Controlling a triangular flexible formation of autonomous agents

In formation control, triangular formations consisting of three autonomous agents serve as a class of benchmarks that can be used to test and compare the performances of different controllers. We present an algorithm that combines the advantages of both position- and distance-based gradient descent control laws. For example, only two pairs of neighboring agents need to be controlled, agents can work in their own local frame of coordinates and the orientation of the formation with respect to a global frame of coordinates is not prescribed. We first present a novel technique based on adding artificial biases to neighboring agents' range sensors such that their eventual positions correspond to a collinear configuration. Right after, a small modification in the bias terms by introducing a prescribed rotation matrix will allow the control of the bearing of the neighboring agents.Comment: 7 pages, accepted in the 20th World Congress of the International Federation of Automatic Control (IFAC

Control of one-dimensional guided formations using coarse information

Motivated by applications in intelligent highway systems, the paper studies the problem of guiding mobile agents in a one-dimensional formation to their desired relative positions. Only coarse information is used which is communicated from a guidance system that monitors in real time the agents' motions. The desired relative positions are defined by the given distance constraints between the agents under which the overall formation is rigid in shape and thus admits locally a unique realization. It is shown that even when the guidance system can only transmit at most four bits of information to each agent, it is still possible to design control laws to guide the agents to their desired positions. We further delineate the thin set of initial conditions for which the proposed control law may fail using the example of a three-agent formation. Tools from non-smooth analysis are utilized for the convergence analysis.Comment: 13 pages, 4 figure

Quantization effects and convergence properties of rigid formation control systems with quantized distance measurements

In this paper, we discuss quantization effects in rigid formation control systems when target formations are described by inter-agent distances. Because of practical sensing and measurement constraints, we consider in this paper distance measurements in their quantized forms. We show that under gradient-based formation control, in the case of uniform quantization, the distance errors converge locally to a bounded set whose size depends on the quantization error, while in the case of logarithmic quantization, all distance errors converge locally to zero. A special quantizer involving the signum function is then considered with which all agents can only measure coarse distances in terms of binary information. In this case, the formation converges locally to a target formation within a finite time. Lastly, we discuss the effect of asymmetric uniform quantization on rigid formation control.Comment: 29 pages, International Journal of Robust and Nonlinear Control 201

Formation Pattern Based on Modified Cell Decomposition Algorithm

The purpose of this paper is to present the shortest path algorithm for Quadrotor to make a formation quickly and avoid obstacles in an unknown area. There are three algorithms proposed in this paper namely fuzzy, cell decomposition, and potential field algorithms. Cell decomposition algorithm is an algorithm derived from graph theory used to create maps of robot formations. Fuzzy algorithm is an artificial intelligence control algorithm used for robot navigation. The merger of these two algorithms are not able to form an optimum formation because some Quadrotors which have been hovering should wait for the other Quadrotors which are unable to find the shortest distance to reach the formation quickly. The problem is that the longer time the multi Quadrotors take to make a formation, the more energy they use. It can be overcome by adding potential field algorithm. The algorithm is used to give values of weight to the path planning taken by the Quadrotors. The proposed algorithms have shown that multi Quadrotors can quickly make a formation because they are able to avoid various obstacles and find the shortest path so that the time required to get to the goal position is fast

Behaviour-based pattern formation in a swarm of anonymous robots

The ability to form patterns is useful to maximize the sensor coverage of a team of robots. Current pattern formation algorithms for multi-robot systems require the robots to be able to uniquely identify each other. This increases the sensory and computational requirements of the individual robots, and reduces the scalability, ro- bustness, and exibility of the pattern formation algorithm. The research presented in this thesis focuses on the development of a novel pattern formation algorithm called the Dynamic Neighbour Selection (DNS) algorithm. The DNS algorithm does not require robots to be uniquely identified to each other, thus improving the scal- ability, robustness, and exibility of the technique. The algorithm was developed in simulation, and demonstrated on a team of vision-enabled Bupimo robots. The Bupimo robots were developed as part of the research reported in this thesis. They are a low-cost, vision enabled, mobile robotic platform intended for use in swarm robotics research and education. Experiments conducted using the DNS algorithm were performed using a computer simulation and in real world trials. The exper- iments conducted via simulation compared the performance of the DNS algorithm to an other similar algorithm when forming a number of patterns. The results of these experiments demonstrate that the DNS algorithm was able to assume the de- sired formation while the robots traversed a shorter distance when compared to the alternative algorithm. The real robot trials had three outcomes. First, they demon- strated the functionality of the Bupimo robots, secondly they were used to develop an effective robot-robot collision avoidance technique, and lastly they demonstrated the performance of the DNS algorithm on real robots

Control and Coordination in a Networked Robotic Platform

Control and Coordination of the robots has been widely researched area among the swarm robotics. Usually these swarms are involved in accomplishing tasks assigned to them either one after another or concurrently. Most of the times, the tasks assigned may not need the entire population of the swarm but a subset of them. In this project, emphasis has been given to determination of such subsets of robots termed as ”flock” whose size actually depends on the complexity of the task. Once the flock is determined from the swarm, leader and follower robots are determined which accomplish the task in a controlled and cooperative fashion. Although the entire control system,which is determined for collision free and coordinated environment, is stable, the results show that both wireless (bluetooth) and internet (UDP) communication system can introduce some lag which can lead robot trajectories to an unexpected set. The reason for this is each robot and a corresponding computer is considered as a complete robot and communication between the robot and the computer and between the computers was inevitable. These problems could easily be solved by integrating a computer on the robot or just add a wifi transmitter/receiver on the robot. On going down the lane, by introducing smarter robots with different kinds of sensors this project could be extended on a large scale for varied heterogenous and homogenous applications

Decentralized receding horizon control of cooperative vehicles with communication delays

This thesis investigates the decentralized receding horizon control (DRHC) for a network of cooperative vehicles where each vehicle in the group plans its future trajectory over a finite prediction horizon time. The vehicles exchange their predicted paths with the neighbouring vehicles through a communication channel in order to maintain the cooperation objectives. In this framework, more frequent communication provides improved performance and stability properties. The main focus of this thesis is on situations where large inter-vehicle communication delays are present. Such large delays may occur due to fault conditions with the communication devices or limited communication bandwidth. Large communication delays can potentially lead to poor performance, unsafe behaviour and even instability for the existing DRHC methods. The main objective of this thesis is to develop new DRHC methods that provide improved performance and stability properties in the presence of large communication delays. Fault conditions are defined and diagnosis algorithms are developed for situations with large communication delays. A fault tolerant DRHC architecture is then proposed which is capable of effectively using the delayed information. The main idea with the proposed approach is to estimate the path of the neighbouring faulty vehicles, when they are unavailable due to large delays, by adding extra decision variables to the cost function. It is demonstrated that this approach can result in significant improvements in performance and stability. Furthermore, the concept of the tube DRHC is proposed to provide the safety of the fleet against collisions during faulty conditions. In this approach, a tube shaped trajectory is assumed in the region around the delayed trajectory of the faulty vehicle instead of a line shaped trajectory. The neighbouring vehicles calculate the tube and are not allowed to enter that region. Feasibility, stability, and performance of the proposed fault tolerant DRHC are also investigated. Finally, a bandwidth allocation algorithm is proposed in order to optimize the communication periods so that the overall teaming performance is optimized. Together, these results form a new and effective framework for decentralized receding horizon control with communication faults and large communication delays

Mission programming for flying ensembles: combining planning with self-organization

The application of autonomous mobile robots can improve many situations of our daily lives. Robots can enhance working conditions, provide innovative techniques for different research disciplines, and support rescue forces in an emergency. In particular, flying robots have already shown their potential in many use-cases when cooperating in ensembles. Exploiting this potential requires sophisticated measures for the goal-oriented, application-specific programming of flying ensembles and the coordinated execution of so defined programs. Because different goals require different robots providing different capabilities, several software approaches emerged recently that focus on specifically designed robots. These approaches often incorporate autonomous planning, scheduling, optimization, and reasoning attributable to classic artificial intelligence. This allows for the goal-oriented instruction of ensembles, but also leads to inefficiencies if ensembles grow large or face uncertainty in the environment. By leaving the detailed planning of executions to individuals and foregoing optimality and goal-orientation, the selforganization paradigm can compensate for these drawbacks by scalability and robustness. In this thesis, we combine the advantageous properties of autonomous planning with that of self-organization in an approach to Mission Programming for Flying Ensembles. Furthermore, we overcome the current way of thinking about how mobile robots should be designed. Rather than assuming fixed-design robots, we assume that robots are modifiable in terms of their hardware at run-time. While using such robots enables their application in many different use cases, it also requires new software approaches for dealing with this flexible design. The contributions of this thesis thus are threefold. First, we provide a layered reference architecture for physically reconfigurable robot ensembles. Second, we provide a solution for programming missions for ensembles consisting of such robots in a goal-oriented fashion that provides measures for instructing individual robots or entire ensembles as desired in the specific use case. Third, we provide multiple self-organization mechanisms to deal with the system’s flexible design while executing such missions. Combining different self-organization mechanisms ensures that ensembles satisfy the static requirements of missions. We provide additional self-organization mechanisms for coordinating the execution in ensembles ensuring they meet the dynamic requirements of a mission. Furthermore, we provide a solution for integrating goal-oriented swarm behavior into missions using a general pattern we have identified for trajectory-modification-based swarm behavior. Using that pattern, we can modify, quantify, and further process the emergent effect of varying swarm behavior in a mission by changing only the parameters of its implementation. We evaluate results theoretically and practically in different case studies by deploying our techniques to simulated and real hardware.Der Einsatz von autonomen mobilen Robotern kann viele Abläufe unseres täglichen Lebens erleichtern. Ihr Einsatz kann Arbeitsbedingungen verbessern, als innovative Technik für verschiedene Forschungsdisziplinen dienen oder Rettungskräfte im Einsatz unterstützen. Insbesondere Flugroboter haben ihr Potenzial bereits in vielerlei Anwendungsfällen gezeigt, gerade wenn mehrere in Ensembles eingesetzt werden. Das Potenzial fliegender Ensembles zielgerichtet und anwendungsspezifisch auszuschöpfen erfordert ausgefeilte Programmiermethoden und Koordinierungsverfahren. Zu diesem Zweck sind zuletzt viele unterschiedliche und auf speziell entwickelte Roboter zugeschnittene Softwareansätze entstanden. Diese verwenden oft klassische Planungs-, Scheduling-, Optimierungs- und Reasoningverfahren. Während dies vor allem den zielgerichteten Einsatz von Ensembles ermöglicht, ist es jedoch auch oft ineffizient, wenn die Ensembles größer oder deren Einsatzumgebungen unsicher werden. Die genannten Nachteile können durch das Paradigma der Selbstorganisation kompensiert werden: Falls Anwendungen nicht zwangsläufig auf Optimalität und strikte Zielorientierung ausgelegt sind, kann so Skalierbarkeit und Robustheit im System erreicht werden. In dieser Arbeit werden die vorteilhaften Eigenschaften klassischer Planungstechniken mit denen der Selbstorganisation in einem Ansatz zur Missionsprogrammierung für fliegende Ensembles kombiniert. In der dafür entwickelten Lösung wird von der aktuell etablierten Ansicht einer unveränderlichen Roboterkonstruktion abgewichen. Stattdessen wird die Hardwarezusammenstellung der Roboter als zur Laufzeit modifizierbar angesehen. Der Einsatz solcher Roboter erfordert neue Softwareansätze um mit genannter Flexibilität umgehen zu können. Die hier vorgestellten Beiträge zu diesem Thema lassen sich in drei Punkten zusammenfassen: Erstens wird eine Schichtenarchitektur als Referenz für physikalisch konfigurierbare Roboterensembles vorgestellt. Zweitens wird eine Lösung zur zielorientierten Missions-Programmierung für derartige Ensembles präsentiert, mit der sowohl einzelne Roboter als auch ganze Ensembles instruiert werden können. Drittens werden mehrere Selbstorganisationsmechanismen vorgestellt, die die autonome Ausführung so erstellter Missionen ermöglichen. Durch die Kombination verschiedener Selbstorganisationsmechanismen wird sichergestellt, dass Ensembles die missionsspezifischen Anforderungen erfüllen. Zusätzliche Selbstorganisationsmechanismen ermöglichen die koordinierte Ausführung der Missionen durch die Ensembles. Darüber hinaus bietet diese Lösung die Möglichkeit der Integration zielorientierten Schwarmverhaltens. Durch ein allgemeines algorithmisches Verfahren für auf Trajektorien-Modifikation basierendes Schwarmverhalten können allein durch die Änderung des Parametersatzes unterschiedliche emergente Effekte in einer Mission erzielt, quantifiziert und weiterverarbeitet werden. Zur theoretischen und praktischen Evaluierung der Ergebnisse dieser Arbeit wurden die vorgestellten Techniken in verschiedenen Fallstudien auf simulierter sowie realer Hardware zum Einsatz gebracht

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described