昆虫尺度扑翼飞行机器人通过深度学习的鲁棒管模型预测控制实现特技机动
Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control
AI总结：
本研究通过深度学习的鲁棒管模型预测控制，在750毫克扑翼机器人上实现了昆虫级特技机动，显著提升了扫视速度和加速度，并完成了连续身体翻转。
摘要：
空中昆虫在扰动下表现出高度敏捷的机动，如急刹车、扫视和身体翻转。相比之下，昆虫尺度的空中机器人仅限于跟踪具有小身体加速度的非激进轨迹。这种性能差距是由低机器人惯性、快速动力学、扑翼空气动力学的不确定性以及对环境扰动的高敏感性共同造成的。执行高度动态的机动需要生成推动硬件极限的激进飞行轨迹，以及考虑模型和环境不确定性的高速率反馈控制器。在这里，通过设计一种深度学习的鲁棒管模型预测控制器，我们在一个750毫克的扑翼机器人上展示了类似昆虫的飞行敏捷性和鲁棒性。我们的模型预测控制器能够在扰动下跟踪激进的飞行轨迹。为了在计算受限的实时系统中实现高反馈速率，我们设计了模仿学习方法，训练一个两层全连接神经网络，该网络类似于由中枢神经系统和运动神经元组成的昆虫飞行控制架构。我们的机器人展示了侧向速度和加速度分别为每秒197厘米和每秒11.7米平方的类似昆虫的扫视运动，相比之前的结果分别提高了447%和255%。该机器人还能够在每秒160厘米的风扰动和大的指令到力映射误差下执行扫视机动。此外，它在11秒内连续执行了10次身体翻转——这是亚克级飞行器中最具挑战性的机动。这些结果标志着实现昆虫尺度飞行敏捷性的里程碑，并激发了对传感和计算自主性的未来研究。
英文摘要：
Aerial insects exhibit highly agile maneuvers such as sharp braking, saccades, and body flips under disturbance. In contrast, insect-scale aerial robots are limited to tracking non-aggressive trajectories with small body acceleration. This performance gap is contributed by a combination of low robot inertia, fast dynamics, uncertainty in flapping-wing aerodynamics, and high susceptibility to environmental disturbance. Executing highly dynamic maneuvers requires the generation of aggressive flight trajectories that push against the hardware limit and a high-rate feedback controller that accounts for model and environmental uncertainty. Here, through designing a deep-learned robust tube model predictive controller, we showcase insect-like flight agility and robustness in a 750-millgram flapping-wing robot. Our model predictive controller can track aggressive flight trajectories under disturbance. To achieve a high feedback rate in a compute-constrained real-time system, we design imitation learning methods to train a two-layer, fully connected neural network, which resembles insect flight control architecture consisting of central nervous system and motor neurons. Our robot demonstrates insect-like saccade movements with lateral speed and acceleration of 197 centimeters per second and 11.7 meters per second square, representing 447$\%$ and 255$\%$ improvement over prior results. The robot can also perform saccade maneuvers under 160 centimeters per second wind disturbance and large command-to-force mapping errors. Furthermore, it performs 10 consecutive body flips in 11 seconds - the most challenging maneuver among sub-gram flyers. These results represent a milestone in achieving insect-scale flight agility and inspire future investigations on sensing and compute autonomy.
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。
