Closed-Loop Perching and Spatial Guidance Laws for Bio-Inspired Articulated Wing MAV

This paper presents the underlying theoretical developments and successful experimental demonstrations of perching of an aerial robot. The open-loop lateral-directional dynamics of the robot are inherently unstable because it lacks a vertical tail for agility, similar to birds. A unique feature of this robot is that it uses wing articulation for controlling the flight path angle as well as the heading. New guidance algorithms with guaranteed stability are obtained by rewriting the flight dynamic equations in the spatial domain rather than as functions of time, after which dynamic inversion is employed. It is shown that nonlinear dynamic inversion naturally leads to proportional-integral-derivative (PID) controllers, thereby providing an exact method for tuning the gains. The effectiveness of the proposed bio-inspired robot design and its novel closed-loop perching controller has been successfully demonstrated with perched landings on a human hand

Human Demonstrations for Fast and Safe Exploration in Reinforcement Learning

<p>Reinforcement learning is a promising framework for controlling complex vehicles with a high level of autonomy, since it does not need a dynamic model of the vehicle, and it is able to adapt to changing conditions. When learning from scratch, the performance of a reinforcement learning controller may initially be poor and -for real life applications- unsafe. In this paper the effects of using human demonstrations on the performance of reinforcement learning is investigated, using a combination of offline and online least squares policy iteration. It is found that using the human as an efficient explorer improves learning time and performance for a benchmark reinforcement learning problem. The benefit of the human demonstration is larger for problems where the human can make use of its understanding of the problem to efficiently explore the state space. Applied to a simplified quadrotor slung load drop off problem, the use of human demonstrations reduces the number of crashes during learning. As such, this paper contributes to safer and faster learning for model-free, adaptive control problems.</p