To improve the obstacle avoidance trajectory planning ability of mobile robotic arm in narrow channel and obstacle constraint situations,by combining Artificial Potential Field method(APF)and Deep Deterministic Policy Gradient algorithm(DDPG),an improved algorithm named APF-DDPG was proposed.The APF planning was de-signed for the robotic arm to get the approximate pose,and the research problem was represented as a Markov deci-sion process.The state space,action space and reward and punishment functions were designed,and the planning process was analyzed and processed in phases.A mechanism for guiding was designed to transition the various con-trol phases,which the obstacle avoidance phase of the training was dominated by DDPG,and the approximate pose dominated the goal planning phase to guide the DDPG for the training.Thus the strategy model for planning was ob-tained from the training.Finally,simulation experiments of fixed and random state scenarios were established and designed to verify the effectiveness of the proposed algorithm.The experimental results showed that APF-DDPG al-gorithm could be trained with higher convergence efficiency to obtain a policy model with more efficient control per-formance by comparing with the traditional DDPG algorithm.
关键词
移动机械臂/避障轨迹规划/人工势场法/深度确定性策略梯度/引导训练
Key words
mobile robotic arm/obstacle avoidance trajectory planning/artificial potential field/deep deterministic policy gradient/guided training
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