Reactive Motion Planning and Control under Constraints

Doktorsavhandling, 2023

Modern robots are increasingly being designed to operate in dynamic and unstructured environments shared with humans and other mobile agents. In these scenarios, the robot must react to any online detected changes to provide a correct and safe operation.
This thesis introduces techniques for motion planning and control employing Dynamical Systems (DSs) formulations, such as Artificial Potential Fields (APF) and Dynamical Movement Primitives (DMP). While several DS-based methods facilitate reactivity to changes in the robot environment with guaranteed convergence to a specified goal, they often lack the integration of robot constraints. Moreover, convergence depends on specific environmental conditions, e.g., the environment being a star world.

To extend the range of practical scenarios in which convergence to the goal is guaranteed in star worlds, a method is proposed for online adjustment of the robot's perceived environment to align with the necessary conditions. This method is enabled through a comprehensive exploration of the concept starshaped hull. To account for robot constraints, two strategies are proposed. The first approach employs online modification of the DS through a scaling factor to meet velocity and acceleration constraints. Compared to standard scaling approaches, focus is placed on mitigating the feasibility issues that are more prominent in reactive motion planning. In particular, by proactively scaling the DS before reaching the acceleration limits, the proposed method retains the feasibility for a wider set of trajectories. The efficacy of the scaling is showcased by experiments on a robotic arm. The second approach integrates a DS method within a Model Predictive Control (MPC) scheme. This integration effectively combines the favorable convergence characteristics of the DS with the intuitive representation of system constraints offered by MPC. In this configuration, the DS generates a path with an obstacle clearance, while the MPC provides a feasible trajectory that adheres to this clearance distance. The scheme is proven to accomplish collision avoidance and to preserve the convergence guarantees provided by the DS for a substantial portion of the workspace. Furthermore, the scheme is extended to encompass path-following control as well.