ADVANCES IN MULTI-AGENT FLOCKING: CONTINUOUS-TIME AND DISCRETE-TIME ALGORITHMS

We present multi-agent control methods that address flocking in continuous-time and discrete-time settings. The method is decentralized, that is, each agents controller relies on local sensing to determine the relative positions and velocities of nearby agents. In the continuous-time setting, each agent has double-integrator dynamics. In the discrete-time setting, each agent has the discrete-time double-integrator dynamics obtained by sampling the continuous-time double integrator and applying a zero-order hold on the control input. We demonstrate using analysis, numerical simulations, and experimental demonstrations that agents using the flocking methods converge to flocking formations and follow the centralized leader (if applicable)

A Survey on Aerial Swarm Robotics

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 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 this 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

Guided Self-Organizing Particle Systems for Basic Problem Solving

In recent years researchers have shown increasing interest in swarm intelligence as a promising approach to adaptive distributed problem solving. Swarm intelligence consists of techniques inspired by nature, especially social insects and aggregations of animals, and even human interactions. They are based on self-organization (a system's overall behavior emerges from the local interactions among its relatively simple components) and are often decentralized and massively distributed. Particle systems are an approach to swarm intelligence that focus on collective movements, and have been used successfully for applications such as computer animation in graphics and control of movements of autonomous robotic vehicle teams. However, particle system techniques have not been applied substantially to problem solving beyond merely collective navigational tasks. In this dissertation, I present an extension to particle systems that incorporates top-down, high-level control to self-organizing mobile agents, thereby guiding the self-organizing process and making it possible for particle systems to undertake problem solving directed by goal-oriented behavior while retaining their decentralized, local nature. This extended particle system approach is critically evaluated through three experimental studies that are adapted from well-known problems in multi-agent systems: search and collect, cooperative transport and logistics. The results provide evidence that extended particle systems are capable of exhibiting behavior important for distributed problem solving, such as cooperative sensing, division of labor, sharing of information, and developing global strategies through local interactions. They also show that aggregated movements can be utilized to create coordination at different levels and phases of the performance of a task, whether those include navigation or not, making extended particle systems a useful tool in the construction of adaptive distributed systems

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized

Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

Asset protection in a limited swarm environment utilizing artificial potential fields

Asset protection is a behavior in which a team of robots establishes a formation around a resource marked as an asset in a hostile environment in order to protect the asset from threats. The robots are assumed to be homogeneous and run a decentralized control algo- rithm and possess a repulsive quality to the threats. Previous works in this area have used centralized control or considered the use of many robots. This work aims at developing an algorithm that is both decentralized, and able to protect assets using only a few robots. In order to provide this behavior an algorithm coined the Asset Guarding Intelligent System (AeGIS), was developed and analyzed. Using AeGIS, each robot will detect an asset move towards it and form a protective formation around it. AeGIS utilizes Quadratic Artificial Potential Fields (QAPFs) as the robot\u27s path planning module. As such the fields are designed to move the robots into formation, avoid collisions, and in turn protect assets. AeGIS is tested using Leviathan -- an event-driven simulator designed to test groups of autonomous swarm robots employing distributed control algorithms. The success rate of different variations of AeGIS were tested. Additionally, the number of threats, robots employing AeGIS, and the number and mobility of assets were varied to observe their effect on the success rate. The simulation results show that with sufficient number of robots, the assets, static or mobile are well protected against 20 modeled threats. Through these results it is shown that AeGIS is a solution to the asset protection problem

Coverage, connectivity and failure recovery control of wireless sensor networks under mobility

Recent advances in micro-electro-mechanical technology, embedded systems and wireless communications, with demands for greater user mobility have provided a major impetus toward the development of deployable, controllable, and self-healing mobile sensor networks. This thesis considers mobile sensors and the control of their mobility. The objective of this thesis is to present a novel sensor movement control strategy in which a commander controls a cluster of mobile sensors to monitor a target region ahead of the commander, and in the direction of the commander\u27s movement. Once the speed and direction of the movement of the commander are changed, the new positions of the sensors are decided by our control algorithm, and the sensors move to their new positions at a speed and in a direction also determined by the algorithm. After an upper bounded adjustment time, the sensors will all arrive at their new positions and the commander monitors a new region by these sensors. Connectivity between sensors during movement is guaranteed. Simulation results are presented to demonstrate the effectiveness of the movement strategy. Since mobile sensor failure is inevitable and always results in data unavailability and communication unavailability faults, this thesis also presents a fault tolerance strategy, in which an estimation recovery mechanism is used to solve data unavailability fault. An algorithm is introduced which determines the movement of backup sensors in order to guarantee that the network bi-connected, and hence can withstand single sensor faults, and therefore solve communication unavailability problem

Distributed Formation Control for Multi-Vehicle Systems With Splitting and Merging Capability

This letter develops a novel strategy for splitting and merging of agents travelling in formation. The method converts the formation control problem into an optimization problem, which is solved among the agents in a distributed fashion. The proposed control strategy is one type of Distributed Model Predictive Control (DMPC) which allows the system to cope with disturbances and dynamic environments. A modified Alternating Direction Method of Multipliers (ADMM) is designed to solve the trajectory optimization problem and achieve formation scaling. Furthermore, a mechanism is designed to implement path homotopy in splitting and merging of the formation, which examines the H-signature of the generated trajectories. Simulation shows that, by using the proposed method, the formation is able to automatically resize and dynamically split to better avoid obstacles, even in the case of losing communication among agents. Upon splitting the newly formed groups proceed and merge again when it becomes possible