自主空中源搜索中的并发主动学习：面向探索与利用的双重控制
Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation
AI总结：
提出用于未知环境自主源搜索的CL-DCEE并发学习双重控制框架，利用并行估计器量化不确定性并兼顾跟踪与探测，证明其收敛性且仿真显示性能更优、计算更省。
摘要：
本文针对配备机载传感器的自主平台在未知环境中执行源搜索任务，开发了一种并发学习框架。不同于现有解决方案需要大量计算能力进行贝叶斯估计和路径规划，所提方案在机载处理器上具有可承受的计算成本。论文提出了使用多个并行估计器进行并发学习的新概念，以学习运行环境并量化估计不确定性。搜索智能体被赋予双重能力：利用当前估计参数跟踪源，并探测环境以降低不确定性的影响，即基于并发学习的探索与利用双重控制（Concurrent Learning based Dual Control for Exploration and Exploitation，CL-DCEE）。在此设置下，控制动作不仅最小化智能体未来位置与估计源位置之间的跟踪误差，还最小化预测估计的不确定性。更重要的是，在关于噪声的温和假设下，建立了CL-DCEE算法收敛性等经过严格证明的性质，并考察了噪声对搜索性能的影响。仿真结果验证了所提CL-DCEE算法的有效性。与信息论方法相比，CL-DCEE不仅保证收敛，而且产生更好的搜索性能，并消耗少得多的计算时间。
英文摘要：
In this paper, a concurrent learning framework is developed for source search in an unknown environment using autonomous platforms equipped with onboard sensors. Distinct from the existing solutions that require significant computational power for Bayesian estimation and path planning, the proposed solution is computationally affordable for onboard processors. A new concept of concurrent learning using multiple parallel estimators is proposed to learn the operational environment and quantify estimation uncertainty. The search agent is empowered with dual capability of exploiting current estimated parameters to track the source and probing the environment to reduce the impacts of uncertainty, namely Concurrent Learning based Dual Control for Exploration and Exploitation (CL-DCEE). In this setting, the control action not only minimises the tracking error between future agent's position and estimated source location, but also the uncertainty of predicted estimation. More importantly, the rigorous proven properties such as the convergence of CL-DCEE algorithm are established under mild assumptions on noises, and the impact of noises on the search performance is examined. Simulation results are provided to validate the effectiveness of the proposed CL-DCEE algorithm. Compared with the information-theoretic approach, CL-DCEE not only guarantees convergence, but produces better search performance and consumes much less computational time.
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。
