The complexity of the path finding and navigation problem increases when multiple agents are involved and these agents have to maintain a predefined formation while moving on a 3-D terrain. In this thesis, a novel approach for multiresolution formation representation is proposed, that allows hierarchical formations of arbitrary depth to be defined using different referencing schemes. This formation representation approach is then utilized to find and realize a collision free optimal path from an initial location to a goal location on a 3-D terrain, while preserving the formation. The proposed metod first employs a terrain analysis technique that constructs a weighted search graph from height-map data. The graph is used by an off-line search algorithm to find the shortest path. The path is realized by an on-line planner, which guides the formation along the path while avoiding collisions and maintaining the formation. The methods proposed here are easily adaptable to several application areas, especially to real time strategy games and military simulations.

Our research focuses on one of the more fundamental issues in multi-agent, mobile robotics: the formation control problem. The idea is to create controllers that cause robots to move into a predefined formation shape. This is a well studied problem for the scenario in which the robots know in advance to which point in the formation they are assigned. In our case, we assume this information is not given in advance, but must be determined dynamically. This thesis presents an algorithm that can be used by a network of mobile robots to simultaneously determine efficient robot assignments and formation pose for rotationally and translationally invariant formations. This allows simultaneous role assignment and formation sysnthesis without the need for additional control laws. The thesis begins by introducing some general concepts regarding multi-agent robotics. Next, previous work and background information specific to the formation control and assignment problems are reviewed. Then the proposed assignment al- gorithm for role assignment and formation control is introduced and its theoretical properties are examined. This is followed by a discussion of simulation results. Lastly, experimental results are presented based on the implementation of the assignment al- gorithm on actual robots.

This thesis considers the cooperation and coordination of multi vehicle systems cohesively in order to keep the formation geometry and provide the string stability. We first present the modeling of aerial and road vehicles representing different motion characteristics suitable for cooperative operations. Then, a set of three dimensional cohesive motion coordination and formation control schemes for teams of autonomous vehicles is proposed. The two main components of these schemes are i) platform free high level online trajectory generation algorithms and ii) individual trajectory tracking controllers. High level algorithms generate the desired trajectories for three dimensional leader-follower structured tight formations, and then distributed controllers provide the individual control of each agent for tracking the desired trajectories. The generic goal of the control scheme is to move the agents while maintaining the formation geometry. We propose a distributed control scheme to solve this problem utilizing the notions of graph rigidity and persistence as well as techniques of virtual target tracking and smooth switching. The distributed control scheme is developed by modeling the agent kinematics as a single-velocity integrator; nevertheless, extension to the cases with simplified kinematic and dynamic models of fixed-wing autonomous aerial vehicles and quadrotors is discussed. The cohesive cooperation in three dimensions is so beneficial for surveillance and reconnaissance activities with optimal geometries, operation security in military activities, more viable with autonomous flying, and future aeronautics aspects, such as fractionated spacecraft and tethered formation flying. We then focus on motion control task modeling for three dimensional agent kinematics and considering parametric uncertainties originated from inertial measurement noise. We design an adaptive controller to perform the three dimensional motion control task, paying attention to the parametric uncertainties, and employing a recently developed immersion and invariance based scheme. Next, the cooperative driving of road vehicles in a platoon and string stability concepts in one-dimensional traffic are discussed. Collaborative driving of commercial vehicles has significant advantages while platooning on highways, including increased road-capacity and reduced traffic congestion in daily traffic. Several companies in the automotive sector have started implementing driver assistance systems and adaptive cruise control (ACC) support, which enables implementation of high level cooperative algorithms with additional softwares and simple electronic modifications. In this context, the cooperative adaptive cruise control approach are discussed for specific urban and highway platooning missions. In addition, we provide details of vehicle parameters, mathematical models of control structures, and experimental tests for the validation of our models. Moreover, the impact of vehicle to vehicle communication in the existence of static road-side units are given. Finally, we propose a set of stability guaranteed controllers for highway platooning missions. Formal problem definition of highway platooning considering constant and velocity dependent spacing strategies, and formal string stability analysis are included. Additionally, we provide the design of novel intervehicle distance based priority coefficient of feed-forward filter for robust platooning. In conclusion, the importance of increasing level of autonomy of single agents and platoon topology is discussed in performing cohesive coordination and collaborative driving missions and in mitigating sensory errors. Simulation and experimental results demonstrate the performance of our cohesive motion and string stable controllers, in addition we discuss application in formation control of autonomous multi-agent systems.

<div> <div> <div> <p>A control methodology to design and establish spacecraft formations is presented. The intuitive design of complex spacecraft formation geometry is achieved by utilizing two different sets of relative orbital elements derived from a linearization of the dynamics. These sets provide strong insights into the shape, size, and orientation of the relative trajectory and facilitate the design of relative orbits in addition to relative positions. An artificial potential function (APF) composed of an attractive potential for goal seeking and a repulsive potential for obstacle avoidance is constructed. The derivation of a control law from this APF results in a computationally efficient algorithm able to fully control the relative position and velocity of the spacecraft and therefore to establish spacecraft formations. The autonomous selection of some of the design parameters of the model based on fuel minimization considerations is described. An assessment of the formation establishment accuracy is conducted for different orbital perturbation as well as various degrees of thrust errors and state uncertainties. Then, the performance of the control algorithm is demonstrated with the numerical simulation of four different scenarios. The first scenario is the design and establishment of a 10-spacecraft triangular lattice, followed by the establishment of a 37-spacecraft formation composed of two hexagonal lattices on two different relative planes. The control method is used to illustrate proximity operations with the visual inspection of an on-orbit structure in the third scenario. Finally, a formation composed of four spacecraft arranged in a tetrahedron is presented.<br></p> </div> </div> </div>

Quadcopter drone formation control is an important capability for fields like area surveillance, search and rescue, agriculture, and reconnaissance. Of particular interest is formation control in environments where radio communications and/or GPS may be either denied or not sufficiently accurate for the desired application. To address this, we focus on vision as the sensing modality. We train an Hourglass Convolutional Neural Network (CNN) to discriminate between quadcopter pixels and non-quadcopter pixels in a live video feed and use it to guide a formation of quadcopters. The CNN outputs "heatmaps" - pixel-by-pixel likelihood estimates of the presence of a quadcopter. These heatmaps suffer from short-lived false detections. To mitigate these, we apply a version of the Siamese networks technique on consecutive frames for clutter mitigation and to promote temporal smoothness in the heatmaps. The heatmaps give an estimate of the range and bearing to the other quadcopter(s), which we use to calculate flight control commands and maintain the desired formation. We implement the algorithm on a single-board computer (ODROID XU4) with a standard webcam mounted to a quadcopter drone. Flight tests in a motion capture volume demonstrate successful formation control with two quadcopters in a leader-follower setup.

Training autonomous agents that are capable of performing their assigned job without fail is the ultimate goal of deep reinforcement learning. This thesis introduces a dueling Quantile Regression Deep Q-network, where the network learns the state value quantile function and advantage quantile function separately. With this network architecture the agent is able to learn to control simulated robots in the Gazebo simulator. Carefully crafted reward functions and state spaces must be designed for the agent to learn in complex non-stationary environments. When trained for only 100,000 timesteps, the agent is able reach asymptotic performance in environments with moving and stationary obstacles using only the data from the inertial measurement unit, LIDAR, and positional information. Through the use of transfer learning, the agents are also capable of formation control and flocking patterns. The performance of agents with frozen networks is improved through advice giving in Deep Q-networks by use of normalized Q-values and majority voting.

This dissertation focuses on the path planning of unmanned aerial vehicle (UAV) swarms under distributed and hybrid control scenarios. It presents two such models and analyzes them both from theory and practice. In the first method, a distributed formation control strategy for UAV swarm based on consensus law is presented. This model makes use of the fundamental concepts of leader-follower structure, social potential functions, and algebraic graph theory to jointly address flocking and de-confliction in the formation control problem. The impact of network topology on formation control is analyzed. It is shown that the degree distribution of the network representing the multi-agent system defines the rate at which formation is attained. Conditions for convergence and stability are derived. In the second method, a hybrid framework for path planning and coverage area by UAV swarms is presented. This strategy significantly improves the current labor-intensive and resource-constraint operations in aquaculture farms. To monitor the farms periodically, an optimized back-and-forth flight path based on the Shamos algorithm is utilized. A trajectory tracking strategy for UAV swarms under uncertain wind conditions is presented.

Recent rapid advances in computing, communication, sensing, and actuation, together with miniaturization technologies, have offered unprecedented opportunities to employ large numbers of autonomous vehicles (air, ground, and water) working cooperatively to accomplish a mission. Cooperative control of such multi-agent dynamical systems has potential impact on numerous civilian, homeland security, and military applications. Compared with single-agent control problems, new theoretical and practical challenges emerge and need to be addressed in cooperative control of multiagent dynamical systems, including but not limited to problem size, task coupling, limited computational resources at individual agent level, communication constraints, and the need for real-time obstacle/collision avoidance. In order to address these challenges, a unified optimal multi-agent cooperative control strategy is proposed to formulate the multi-objective cooperative control problem into one unified optimal control framework. Many cooperative behaviors, such as consensus, cooperative tracking, formation, obstacle/collision avoidance, or flocking with cohesion and repulsion, can be treated in one optimization process. An innovative inverse optimal control approach is utilized to include these cooperative objectives in derived cost functions such that a closedorm cooperative control law can be obtained. In addition, the control law is distributed and only depends on the local neighboring agents’ information. Therefore, this new method does not demand intensive computational load and is easy for real-time onboard implementation. Furthermore, it is very scalable to large multi-agent cooperative dynamical systems. The closed-loop asymptotic stability and optimality are theoretically proved. Simulations based on MATLAB are conducted to validate the cooperative behaviors including consensus, Rendezvous, formation flying, and flocking, as well as the obstacle avoidance performance.

This dissertation concerns the formation problems for multiple reduced attitudes, which are extensively utilized in many pointing applications and under-actuated scenarios for attitude maneuvers. In contrast to most existing methodologies on formation control, the proposed method does not need to contain any formation errors in the protocol. Instead, the constructed formation is attributed to geometric properties of the configuration space and the designed connection topology. We refer to this type of formation control as intrinsic formation control. Besides, the control protocols proposed in this work are designed directly in space S2, avoiding to use any attitude parameterisations. At last but not least, along the studies, some elementary tools for reduced attitudes control are developed.In paper A, a continuous control law is provided for a reduced attitude systems, by which a regular tetrahedron formation can achieve asymptotic stability under a quite large family of gain functions in the control. Then, with a further restriction on the control gain, almost global stability of the tetrahedron formation is also obtained. In this work, we introduce a novel coordinates transformation that represents the relative reduced attitudes be-tween the agents. The proposed method is an intrinsic formation control that does not need to involve any information of the desired formation before-hand. Another virtue of the method proposed is that only relative attitude measurement is required.Paper B further concerns the formation control of all regular polyhedral configurations (also called Platonic solids) for reduced attitudes. According to the symmetries possessed by regular polyhedra, a unified framework is proposed for their formations. Via using the coordinates transformation previously proposed, it is shown that the stability of the desired formations can be provided by stabilizing a constrained nonlinear system. Then, a methodology to investigate the stability of this type of constrained systems is also presented. Paper C considers the problem of tracking and encircling a moving target by agents in 3-dimensional space. By this work, we show that similar design techniques proposed for reduced attitudes formations can also be applied to the formation control for point mass systems. Therein, a group of agents are driven to some desired formation on a spherical surface and simultaneously keep the center of this spherical formation coinciding with the target to be tracked. By properly designing communication topology, the agents constitute a cyclic formation along the equator of an encircling sphere. / <p>QC 20170302</p>

With the recent trend for systems to be more and more autonomous, there is a growing need for cooperative trajectory planning. Applications that can be considered as cooperative systems such as surveying, formation flight, and traffic control need a method that can rapidly produce trajectories while considering all of the constraints on the system. Currently most of the existing methods to handle cooperative control are based around either simple dynamics and/or on the assumption that all vehicles have homogeneous properties. In reality, typical autonomous systems will have heterogeneous, nonlinear dynamics while also being subject to extreme constraints on certain state and control variables. In this thesis, a new approach to the cooperative control problem is presented based on the bio-inspired motion strategy known as local pursuit. In this framework, decision making about the group trajectory and formation are handled at a cooperative level while individual trajectory planning is considered in a local sense. An example is presented for a case of an autonomous farming system (e.g. scouting) utilizing nonlinear vehicles to cooperatively accomplish various farming task with minimal energy consumption or minimum time. The decision making and trajectory generation is handled very quickly while being able to consider changing environments laden with obstacles