53,228 research outputs found
A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute paths
in high-dimensional state spaces while accounting for physical constraints
related to motors and joints, generating smooth and stable motions, avoiding
obstacles, and preventing collisions. A motion planning algorithm must
therefore balance competing demands, and should ideally incorporate uncertainty
to handle noise, model errors, and facilitate deployment in complex
environments. To address these issues, we introduce a framework for robot
motion planning based on variational Gaussian Processes, which unifies and
generalizes various probabilistic-inference-based motion planning algorithms.
Our framework provides a principled and flexible way to incorporate
equality-based, inequality-based, and soft motion-planning constraints during
end-to-end training, is straightforward to implement, and provides both
interval-based and Monte-Carlo-based uncertainty estimates. We conduct
experiments using different environments and robots, comparing against baseline
approaches based on the feasibility of the planned paths, and obstacle
avoidance quality. Results show that our proposed approach yields a good
balance between success rates and path quality
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
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