Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante
This addresses the need for real-time trajectory planning in robotics, offering a practical solution that is easy to implement and applicable to various systems, though it is incremental as it builds on existing optimization methods.
The paper tackles the problem of enabling autonomous robots to perform online trajectory planning by introducing Guided Trajectory Learning, which learns a function approximation from offline trajectory optimization solutions to generate trajectories efficiently online, demonstrating computational performance on the Atalante exoskeleton for flat-foot walking.
Autonomous robots require online trajectory planning capability to operate in the real world. Efficient offline trajectory planning methods already exist, but are computationally demanding, preventing their use online. In this paper, we present a novel algorithm called Guided Trajectory Learning that learns a function approximation of solutions computed through trajectory optimization while ensuring accurate and reliable predictions. This function approximation is then used online to generate trajectories. This algorithm is designed to be easy to implement, and practical since it does not require massive computing power. It is readily applicable to any robotics systems and effortless to set up on real hardware since robust control strategies are usually already available. We demonstrate the computational performance of our algorithm on flat-foot walking with the self-balanced exoskeleton Atalante.