Effects of anisotropic interactions on the structure of animal groups

This paper proposes an agent-based model which reproduces different structures of animal groups. The shape and structure of the group is the effect of simple interaction rules among individuals: each animal deploys itself depending on the position of a limited number of close group mates. The proposed model is shown to produce clustered formations, as well as lines and V-like formations. The key factors which trigger the onset of different patterns are argued to be the relative strength of attraction and repulsion forces and, most important, the anisotropy in their application.Comment: 22 pages, 9 figures. Submitted. v1-v4: revised presentation; extended simulations; included technical results. v5: added a few clarification

Practical Coordination of Multi-Vehicle Systems in Formation

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

Online Flocking Control of UAVs with Mean-Field Approximation

We present a novel approach to the formation controlling of aerial robot swarms that demonstrates the flocking behavior. The proposed method stems from the Unmanned Aerial Vehicle (UAV) dynamics; thus, it prevents any unattainable control inputs from being produced and subsequently leads to feasible trajectories. By modeling the inter-agent relationships using a pairwise energy function, we show that interacting robot swarms constitute a Markov Random Field. Our algorithm builds on the Mean-Field Approximation and incorporates the collective behavioral rules: cohesion, separation, and velocity alignment. We follow a distributed control scheme and show that our method can control a swarm of UAVs to a formation and velocity consensus with real-time collision avoidance. We validate the proposed method with physical and high-fidelity simulation experiments.Comment: To appear in the proceedings of IEEE International Conference on Robotics and Automation (ICRA), 202

Adaptive and Optimal Motion Control of Multi-UAV Systems

This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At fi rst, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixed-wing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations and experiments on a multi-quadrotor UAV system testbed

A Review of Consensus-based Multi-agent UAV Implementations

In this paper, a survey on distributed control applications for multi Unmanned Aerial Vehicles (UAVs) systems is proposed.The focus is on consensus-based control, and both rotary-wing and fixed-wing UAVs are considered. On one side, the latest experimental configurations for the implementation of formation flight are analysed and compared for multirotor UAVs. On the other hand, the control frameworks taking into account the mobility of the fixed-wing UAVs performing target tracking are considered. This approach can be helpful to assess and compare the solutions for practical applications of consensus in UAV swarms

Adaptive Formation Control of Cooperative Multi-Vehicle Systems

The literature comprises many approaches and results for the formation control of multi-vehicle systems; however, the results established for the cases where the vehicles contain parametric uncertainties are limited. Motivated by the need for explicit characterization of the effects of uncertainties on multi-vehicle formation motions, we study distributed adaptive formation control of multi-vehicle systems in this thesis, focusing on different interrelated sub-objectives. We first examine the cohesive motion control problem of minimally persistent formations of autonomous vehicles. Later, we consider parametric uncertainties in vehicle dynamics in such autonomous vehicle formations. Following an indirect adaptive control approach and exploiting the features of the certainty equivalence principle, we propose control laws to solve maneuvering problem of the formations, robust to parametric modeling uncertainties. Next, as a formation acquisition/closing ranks problem, we study the adaptive station keeping problem, which is defined as positioning an autonomous mobile vehicle $A$ inside a multi-vehicle network, having specified distances from the existing vehicles of the network. In this setting, a single-integrator model is assumed for the kinematics for the vehicle $A$, and $A$ is assumed to have access to only its own position and its continuous distance measurements to the vehicles of the network. We partition the problem into two sub-problems; localization of the existing vehicles of the network using range-only measurements and motion control of $A$ to its desired location within the network with respect to other vehicles. We design an indirect adaptive control scheme, provide formal stability and convergence analysis and numerical simulation results, demonstrating the characteristics and performance of the design. Finally, we study re-design of the proposed station keeping scheme for the more challenging case where the vehicle $A$ has non-holonomic motion dynamics and does not have access to its self-location information. Overall, the thesis comprises methods and solutions to four correlated formation control problems in the direction of achieving a unified distributed adaptive formation control framework for multi-vehicle systems

Bio-inspired vision-based leader-follower formation flying in the presence of delays

Flocking starlings at dusk are known for the mesmerizing and intricate shapes they generate, as well as how fluid these shapes change. They seem to do this effortlessly. Real-life vision-based flocking has not been achieved in micro-UAVs (micro Unmanned Aerial Vehicles) to date. Towards this goal, we make three contributions in this paper: (i) we used a computational approach to develop a bio-inspired architecture for vision-based Leader-Follower formation flying on two micro-UAVs. We believe that the minimal computational cost of the resulting algorithm makes it suitable for object detection and tracking during high-speed flocking; (ii) we show that provided delays in the control loop of a micro-UAV are below a critical value, Kalman filter-based estimation algorithms are not required to achieve Leader-Follower formation flying; (iii) unlike previous approaches, we do not use external observers, such as GPS signals or synchronized communication with flock members. These three contributions could be useful in achieving vision-based flocking in GPS-denied environments on computationally-limited agents

Cooperative control for multi-vehicle swarms

The cooperative control of large-scale multi-agent systems has gained a significant interest in recent years from the robotics and control communities for multi-vehicle control. One motivator for the growing interest is the application of spatially and temporally distributed multiple unmanned aerial vehicle (UAV) systems for distributed sensing and collaborative operations. In this research, the multi-vehicle control problem is addressed using a decentralised control system. The work aims to provide a decentralised control framework that synthesises the self-organised and coordinated behaviour of natural swarming systems into cooperative UAV systems. The control system design framework is generalised for application into various other multi-agent systems including cellular robotics, ad-hoc communication networks, and modular smart-structures. The approach involves identifying su itable relationships that describe the behaviour of the UAVs within the swarm and the interactions of these behaviours to produce purposeful high-level actions for system operators. A major focus concerning the research involves the development of suitable analytical tools that decomposes the general swarm behaviours to the local vehicle level. The control problem is approached using two-levels of abstraction; the supervisory level, and the local vehicle level. Geometric control techniques based on differential geometry are used at the supervisory level to reduce the control problem to a small set of permutation and size invariant abstract descriptors. The abstract descriptors provide an open-loop optimal state and control trajectory for the collective swarm and are used to describe the intentions of the vehicles. Decentralised optimal control is implemented at the local vehicle level to synthesise self-organised and cooperative behaviour. A deliberative control scheme is implemented at the local vehicle le vel that demonstrates autonomous, cooperative and optimal behaviour whilst the preserving precision and reliability at the local vehicle level