The six-degree-of-freedom robotic arm optimization iterative learning algorithm improves trajectory planning

Among the many fields covered by artificial intelligence (AI), path planning is undoubtedly one of the problems involving a wide range of research areas. Being able to find an optimal solution that allows the robot to establish a safe and effective way to get from the initial state to the final state is a challenge for continuing research today. The increasingly widespread use of robots has made path planning an important aspect of integrating these systems into countless applications. Therefore, taking a 6-DOF manipulator as an example, the mathematical model of working motion of the 6-DOF manipulator was established according to the optimization iterative learning algorithm, and the forward and inverse kinematics were analyzed. In path PLANNING, THE FAST SCALING BASED RANDOM TREE and bidirectional scaling BALANCED connected biTREE path planning method are combined to realize the high-dimensional and complex constraints of non-conflict path planning. The simulation results are in good agreement with the calculation results, which verifies that the structure of the model is consistent with the calculation method, and provides a useful reference for the design of similar manipulator.