The recent development of novel aerial vehicles capable of physically interacting with the environment leads to new applications such as contact-based inspection. These tasks require the robotic system to exchange forces with partially-known environments, which may contain uncertainties including unknown spatially-varying friction properties and discontinuous variations of the surface geometry. Finding a control strategy that is robust against these environmental uncertainties remains an open challenge. This paper presents a learning-based adaptive control strategy for aerial sliding tasks. In particular, the gains of a standard impedance controller are adjusted in real-time by a policy based on the current control signals, proprioceptive measurements, and tactile sensing. This policy is trained in simulation with simplified actuator dynamics in a student-teacher learning setup. The real-world performance of the proposed approach is verified using a tilt-arm omnidirectional flying vehicle. The proposed controller structure combines data-driven and model-based control methods, enabling our approach to successfully transfer directly and without adaptation from simulation to the real platform. Compared to fine-tuned state of the art interaction control methods we achieve reduced tracking error and improved disturbance rejection.
翻译:最近开发了能够与环境进行物理互动的新航空飞行器,从而产生了新的应用,例如基于接触的检查。这些任务要求机器人系统与部分已知的环境交换力量,这些环境可能包含不确定因素,包括空间变化莫变的未知摩擦特性和地表几何不连续的变化。寻找一种能够抵御这些环境不确定性的控制战略仍然是一项公开的挑战。本文件为空中滑动任务提出了一个基于学习的适应性控制战略。特别是,标准阻力控制器的收益通过基于当前控制信号、自主感测测量和触动感测的政策实时调整。该政策在学生-教师学习设置中,以简化的动能动态进行模拟培训。拟议方法的真实世界性表现是使用倾斜武器全方向飞行飞行器加以核查的。拟议的控制器结构将数据驱动和模型控制方法结合起来,使我们能够在不从模拟向实际平台进行调整的情况下,直接和不作调整地成功转移。与微调的艺术互动控制方法相比,我们减少了跟踪错误并改进了扰动拒绝。