Learning-based Optimization Algorithms Combining Force Control Strategies for Peg-in-Hole Assembly

In this paper, an approach for automatic peg-in-hole assembly is proposed. The task is divided into two main steps: searching phase and inserting phase. First, a multilayer perceptron network is designed to address the hole search problem and a hybrid force position controller is introduced to ensure a safe and stable interaction with the external environment. Then, for the inserting phase, a variable impedance controller is adopted based on the fuzzy Q-learning algorithm to yield compliant behavior from the robot during the hole insertion process. This approach is a practical and general approach to solve complex peg-in-hole assembly problems by taking advantage of both learning-based algorithms and force control strategies, which can greatly improve the efficiency and safety of the industrial manufacturing process without identifying the unknown contact model and tuning tedious parameters. Finally, the peg-in-hole experimental results for an industrial robot verified the effectiveness and robustness of the proposed approach.