Aiming at the problem that biped robots may easily lose their motion stability when walking on uneven ground, a value-based deep reinforcement learning algorithm called Deep Q-Network (DQN) gait control method was proposed, which is an intelligent learning method of posture adjustment. Firstly, an off-line gait for a flat ground environment was obtained through the gait planning of the robot. Secondly, instead of implementing a complex dynamic model compared to traditional control methods, a bipedal robot was regarded as an agent to establish robot environment space, state space, action space and Reward-Punishment (RP) mechanism. Finally, through multiple rounds of training, the biped robot learned to adjust its posture on the uneven ground and ensures the stability of walking. The performance and effectiveness of the proposed algorithm was validated in a V-Rep simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is improved obviously, which achieves the robot's posture adjustment behavior learning and proves the effectiveness of the method.

Mobile sink can efficiently collect data and extend the network lifetime. However, the existing researches about data collection based on mobile sink mainly focus on path-constrained mobile sink. Hence, a path-controlled traversal model for mobile sink data collection was constructed, and a data collection algorithm for mobile sink based on optimal-path traveling was proposed. The algorithm discretized the continuous path problem by local Voronoi grid, used the amount of data collected and system energy consumption as performance metric, combined taboo search algorithm to achieve the maximum amount of data collected and the minimum of network energy consumption traversing. Theoretically and experimentally, it is concluded that the proposed algorithm is able to solve the optimal-path traveling of data collection problem using path-controlled mobile sink.