在不平坦地形上实现动态腿部运动的基于力的自适应控制
Adaptive Force-Based Control of Dynamic Legged Locomotion over Uneven Terrain
AI总结：
本文提出一种将自适应控制融入基于力控制器的四足机器人方法，以应对模型不确定性与未知地形冲击，使Unitree A1能承载高达自重50%的负载并在崎岖地形上动态小跑、跳跃。
摘要：
敏捷腿足机器人已被证明在复杂且具有挑战性的环境（包括灾区和工业场景）中导航和执行任务非常有效。然而，这些应用通常要求机器人具备在保持动态运动的同时承载重载的能力。因此，本文提出了一种将自适应控制融入基于力的控制系统的新方法。四足机器人控制领域的最新进展表明，力控制可以有效实现在崎岖地形上的动态运动。通过将自适应控制集成到基于力的控制器中，我们提出的方法能够保留基线框架的优势，同时适应显著的模型不确定性和未知的地形冲击模型。实验验证已在Unitree A1机器人上成功开展。借助我们的方法，机器人能够承载重载（最高可达其自身重量的50%），同时在不平坦地形上完成快速对角小跑和跳跃等动态步态。
英文摘要：
Agile-legged robots have proven to be highly effective in navigating and performing tasks in complex and challenging environments, including disaster zones and industrial settings. However, these applications normally require the capability of carrying heavy loads while maintaining dynamic motion. Therefore, this paper presents a novel methodology for incorporating adaptive control into a force-based control system. Recent advancements in the control of quadruped robots show that force control can effectively realize dynamic locomotion over rough terrain. By integrating adaptive control into the force-based controller, our proposed approach can maintain the advantages of the baseline framework while adapting to significant model uncertainties and unknown terrain impact models. Experimental validation was successfully conducted on the Unitree A1 robot. With our approach, the robot can carry heavy loads (up to 50% of its weight) while performing dynamic gaits such as fast trotting and bounding across uneven terrains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
