Switched adaptive control improves aerial robot arm stability during task changes
A Switched Adaptive Control Framework for Aerial Manipulators Under Dynamic Transitions
Robotics
Summary
Controlling flying robots with robotic arms is tricky, especially when the robot switches from one type of movement or task to another. Changes in how the robot behaves can cause instability, and it’s hard to model how the flying part affects the arm’s movement. The authors created a control system that can adapt to these sudden changes without needing detailed knowledge of how the parts interact. Their method keeps the robot stable during task transitions and worked better than existing methods in tests.
What this means in practice
- •For drone operators: Maintain stable control of drones equipped with robotic arms when switching between different tasks or movements.
- •For robotics engineers: Design controllers for flying robots that can handle uncertain interactions between the drone and its manipulator without detailed modeling.
Authors
Rishabh Dev Yadav, Saksham Gupta, Amitabh Sharma, Sarthak Mishra, Wei Pan, Spandan Roy, Simone Baldi
Abstract
Aerial manipulators represent the forefront of aerial robotics. Although potentially capable of complex interaction tasks, controlling aerial manipulators throughout the dynamic transitions occurring during task execution presents significant challenges. Abrupt or discontinuous changes in system dynamics generated by the transitions suggest the use of a switched approach, yet the available aerial manipulation methods are not designed for coping with switched regimes. In addition, most available methods fall short in coping with the tight couplings between the aerial vehicle and the manipulator, as well as in coping with the state-dependent uncertainties arising from the difficulty in modeling such couplings. We propose a switched-based adaptive control framework for aerial manipulators not relying on a priori knowledge of the vehicle-manipulator couplings and of state-dependent uncertainties. To guarantee stable manipulation despite changes in system dynamics, the framework provides a class of switching signals characterizing those transition phases for which the system is guaranteed to remain stable. Comparative experiments further validate the effectiveness of the proposed switched-based framework over the state of the art.