A Dual Forward-Backward Algorithm to Solve Convex Model Predictive Control for Obstacle Avoidance in a Logistics Scenario

In recent years, the logistics sector expanded significantly, leading to the birth of smart warehouses. In this context, a key role is represented by autonomous mobile robots, whose main challenge is to find collision-free paths in their working environment in real-time. Model Predictive Control Algorithms combined with global path planners, such as the A* algorithm, show great potential in providing efficient navigation for collision avoidance problems. This paper proposes a Dual Forward-Backward Algorithm to find the solution to a Model Predictive Control problem in which the task of driving a mobile robotic platform into a bi-dimensional semi-structured environment is formulated in a convex optimisation framework.

Automated summary

In recent years, with the expansion of the logistics sector, smart warehouses have emerged. Autonomous mobile robots play a key role in finding collision-free paths in real-time in their working environment. Model Predictive Control Algorithms combined with the A* algorithm show great potential in efficiently navigating collision avoidance problems. This paper proposes a Dual Forward-Backward Algorithm for solving a Model Predictive Control problem in a convex optimization framework, where the task is to drive a mobile robotic platform into a bi-dimensional semi-structured environment.