Cao Meng-Li, Meng Qing-Hao, Wang Jia-Ying, Luo Bing, Jing Ya-Qi, Ma Shu-Gen
Institute of Robotics and Autonomous Systems, Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
Department of Robotics, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu-Shi 525-8577, Japan.
Sensors (Basel). 2015 Mar 27;15(4):7512-36. doi: 10.3390/s150407512.
Maintaining contact between the robot and plume is significant in chemical plume tracing (CPT). In the time immediately following the loss of chemical detection during the process of CPT, Track-Out activities bias the robot heading relative to the upwind direction, expecting to rapidly re-contact the plume. To determine the bias angle used in the Track-Out activity, we propose an online instance-based reinforcement learning method, namely virtual trail following (VTF). In VTF, action-value is generalized from recently stored instances of successful Track-Out activities. We also propose a collaborative VTF (cVTF) method, in which multiple robots store their own instances, and learn from the stored instances, in the same database. The proposed VTF and cVTF methods are compared with biased upwind surge (BUS) method, in which all Track-Out activities utilize an offline optimized universal bias angle, in an indoor environment with three different airflow fields. With respect to our experimental conditions, VTF and cVTF show stronger adaptability to different airflow environments than BUS, and furthermore, cVTF yields higher success rates and time-efficiencies than VTF.
在化学羽流追踪（CPT）中，保持机器人与羽流之间的接触至关重要。在CPT过程中化学探测丢失后的即刻，追踪外出活动会使机器人航向相对于逆风方向产生偏差，期望能迅速重新接触到羽流。为了确定追踪外出活动中使用的偏差角，我们提出了一种基于实例的在线强化学习方法，即虚拟轨迹跟踪（VTF）。在VTF中，动作值是从最近存储的成功追踪外出活动实例中归纳得出的。我们还提出了一种协作式VTF（cVTF）方法，其中多个机器人在同一个数据库中存储自己的实例，并从存储的实例中学习。在具有三种不同气流场的室内环境中，将所提出的VTF和cVTF方法与有偏差的逆风涌动（BUS）方法进行比较，在BUS方法中，所有追踪外出活动都使用离线优化的通用偏差角。就我们的实验条件而言，VTF和cVTF比BUS对不同气流环境表现出更强的适应性，此外，cVTF比VTF产生更高的成功率和时间效率。
