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

Intelligent Hybrid Approach for Multi Robots- Multi Objectives Motion Planning Optimization

Abstract: This paper proposes enhanced approach to find multi objective optimization and obstacle avoidance of motion planning problem for multi mobile robots that have to move smoothly, safely with a shorter time and minimum distance along curvature-constrained motion planning in completely known dynamic environments. The research includes two stages: the first stage is to find an multi objective optimal path and trajectory planning for each robot individually using the Enhanced GA with modified A*. The second stage consists of designing a fuzzy logic to control the movement of the robots with collision free. The global optimal trajectory is fed to fuzzy motion controller which has ability to regenerate the local trajectory of the robot based on the probability of having another dynamic robot in the area. A simulation of the strategy has been presented and the results show that the proposed approach is able to achieve multi objective optimization of motion planning for multi mobile robot in dynamic environment efficiently. Also, it has the ability to find a solution when the environment is complex and the number of obstacles is increasing. The performance of the above mentioned approach has been found to be satisfactory of dynamic obstacle avoidance