Att följa ideala trajektorier med kinodynamiskt begränsade robotar

Abstract

Autonomous mobile robots are becoming increasingly common in production, warehouses, and other automated environments. When several robots move in the same environment, their motions must be coordinated to avoid collisions. Planning collision free motions is a well known and difficult problem, and simplified motion models are often used to make the planning easier. However, these simplifications can make it difficult for real robots to follow the planned paths exactly, which can reintroduce the risk of collisions. This thesis investigates how well robots with more constrained, and therefore possibly more realistic, motion models can follow plans created using simplified motion assumptions. The robots are described using a kinematic bicycle model, where the motion is limited by velocity, acceleration, and steering angle. In contrast, the planning model assumes omnidirectional motion with unlimited acceleration. Four control strategies are compared: Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), LQR with Control Barrier Functions (LQR+CBF), and MPC with Control Barrier Functions (MPC+CBF). The results show that LQR works in simpler scenarios, but has difficulties with sharp turns and when several robots move close to each other. MPC provides better plan tracking and more stable motion. When CBF is used, the number of collisions is reduced, but not all collisions are eliminated in the tested scenarios. Overall, MPC+CBF gives the best balance between plan tracking and collision avoidance.