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A Purely-Reactive Manipulability-Maximising Motion Controller
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We present a novel approach to controlling the instantaneous velocity of a robot end-effector that is able to simultaneously maximise manipulability and avoid joint limits. It operates on non-redundant and redundant robots, which is achieved by adding artificial redundancy in the form of controlled path deviation. We formulate the problem as a quadratic programme and provide an open-source Python implementation that provides solutions in just a few milliseconds. It accepts a robot model expressed using URDF or Denavit-Hartenberg parameterisation. We compare our method to previous work in simulation and on a physical robot.
Forward citations
Cited by 3 Pith papers
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ReMoSPLAT: Reactive Mobile Manipulation Control on a Gaussian Splat
ReMoSPLAT achieves reactive mobile-manipulation collision avoidance by querying distances from a Gaussian Splat reconstruction, matching a ground-truth-SDF controller in simulation.
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Reactive Model Predictive Contouring Control for Robot Manipulators
A 100 Hz reactive model predictive contouring controller with control barrier functions lets robot arms follow paths while avoiding obstacles, singularities, and self-collisions.
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Residual Force Polytope: Admissible Task-Space Forces of Dynamic Trajectories
A dynamic-aware 'residual force polytope' is introduced to represent the task-space forces a robot can resist during a planned motion, and it is used as a trajectory optimization objective to improve robustness.
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