Trajectory optimization for a class of robots belonging to Constrained Collaborative Mobile Agents (CCMA) family

TL;DR

This paper introduces a new class of constrained mobile robots called CCMA, along with a versatile trajectory optimization method that enhances their design and control, demonstrated through simulations and prototype experiments.

Contribution

It presents a novel CCMA robot class with non-holonomic constraints and a general trajectory optimization approach that integrates design and control policy optimization.

Findings

Trajectory optimization improves CCMA workspace and manipulation capabilities.

Simulation results demonstrate effectiveness across various CCMA configurations.

Experimental validation confirms practical applicability with prototypes.

Abstract

We present a novel class of robots belonging to Constrained Collaborative Mobile Agents (CCMA) family which consists of ground mobile bases with non-holonomic constraints. Moreover, these mobile robots are constrained by closed-loop kinematic chains consisting of revolute joints which can be either passive or actuated. We also describe a novel trajectory optimization method which is general with respect to number of mobile robots, topology of the closed-loop kinematic chains and placement of the actuators at the revolute joints. We also extend the standalone trajectory optimization method to optimize concurrently the design parameters and the control policy. We describe various CCMA system examples, in simulation, differing in design, topology, number of mobile robots and actuation space. The simulation results for standalone trajectory optimization with fixed design parameters is presented for CCMA system examples. We also show how this method can be used for tasks other than end-effector positioning such as internal collision avoidance and external obstacle avoidance. The concurrent design and control policy optimization is demonstrated, in simulations, to increase the CCMA system workspace and manipulation capabilities. Finally, the trajectory optimization method is validated in experiments through two 4-DOF prototypes consisting of 3 tracked mobile bases.