In this paper, we derive an algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated volume of space, such as the air space around an airport. The herding algorithm, referred to as the m-waypoint algorithm, is designed using a dynamic model of bird flocking based on Reynolds' rules. We derive bounds on its performance using a combination of reduced-order modeling of the flock's motion, heuristics, and rigorous analysis. A unique contribution of the paper is the experimental demonstration of several facets of the herding algorithm on flocks of live birds reacting to a robotic pursuer. The experiments allow us to estimate several parameters of the flocking model, and especially the interaction between the pursuer and the flock. The herding algorithm is also demonstrated using numerical simulations.

This work presents a novel spherical tensegrity robot, T12-R, which is designed and prototyped based on a twelve-rod tensegrity structure that resembles a rhombicuboctahedron. The geometry of T12-R allows for fast rolling and a detailed description of the robot design is provided. A simulation study of T12-R shows that the robot is capable of performing static locomotion. Control strategies for achieving dynamic locomotion in hardware are discussed.

This work presents a 10 kg tensegrity ball probe that can quickly and precisely deliver a 1 kg payload over a 1 km distance on the Moon by combining cable-driven rolling and thruster-based hopping. Previous research has shown that cable-driven rolling is effective for precise positioning, even in rough terrain. However, traveling large distances using thruster-based hopping, which is made feasible by the lightweight and compliant nature of the tensegrity structure, has not been explored. To evaluate the feasibility of a thruster-based tensegrity robot, a centrally-positioned cold gas thruster with nitrogen propellant was selected, and the system was simulated using the NASA Tensegrity Robotics Toolkit (NTRT) for four hopping profiles on hilly terrains. Optimizing energy efficiency and mechanical capabilities of the tensegrity robot, hopping profiles with a long flight distance per hop, followed by the higher accuracy rolling, are recommended. Simulations also show that thrust regulation can improve energy efficiency. Regulation of thrust magnitude can be achieved using a pressure regulator, but regulation of thrust orientation calls for additional control effort. In this paper, it is demonstrated that gimbal systems as well as shape-shifting control of the tensegrity structure have the potential to regulate thrust orientation. Finally, algorithms for localization and path planning that combine hopping and rolling for energy-efficient navigation are presented.

Robots based on tensegrity structures have the potential to be robust, efficient and adaptable. While traditionally being difficult to control, recent control strategies for ball-shaped tensegrity robots have successfully enabled punctuated rolling, hill-climbing and obstacle climbing. These gains have been made possible through the use of machine learning and physics simulations that allow controls to be “learned” instead of being engineered in a top-down fashion. While effective in simulation, these emergent methods unfortunately give little insight into how to generalize the learned control strategies and evaluate their robustness. These robustness issues are especially important when applied to physical robots as there exists errors with respect to the simulation, which may prevent the physical robot from actually rolling. This paper describes how the robustness can be addressed in three ways: 1) We present a dynamic relaxation technique that describes the shape of a tensegrity structure given the forces on its cables; 2) We then show how control of a tensegrity robot “ball” for locomotion can be decomposed into finding its shape and then determining the position of the center of mass relative to the supporting polygon for this new shape; 3) Using a multi-step Monte Carlo based learning algorithm, we determine the structural geometry that pushes the center of mass out of the supporting polygon to provide the most robust basic mobility step that can lead to rolling. Combined, these elements will give greater insight into the control process, provide an alternative to the existing physics simulations and offer a greater degree of robustness to bridge the gap between simulation and hardware.