Control and Estimation for Cooperative Manipulator Tasks
The objective of this project is to achieve reliable transfer of an object from one robotic manipulator to another. This capability is useful for a number of applications, for instance robotic assembly, or robots with multiple manipulators, such as humanoid robots.Achieving reliable object transfer poses a number of challenges for both control and estimation. As with most manipulation problems, the inverse kinematics problem must be solved so that the desired endpoint location can be specified in Cartesian coordinates, rather than in the joint space of the manipulator. Anadditional challenge particular to the cooperative robotics problem is that more than one manipulator may have a grasp on the same object. Manipulators that are carrying out simple position control may encounter problems when grasping the same object. Minor errors in forward kinematics can lead to large controllerforces, or even unstable dynamics, as each controller tries to counteract the other to drive the perceived error to zero.On the estimation side, carrying out reliable transfer depends critically on determining the grasp state; in other words, does a particular robot have a grasp on the object, or do both have the object? The grasp state must be determined before the sequence of events in a transfer task can proceed. For example, the manipulator receiving the object cannot move away until it is certain that the manipulator passing the object has released. In many instances, having pressure sensors mounted in the hand is infeasible. For example, packaging reasons can mean that the necessary space is not available, as is the case with the JPL LEMUR hexapod. We therefore need to infer the grasp state from the available observations, which are usually supplied by position encoders at the joints.For this project we assume that each manipulator carries out estimation independently, without joint angle observations from the other robot, but with knowledge of its own joint angles and of the commands to be issued to both robots. This is typical of a multi-agent cooperative task, and the lack of observations makes the estimation task even more challenging.This report describes the approach we use to solve this problem, which is comprised of an impedance controller and a hybrid estimator.