MPC based Caster Wheel Aware Motion Planning for Differential Drive Robots

Abstract: The inherited rotation in a caster wheel allows movement in any direction, but pays at the expense of reaction torques. When implemented in a mobile robot, these forces have a negative impact in its performance. One approach is to restrict rotations on the spot by attaching a filter to the output of the motion planner. However, this formulation compromises the navigation’s completion in critical scenarios, such as parking, taking curves in narrow corridors or navigating at the presence of a high density of obstacles. Therefore, in this thesis we consider the influence of caster wheels in the motion planning stage, commonly presented as local planning. This work proposes a Model Predictive Control (MPC) based local planner that integrates the caster wheel physics into the motion planning stage. A caster wheel aware term is combined with a reference tracking based navigation, which leads to the formulation of the Caster Wheel Aware Local Planner (CWAWLP). Since this method requires knowing the caster wheel’s state and there is no sensor that provides this information, a caster wheel state observer is also formulated. In order to evaluate the impact of the caster wheel aware term, CWAWLP is compared to a Caster Wheel based Agnostic Local Planner (CWAGLP) and a Caster Wheel based Agnostic Planner Local Planner with Path Filter (CWPFLP). After running simulations for three case studies in a virtual framework, two experimental case studies are conducted in an intra-logistics robot. These are evaluated according to the navigation’s quality, motor torque usage and energy consumption. According to the patterns observed in the evaluation, CWAWLP covers a longer distance than CWAGLP wihout decreasing the navigation’s quality. At the same time, its motor torques are similar to the ones of CWPFLP. Therefore, CWAWLP is capable of considering caster wheel physics without sacrificing navigation capabilities. The formulated caster wheel aware term is compatible with any MPC based navigation algorithm and inherits the derivation of an observer capable of estimating caster wheel rotation angles and rolling speeds. Even if the caster wheel awareness has been implemented in a differential driven robot, this approach is also applicable to vehicles with an alternative drivetrain, such as car-like robots.