Fast Manipulability Maximization Using Continuous-Time Trajectory Optimization

A significant challenge in manipulation motion planning is to ensure agility in the face of unpredictable changes during task execution. This requires the identification and possible modification of suitable joint-space trajectories, since the joint velocities required to achieve a specific endeffector motion vary with manipulator configuration. For a given manipulator configuration, the joint space-to-task space velocity mapping is characterized by a quantity known as the manipulability index. In contrast to previous control-based approaches, we examine the maximization of manipulability during planning as a way of achieving adaptable and safe joint space-to-task space motion mappings in various scenarios. By representing the manipulator trajectory as a continuous-time Gaussian process (GP), we are able to leverage recent advances in trajectory optimization to maximize the manipulability index during trajectory generation. Moreover, the sparsity of our chosen representation reduces the typically large computational cost associated with maximizing manipulability when additional constraints exist. Results from simulation studies and experiments with a real manipulator demonstrate increases in manipulability, while maintaining smooth trajectories with more dexterous (and therefore more agile) arm configurations.Comment: In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS'19), Macau, China, Nov. 4-8, 201

Method and apparatus for configuration control of redundant robots

A method and apparatus to control a robot or manipulator configuration over the entire motion based on augmentation of the manipulator forward kinematics is disclosed. A set of kinematic functions is defined in Cartesian or joint space to reflect the desirable configuration that will be achieved in addition to the specified end-effector motion. The user-defined kinematic functions and the end-effector Cartesian coordinates are combined to form a set of task-related configuration variables as generalized coordinates for the manipulator. A task-based adaptive scheme is then utilized to directly control the configuration variables so as to achieve tracking of some desired reference trajectories throughout the robot motion. This accomplishes the basic task of desired end-effector motion, while utilizing the redundancy to achieve any additional task through the desired time variation of the kinematic functions. The present invention can also be used for optimization of any kinematic objective function, or for satisfaction of a set of kinematic inequality constraints, as in an obstacle avoidance problem. In contrast to pseudoinverse-based methods, the configuration control scheme ensures cyclic motion of the manipulator, which is an essential requirement for repetitive operations. The control law is simple and computationally very fast, and does not require either the complex manipulator dynamic model or the complicated inverse kinematic transformation. The configuration control scheme can alternatively be implemented in joint space

Robotic control of the seven-degree-of-freedom NASA laboratory telerobotic manipulator

A computationally efficient robotic control scheme for the NASA Laboratory Telerobotic Manipulator (LTM) is presented. This scheme utilizes the redundancy of the seven-degree-of-freedom LTM to avoid joint limits and singularities. An analysis to determine singular configurations is presented. Performance criteria are determined based on the joint limits and singularity analysis. The control scheme is developed in the framework of resolved rate control using the gradient projection method, and it does not require the generalized inverse of the Jacobian. An efficient formulation for determining the joint velocities of the LTM is obtained. This control scheme is well suited for real-time implementation, which is essential if the end-effector trajectory is continuously modified based on sensory feedback. Implementation of this scheme on a Motorola 68020 VME bus-based controller of the LTM is in progress. Simulation results demonstrating the redundancy utilization in the robotic mode are presented

Point trajectory planning of flexible redundant robot manipulators using genetic algorithms

The paper focuses on the problem of point-to-point trajectory planning for flexible redundant robot manipulators (FRM) in joint space. Compared with irredundant flexible manipulators, a FRM possesses additional possibilities during point-to-point trajectory planning due to its kinematics redundancy. A trajectory planning method to minimize vibration and/or executing time of a point-to-point motion is presented for FRMs based on Genetic Algorithms (GAs). Kinematics redundancy is integrated into the presented method as planning variables. Quadrinomial and quintic polynomial are used to describe the segments that connect the initial, intermediate, and final points in joint space. The trajectory planning of FRM is formulated as a problem of optimization with constraints. A planar FRM with three flexible links is used in simulation. Case studies show that the method is applicable

A new approach to global control of redundant manipulators

A new and simple approach to configuration control of redundant manipulators is presented. In this approach, the redundancy is utilized to control the manipulator configuration directly in task space, where the task will be performed. A number of kinematic functions are defined to reflect the desirable configuration that will be achieved for a given end-effector position. The user-defined kinematic functions and the end-effector Cartesian coordinates are combined to form a set of task-related configuration variables as generalized coordinates for the manipulator. An adaptive scheme is then utilized to globally control the configuration variables so as to achieve tracking of some desired reference trajectories. This accomplishes the basic task of desired end-effector motion, while utilizing the redundancy to achieve any additional task through the desired time variation of the kinematic functions. The control law is simple and computationally very fast, and does not require the complex manipulator dynamic model

Vision Based Robotic Grasping Tracking of a Moving Object

Real-time vision allows the direct steering of a robotic manipulator. In this thesis we use a stereo rig mounted on an industrial robot (ABB IRB-6/2) which gives us information about the end effector position of a second robot (ABB IRB-2000/3). This second robotic manipulator performs the task of tracking a moving object, which in our case is a rolling ball. The first concern of this project is to deal with the different kinds of problems occurring in such a system. After measuring the time-delays in our system we can establish the degree in which they effect it. We can then use these results for the actual control loop. More specifically a Kalman predictor scheme is implemented to produce estimates of coordinates for requested times, based on the time delay analysis. This Kalman predictor also helps us when we have lost track of either the robotic manipulator or the rolling ball. Finally we perform the actual experiment of tracking the rolling ball. The experimental setup consists of the two robots and the stereo rig mentioned above, as well as a metallic ball rolling on a metallic board. The IRB-6 is mounted with the stereo rig and is set in a pre-decided position to be able to observe the movement of the rolling ball throughout the whole of its course. The other robotic manipulator performs the task of tracking the rolling ball, using as feedback the data received from the stereo rig.</p