采取控制动作以最小化未来机器人位置与目标位置的假设未来估计之间的误差。后者是通过在相应的未来机器人位置（由于控制动作）处的假设测量生成的，其中目标位置的当前估计作为先验。结果表明，该方法可以同时考虑机器人下一个位置与目标位置估计值之间的误差以及目标位置估计值的不确定性。该方法进一步扩展到不仅具有未知源位置，而且具有未知的局部环境（例如风速和风向）的情况。与现有的信息论方法不同，该方法通过驱动机器人向目标位置估计方向移动，同时降低目标位置估计的不确定性，实现了在未知环境中利用和探索目标的最优折衷。该方案是使用粒子滤波的移动机器人。仿真和实验研究表明，所提出的方法有前途的性能。讨论并比较了该方法、信息化路径规划、双重控制和经典模型预测控制之间的关系。
英文摘要：
This paper proposes an optimal autonomous search framework, namely Dual Control for Exploration and Exploitation (DCEE), for a target at unknown location in an unknown environment. Source localisation is to find sources of atmospheric hazardous material release in a partially unknown environment. This paper proposes a control theoretic approach to this autonomous search problem. To cope with an unknown target location, at each step, the target location is estimated by Bayesian inference. Then a control action is taken to minimise the error between future robot position and the hypothesised future estimation of the target location. The latter is generated by hypothesised measurements at the corresponding future robot positions (due to the control action) with the current estimation of the target location as a prior. It shows that this approach can take into account both the error between the next robot position and the estimate of the target location, and the uncertainty of the estimate. This approach is further extended to the case with not only an unknown source location, but also an unknown local environment (e.g. wind speed and direction). Different from current information theoretic approaches, this new control theoretic approach achieves the optimal trade-off between exploitation and exploration in a unknown environment with an unknown target by driving the robot moving towards estimated target location while reducing its estimation uncertainty. This scheme is implemented using particle filtering on a mobile robot. Simulation and experimental studies demonstrate promising performance of the proposed approach. The relationships between the proposed approach, informative path planning, dual control, and classic model predictive control are discussed and compared.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
