Multi-layer perceptron-particle swarm optimization: A lightweight optimization algorithm for the model predictive control local planner

The model predictive control trajectory planner is a popular and effective robot local motion planner. However, it is challenging to satisfy real-time requirements and implement them on embedded platforms due to their high complexity of solving and reliance on optimization solvers. This letter reports a lightweight and efficient two-stage solving algorithm for the model predictive control planner. Firstly, the general form of the model predictive control local planning problem was specified and simplified by the motion primitives. Then, a two-stage solving method of multi-layer perceptron pre-solving and particle swarm optimization re-optimizing is developed after splitting the cost function into two pieces. An multi-layer perceptron neural network was designed and trained offline to learn the solution of the model predictive control local planner without considering obstacles after selecting the inputs and outputs. Next, to accomplish obstacle avoidance, the particle swarm optimization algorithm re-optimizes the trajectory based on the outputs of the neural network. The experiment results demonstrate that the multi-layer perceptron-particle swarm optimization algorithm can quickly and accurately solve local planning problems, guiding robots to complete global paths with the same efficiency as expert solvers. The average solving time has been reduced by over 90%, enabling the robot to increase its control frequency or adopt higher-quality complex motion primitives. The multi-layer perceptron-particle swarm optimization algorithm can also be used for various robots and motion primitives, with a wide range of potential applications.