Chen Liuhongxu, Wang Qibiao, Deng Chao, Xie Bo, Tuo Xianguo, Jiang Gang
School of Computer Science and Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
School of Physics and Electronic Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
Sensors (Basel). 2024 Mar 23;24(7):2061. doi: 10.3390/s24072061.
Detecting transportation pipeline leakage points within chemical plants is difficult due to complex pathways, multi-dimensional survey points, and highly dynamic scenarios. However, hexapod robots' maneuverability and adaptability make it an ideal candidate for conducting surveys across different planes. The path-planning problem of hexapod robots in multi-dimensional environments is a significant challenge, especially when identifying suitable transition points and planning shorter paths to reach survey points while traversing multi-level environments. This study proposes a Particle Swarm Optimization (PSO)-guided Double Deep Q-Network (DDQN) approach, namely, the PSO-guided DDQN (PG-DDQN) algorithm, for solving this problem. The proposed algorithm incorporates the PSO algorithm to supplant the traditional random selection strategy, and the data obtained from this guided approach are subsequently employed to train the DDQN neural network. The multi-dimensional random environment is abstracted into localized maps comprising current and next level planes. Comparative experiments were performed with PG-DDQN, standard DQN, and standard DDQN to evaluate the algorithm's performance by using multiple randomly generated localized maps. After testing each iteration, each algorithm obtained the total reward values and completion times. The results demonstrate that PG-DDQN exhibited faster convergence under an equivalent iteration count. Compared with standard DQN and standard DDQN, reductions in path-planning time of at least 33.94% and 42.60%, respectively, were observed, significantly improving the robot's mobility. Finally, the PG-DDQN algorithm was integrated with sensors onto a hexapod robot, and validation was performed through Gazebo simulations and Experiment. The results show that controlling hexapod robots by applying PG-DDQN provides valuable insights for path planning to reach transportation pipeline leakage points within chemical plants.
由于化工厂内输送管道的路径复杂、测量点多维度且场景高度动态，检测输送管道泄漏点具有挑战性。然而，六足机器人的机动性和适应性使其成为在不同平面进行检测的理想选择。六足机器人在多维度环境中的路径规划问题是一项重大挑战，尤其是在识别合适的过渡点并规划更短路径以在穿越多层环境时到达测量点方面。本研究提出一种粒子群优化（PSO）引导的双深度Q网络（DDQN）方法，即PSO引导的DDQN（PG-DDQN）算法来解决此问题。所提出的算法纳入PSO算法以取代传统的随机选择策略，并且从这种引导方法获得的数据随后用于训练DDQN神经网络。将多维度随机环境抽象为包含当前和下一层平面的局部地图。使用多个随机生成的局部地图，对PG-DDQN、标准DQN和标准DDQN进行了对比实验，以评估算法的性能。在每次迭代测试后，各算法获得总奖励值和完成时间。结果表明，在等效迭代次数下，PG-DDQN收敛速度更快。与标准DQN和标准DDQN相比，路径规划时间分别至少减少了33.94%和42.60%，显著提高了机器人的机动性。最后，将PG-DDQN算法与传感器集成到六足机器人上，并通过Gazebo模拟和实验进行验证。结果表明，应用PG-DDQN控制六足机器人为化工厂内输送管道泄漏点的路径规划提供了有价值的见解。
