Fast Manipulability Maximization Using Continuous-Time Trajectory Optimization
This work addresses the problem of improving agility and adaptability in robotic manipulation for tasks requiring unpredictable changes, representing an incremental advancement over previous control-based approaches.
The paper tackled the challenge of ensuring agile manipulation by maximizing the manipulability index during motion planning, resulting in increased manipulability and more dexterous arm configurations in simulations and real-world experiments.
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.