Path Planning for Mobile Manipulators for Multiple Task Execution
The planning problem for a mobile manipulator system that must perform a sequence of tasks defined by position, orientation, force, and moment vectors at the end-effector is considered. Each task can be performed in multiple configurations due to the redundancy introduced by mobility. The planning problem is formulated as an optimization problem in which the decision variables for mobility (base position) are separated from the manipulator joint angles in the cost function. The resulting numerical problem is nonlinear with nonconvex, unconnected feasible regions in the decision space. Simulated annealing is proposed as a general solution method for obtaining near-optimal results. The problem formulation and numerical solution by simulated annealing are illustrated for a manipulator system with three degrees of freedom mounted on a base with two degrees of freedom. The results are compared with results obtained by conventional nonlinear programming techniques customized for the particular example system.
A Unified Approach to Motion Control of Mobile Manipulators
This paper presents a simple on-line approach for motion control of
mobile robots made up of a manipulator arm mounted on a mobile base.
The proposed approach is equally applicable to nonholonomic mobile
robots, such as rover-mounted manipulators and to holonomic mobile
robots such as tracked robots or compound manipulators. The
computational efficiency of the proposed control scheme makes it
particularly suitable for real-time implementation.
An Optimization Approach to Planning for Mobile Manipulation
We present an optimization-based approach to grasping and path planning for mobile manipulators. We focus on pick-and-place operations, where a given object must be moved from its start configuration to its goal configuration by the robot. Given only the start and goal configurations of the object and a model of the robot and scene, our algorithm finds a grasp and a trajectory for the robot that will bring the object to its goal configuration. The algorithm consists of two phases: optimization and planning. In the optimization phase, the optimal robot configurations and grasp are found for the object in its start and goal configurations using a co-evolutionary algorithm. In the planning phase, a path is found connecting the two robot configurations found by the optimization phase using Rapidly-Exploring Random Trees (RRTs). We benchmark our algorithm and demonstrate it on a 10 DOF mobile manipulator performing complex pick-and-place tasks in simulation.