Real-Time Generation of Near-Minimum-Energy Trajectories via
Constraint-Informed Residual Learning
Real-Time Generation of Near-Minimum-Energy Trajectories via
Constraint-Informed Residual Learning
Industrial robotics demands significant energy to operate, making energy-reduction methodologies increasingly important. Strategies for planning minimum-energy trajectories typically involve solving nonlinear optimal control problems (OCPs), which rarely cope with real-time requirements. In this paper, we propose a paradigm for generating near minimum-energy trajectories for manipulators by learning from optimal solutions. …