A Survey on Aerial Swarm Robotics

Abstract

Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

Optimal Guidance and Control with Nonlinear Dynamics Using Sequential Convex Programming

“…Recently, SCP has been applied to tougher problems with tight constraints like optimal entry and landing [20][21][22]. Often, the optimal state trajectory found with SCP is implemented with a tracking controller rather than determining the optimal control concurrently with the optimal state [23].…”

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

“…To leverage inertial, energetic, and cost benefits of small-scale robots, critical future applications of autonomous technologies may depend on coordinating large numbers of agents with minimal onboard sens-ing and communication resources. However, a critical problem for autonomous multi-robot groups is that state-of-the-art control schemes break down as robotic agents are scaled down (decreasing agent resources) and the numerical size of swarms is scaled up (increasing communication and coordination requirements) (Murray, 2007;Hamann et al, 2016;Yang et al, 2018;Chung et al, 2018). NeuroSwarms addresses the hypothesis that a similar distributed scaling problem may have been solved by the evolved neural architecture of mammalian brains.…”

The Influence of Limited Visual Sensing on the Reynolds Flocking Algorithm

“…Recent advances in the field of aerial robotics and sensor technologies have greatly enhanced the capabilities of unmanned aerial vehicles (UAVs). One consequence of this outcome has been the growing interest in multi-aerial vehicle applications [1], [2], [3]. Clear benefits of multi-drone systems are envisioned for a wide range of missions including search and rescue [4], long-term monitoring [5], sensor data collection [6], indoor navigation [7], environment exploration [8] and cooperative grasping and transportation [9].…”