Affine formation maneuver control of multi-agent systems

A multi-agent formation control task usually consists of two subtasks. The first is to steer the agents to form a desired geometric pattern and the second is to achieve desired collective maneuvers so that the centroid, orientation, scale, and other geometric parameters of the formation can be changed continuously. This paper proposes a novel affine formation maneuver control approach to achieve the two subtasks simultaneously. The proposed approach relies on stress matrices, which can be viewed as generalized graph Laplacian matrices with positive, negative, and zero edge weights. The proposed control laws can track any target formation that is a time-varying affine transformation of a nominal configuration. The centroid, orientation, scales in different directions, and even geometric pattern of the formation can all be changed continuously. The desired formation maneuvers are only known by a small number of agents called leaders, and the rest agents called followers only need to follow the leaders. The proposed control laws are globally stable and do not require global reference frames if the required measurements can be measured in each agent's local reference frame

Heterogeneous robots: Model Predictive Control for bearing-only formation and tracking

openMulti-agent systems are systems composed by more than one autonomous robots which usually work under the assumption that they can communicate sending and receiving positions of other robots that operate in the network. The introduction of this kind of systems is due to the fact that in many situations it is preferable to use more than one robot in order to reach more complex goal without the help of the humans, especially in dangerous situations. In this thesis, the focus is on the heterogeneous robots which are robots whose components are heterogeneous in terms of actuation capabilities, even if it is assumed they can receive bearing information with respect to the other agents in the network. Hence, it is developed an heterogeneous MAS composed by 2 UGVs and 2 UAVs. The goals of the thesis is that the formation has to be maintained and the four agents has also to track a desired trajectory through a leader follower approach based on bearing-only implemented using MPC controllers. The role of the leader is to track the desired trajectory while the followers have to form and maintain the formation also during the tracking. The followers do not know the trajectory to be tracked, nor the distance to the other agents and the leader. The approach is based on decentralized leader follower control with bearing-only. The controllers used are the Model Predictive ones since this type of control allow to prevent the critical situations, solving an online optimization problem at each time instant to select the best control action that drives the predicted output to the reference. The proposed approach is implemented in Matlab and Simulink and the results obtained by the simulations will be discussed.Multi-agent systems are systems composed by more than one autonomous robots which usually work under the assumption that they can communicate sending and receiving positions of other robots that operate in the network. The introduction of this kind of systems is due to the fact that in many situations it is preferable to use more than one robot in order to reach more complex goal without the help of the humans, especially in dangerous situations. In this thesis, the focus is on the heterogeneous robots which are robots whose components are heterogeneous in terms of actuation capabilities, even if it is assumed they can receive bearing information with respect to the other agents in the network. Hence, it is developed an heterogeneous MAS composed by 2 UGVs and 2 UAVs. The goals of the thesis is that the formation has to be maintained and the four agents has also to track a desired trajectory through a leader follower approach based on bearing-only implemented using MPC controllers. The role of the leader is to track the desired trajectory while the followers have to form and maintain the formation also during the tracking. The followers do not know the trajectory to be tracked, nor the distance to the other agents and the leader. The approach is based on decentralized leader follower control with bearing-only. The controllers used are the Model Predictive ones since this type of control allow to prevent the critical situations, solving an online optimization problem at each time instant to select the best control action that drives the predicted output to the reference. The proposed approach is implemented in Matlab and Simulink and the results obtained by the simulations will be discussed

Enclosing a moving target with an optimally rotated and scaled multiagent pattern

We propose a novel control method to enclose a moving target in a two-dimensional setting with a team of agents forming a prescribed geometric pattern. The approach optimises a measure of the overall agent motion costs, via the minimisation of a suitably defined cost function encapsulating the pattern rotation and scaling. We propose two control laws which use global information and make the agents exponentially converge to the prescribed formation with an optimal scale that remains constant, while the team's centroid tracks the target. One control law results in a multiagent pattern that keeps a constant orientation in the workspace; for the other, the pattern rotates with constant speed. These behaviours, whose optimality and steadiness are very relevant for the task addressed, occur independently from the target's velocity. Moreover, the methodology does not require distance measurements, common coordinate references, or communications. We also present formal guarantees of collision avoidance for the proposed approach. Illustrative simulation examples are provided

Bearing-based formation control with second-order agent dynamics

We consider the distributed formation control problem for a network of agents using visual measurements. We propose solutions that are based on bearing (and optionally distance) measurements, and agents with double integrator dynamics. We assume that a subset of the agents can track, in addition to their neighbors, a set of static features in the environment. These features are not considered to be part of the formation, but they are used to asymptotically control the velocity of the agents. We analyze the convergence properties of the proposed protocols analytically and through simulations.Published versionSupporting documentatio

Finite-time bearing-based maneuver of acyclic leader-follower formations

This letter proposes two finite-time bearing-based control laws for acyclic leader-follower formations. The leaders in formation move with a bounded continuous reference velocity and each follower controls its position with regard to three agents in the formation. The first control law uses only bearing vectors, and finite-time convergence is achieved by properly selecting two state-dependent control gains. The second control law requires both bearing vectors and communications between agents. Each agent simultaneously localizes and follows a virtual target. Finite-time convergence of the desired formation under both control laws is proved by mathematical induction and supported by numerical simulations. 10.1109/LCSYS.2021.3088299Comment: Preprint, accepted to L-CS

Robust Distance-Based Formation Control of Multiple Rigid Bodies with Orientation Alignment

This paper addresses the problem of distance- and orientation-based formation control of a class of second-order nonlinear multi-agent systems in 3D space, under static and undirected communication topologies. More specifically, we design a decentralized model-free control protocol in the sense that each agent uses only local information from its neighbors to calculate its own control signal, without incorporating any knowledge of the model nonlinearities and exogenous disturbances. Moreover, the transient and steady state response is solely determined by certain designer-specified performance functions and is fully decoupled by the agents' dynamic model, the control gain selection, the underlying graph topology as well as the initial conditions. Additionally, by introducing certain inter-agent distance constraints, we guarantee collision avoidance and connectivity maintenance between neighboring agents. Finally, simulation results verify the performance of the proposed controllers.Comment: IFAC Word Congress 201

Bearing rigidity theory and its applications for control and estimation of network systems: Life beyond distance rigidity

Distributed control and location estimation of multiagent systems have received tremendous research attention in recent years because of their potential across many application domains [1], [2]. The term agent can represent a sensor, autonomous vehicle, or any general dynamical system. Multiagent systems are attractive because of their robustness against system failure, ability to adapt to dynamic and uncertain environments, and economic advantages compared to the implementation of more expensive monolithic systems

A distributed optimization framework for localization and formation control: applications to vision-based measurements

Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures

Vision-based control of multi-agent systems

Scope and Methodology of Study: Creating systems with multiple autonomous vehicles places severe demands on the design of decision-making supervisors, cooperative control schemes, and communication strategies. In last years, several approaches have been developed in the literature. Most of them solve the vehicle coordination problem assuming some kind of communications between team members. However, communications make the group sensitive to failure and restrict the applicability of the controllers to teams of friendly robots. This dissertation deals with the problem of designing decentralized controllers that use just local sensor information to achieve some group goals.Findings and Conclusions: This dissertation presents a decentralized architecture for vision-based stabilization of unmanned vehicles moving in formation. The architecture consists of two main components: (i) a vision system, and (ii) vision-based control algorithms. The vision system is capable of recognizing and localizing robots. It is a model-based scheme composed of three main components: image acquisition and processing, robot identification, and pose estimation.Using vision information, we address the problem of stabilizing groups of mobile robots in leader- or two leader-follower formations. The strategies use relative pose between a robot and its designated leader or leaders to achieve formation objectives. Several leader-follower formation control algorithms, which ensure asymptotic coordinated motion, are described and compared. Lyapunov's stability theory-based analysis and numerical simulations in a realistic tridimensional environment show the stability properties of the control approaches

Quadcopter drone formation control via onboard visual perception

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