A Local Motion Planner for Closed-loop Robots

Global motion planners have been proposed for closed-loop robot based on the same paradigm than has been proposed for serial chains. First a sparse representation of the configuration space of the robot is constructed as a set of nodes. This is somewhat more complicated than for serial chain as the closure equations of the mechanism should be satisfied. Then a motion planning query consists simply in connecting the start and goal points through an appropriate set of nodes (usually minimizing the length of the trajectory). But such motion planner should be complemented by a local motion planner that addresses the following issues: \begin{enumerate} \item ensure that two successive nodes belong to the same robot kinematic branch (otherwise connecting these nodes will require to disassemble the robot) \item verify that all poses between nodes satisfy the robot constraints (if possible taking into account the uncertainties in the robot modeling) \item eventually try to shorten the trajectory length \end{enumerate} We present such a local motion planner that addresses all three issues and illustrates its use on a Gough parallel robot

A randomized kinodynamic planner for closed-chain robotic systems

Kinodynamic RRT planners are effective tools for finding feasible trajectories in many classes of robotic systems. However, they are hard to apply to systems with closed-kinematic chains, like parallel robots, cooperating arms manipulating an object, or legged robots keeping their feet in contact with the environ- ment. The state space of such systems is an implicitly-defined manifold, which complicates the design of the sampling and steering procedures, and leads to trajectories that drift away from the manifold when standard integration methods are used. To address these issues, this report presents a kinodynamic RRT planner that constructs an atlas of the state space incrementally, and uses this atlas to both generate ran- dom states, and to dynamically steer the system towards such states. The steering method is based on computing linear quadratic regulators from the atlas charts, which greatly increases the planner efficiency in comparison to the standard method that simulates random actions. The atlas also allows the integration of the equations of motion as a differential equation on the state space manifold, which eliminates any drift from such manifold and thus results in accurate trajectories. To the best of our knowledge, this is the first kinodynamic planner that explicitly takes closed kinematic chains into account. We illustrate the performance of the approach in significantly complex tasks, including planar and spatial robots that have to lift or throw a load at a given velocity using torque-limited actuators.Peer ReviewedPreprin

A Certified-Complete Bimanual Manipulation Planner

Planning motions for two robot arms to move an object collaboratively is a difficult problem, mainly because of the closed-chain constraint, which arises whenever two robot hands simultaneously grasp a single rigid object. In this paper, we propose a manipulation planning algorithm to bring an object from an initial stable placement (position and orientation of the object on the support surface) towards a goal stable placement. The key specificity of our algorithm is that it is certified-complete: for a given object and a given environment, we provide a certificate that the algorithm will find a solution to any bimanual manipulation query in that environment whenever one exists. Moreover, the certificate is constructive: at run-time, it can be used to quickly find a solution to a given query. The algorithm is tested in software and hardware on a number of large pieces of furniture.Comment: 12 pages, 7 figures, 1 tabl