Optimal role and position assignment in multi-robot freely reachable formations

Many multi-robot problems require the achievement of formations as part of the overall mission. This work considers a scenario in which unlabeled homogeneous robots must adopt a given formation pattern buildable anywhere in the environment. This involves finding the relative pose of the formation in regard to the initial robot positions, understood as a translation and a rotation; and the optimal assignment of the role of each robot within the formation. This paper provides an optimal solution for the combined parameters of translation, rotation and assignment that minimizes total displacement. To achieve this objective we first formally prove that the three decision variables are separable. Since computing the optimal assignment without accounting for the rotation is a computationally expensive problem, we propose an algorithm that efficiently computes the optimal roles together with the rotation. The algorithm is provably correct and finds the optimal solution in finite time. A distributed implementation is also discussed. Simulation results characterize the complexity of our solution and demonstrate its effectiveness

A control architecture and human interface for agile, reconfigurable micro aerial vehicle formations

This thesis considers the problem of controlling a group of micro aerial vehicles for agile maneuvering cooperatively, or distributively. We first introduce the background and motivation for micro aerial vehicles, especially for the popular multi-rotor aerial vehicle platform. Then, we discuss the dynamics of quadrotor helicopters. A quadrotor is a specific kind of multi-rotor aerial vehicle with a special property called differential flatness, which simplifies the algorithm of trajectory planning, such that, instead of planning a trajectory in a 12-dimensional state space and 4-dimensional input space, we only need to plan the trajectory in 4-dimensional, so called, flat output space, while the 12-dimensional state and 4-dimensional input can be recovered from a mapping called endogenous transformation. We propose a series of approaches to achieve agile maneuvering of a dynamic quadrotor formation, from controlling a single quadrotor in an artificial vector field, to controlling a group of quadrotors in a Virtual Rigid Body (VRB) framework, to balancing the effect between the human control and autonomy for collision avoidance, and to fast on-line distributed collision avoidance with Buffered Voronoi Cells (BVC). In the vector field method, we generate velocity, acceleration, jerk and snap fields, depending on the tasks, or the positions of obstacles, such that a single quadrotor can easily find its required state and input from the endogenous transformation in order to track the artificial vector field. Next, with a Virtual Rigid Body framework, we let a group of quadrotors follow a single control command while also keeping a required formation, or even reconfigure from one formation to another. The Virtual Rigid Body framework decouples the trajectory planning problem into two sub-problems. Then we consider the problem of collision avoidance of the quadrotor formation when it is meanwhile tele-operated by a single human operator. The autonomy with collision avoidance algorithm, based on the vector field methods for a single quadrotor, is an assistive portion of the quadrotor formation controller, such that the human operator can focus on his/her high-level tasks, leaving the low-level collision avoidance task be handled automatically. We also consider the full autonomy problem of quadrotor formations when reconfiguring from one formation to another by developing a fast, on-line distributed collision avoidance algorithm using Buffered Voronoi Cells (BVCs). Our BVC based collision avoidance algorithm only requires sensed relative position, rather than relative position and velocity, while the computational complexity is comparable to other methods like velocity obstacles. At last, we introduce our experimental quadrotor platform which is built from PixHawk flight controller and Odroid-XU4 single-board computer. The hardware and software architecture of this multiple-quadrotor platform is described in detail so that our platform can easily be adopted and extended with different purposes. Our conclusion remark and discussion of future work are also given in this thesi

Multi-agent persistent surveillance under temporal logic constraints

This thesis proposes algorithms for the deployment of multiple autonomous agents for persistent surveillance missions requiring repeated, periodic visits to regions of interest. Such problems arise in a variety of domains, such as monitoring ocean conditions like temperature and algae content, performing crowd security during public events, tracking wildlife in remote or dangerous areas, or watching traffic patterns and road conditions. Using robots for surveillance is an attractive solution to scenarios in which fixed sensors are not sufficient to maintain situational awareness. Multi-agent solutions are particularly promising, because they allow for improved spatial and temporal resolution of sensor information. In this work, we consider persistent monitoring by teams of agents that are tasked with satisfying missions specified using temporal logic formulas. Such formulas allow rich, complex tasks to be specified, such as "visit regions A and B infinitely often, and if region C is visited then go to region D, and always avoid obstacles." The agents must determine how to satisfy such missions according to fuel, communication, and other constraints. Such problems are inherently difficult due to the typically infinite horizon, state space explosion from planning for multiple agents, communication constraints, and other issues. Therefore, computing an optimal solution to these problems is often infeasible. Instead, a balance must be struck between computational complexity and optimality. This thesis describes solution methods for two main classes of multi-agent persistent surveillance problems. First, it considers the class of problems in which persistent surveillance goals are captured entirely by TL constraints. Such problems require agents to repeatedly visit a set of surveillance regions in order to satisfy their mission. We present results for agents solving such missions with charging constraints, with noisy observations, and in the presence of adversaries. The second class of problems include an additional optimality criterion, such as minimizing uncertainty about the location of a target or maximizing sensor information among the team of agents. We present solution methods and results for such missions with a variety of optimality criteria based on information metrics. For both classes of problems, the proposed algorithms are implemented and evaluated via simulation, experiments with robots in a motion capture environment, or both

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects