Cordie Troy, Roberts Jonathan, Dunbabin Matthew, Dungavell Ross, Bandyopadhyay Tirthankar
CSIRO Robotics, Data61, Pullenvale, QLD, Australia.
Faculty of Engineering, Queensland University of Technology, QLD, Brisbane, Australia.
Front Robot AI. 2024 Mar 13;11:1225297. doi: 10.3389/frobt.2024.1225297. eCollection 2024.
Actuator failure on a remotely deployed robot results in decreased efficiency or even renders it inoperable. Robustness to these failures will become critical as robots are required to be more independent and operate out of the range of repair. To address these challenges, we present two approaches based on modular robotic architecture to improve robustness to actuator failure of both fixed-configuration robots and modular reconfigurable robots. Our work uses modular reconfigurable robots capable of modifying their style of locomotion and changing their designed morphology through ejecting modules. This framework improved the distance travelled and decreased the effort to move through the environment of simulated and physical robots. When the deployed robot was allowed to change its locomotion style, it showed improved robustness to actuator failure when compared to a robot with a fixed controller. Furthermore, a robot capable of changing its locomotion and design morphology statistically outlasted both tests with a fixed morphology. Testing was carried out using a gazebo simulation and validated in multiple tests in the field. We show for the first time that ejecting modular failed components can improve the overall mission length.
远程部署的机器人出现执行器故障会导致效率降低,甚至使其无法运行。随着机器人需要更加独立并在无法维修的范围内运行,对这些故障的鲁棒性将变得至关重要。为应对这些挑战,我们提出了两种基于模块化机器人架构的方法,以提高固定配置机器人和模块化可重构机器人对执行器故障的鲁棒性。我们的工作使用了模块化可重构机器人,它能够通过弹出模块来改变其运动方式并改变其设计形态。该框架提高了模拟机器人和实体机器人在环境中的移动距离,并减少了移动所需的努力。当部署的机器人被允许改变其运动方式时,与具有固定控制器的机器人相比,它对执行器故障表现出更高的鲁棒性。此外,一个能够改变其运动和设计形态的机器人在统计上比两种具有固定形态的测试机器人都更持久。测试使用了Gazebo模拟进行,并在现场的多次测试中得到验证。我们首次表明,弹出模块化故障组件可以提高总体任务时长。