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General detection model in cooperative multirobot localization
Odakura, Valguima Victoria Viana Aguiar;Bianchi, Reinaldo Augusto da Costa;Costa, Anna Helena Reali;
Journal of the Brazilian Computer Society , 2009, DOI: 10.1007/BF03194504
Abstract: the cooperative multirobot localization problem consists in localizing each robot in a group within the same environment, when robots share information in order to improve localization accuracy. it can be achieved when a robot detects and identifies another one, and measures their relative distance. at this moment, both robots can use detection information to update their own poses beliefs. however some other useful information besides single detection between a pair of robots can be used to update robots poses beliefs as: propagation of a single detection for non participants robots, absence of detections and detection involving more than a pair of robots. a general detection model is proposed in order to aggregate all detection information, addressing the problem of updating poses beliefs in all situations depicted. experimental results in simulated environment with groups of robots show that the proposed model improves localization accuracy when compared to conventional single detection multirobot localization.
Adaptive Group Formation in Multirobot Systems  [PDF]
Ahmed Wagdy,Alaa Khamis
Advances in Artificial Intelligence , 2013, DOI: 10.1155/2013/692658
Abstract: Multirobot systems (MRSs) are capable of solving task complexity, increasing performance in terms of maximizing spatial/temporal/radio coverage or minimizing mission completion time. They are also more reliable than single-robot systems as robustness is increased through redundancy. Many applications such as rescue, reconnaissance, and surveillance and communication relaying require the MRS to be able to self-organize the team members in a decentralized way. Group formation is one of the benchmark problems in MRS to study self-organization in these systems. This paper presents a hybrid approach to group formation problem in multi-robot systems. This approach combines the efficiency of the cellular automata as finite state machine, the interconnectivity of the virtual grid and its bonding technique, and last but not least the decentralization of the adaptive dynamic leadership. 1. Introduction Any group of robots in a multirobot system (MRS) moving and coordinating together will always require the ability to quickly change group formation to adapt to the environment. All the robots within this system cooperate with each other to achieve the common goal of having the best group formation with decentralized communication between the robots in that system. This means that each robot has to consider the environmental changes, positions of other robots, and the global goal. The multi-robot systems consist of either homogenous or heterogeneous robots. Homogenous robot system consists of a number of robots with the same properties, capabilities, configuration, and shape. On the other hand, heterogeneous robot system consists of robots that have different capabilities, properties, configuration, and shapes which makes task of implementing an algorithm to control their group formation without a centralized controller/coordinator difficult. Search and destroy, search and rescue, surround and conquer, and many military applications require multi-robot systems that are able to form a group and to adapt robustly. In order to solve the group formation problem in MRS, it is required to (i)model the relationship between robots in the same system, (ii)avoid clashes between robots, obstacles and goal,(iii)build all desired formations,(iv)coordinate the motion of each robot, (v)maintain formation while in motion,(vi)develop an approach that ensures adaptability of the formation.This paper presents a hybrid approach to group formation problem in multi-robot systems. This approach combines the efficiency of the cellular automata as finite state machine, the interconnectivity
Development and Simulation of a Task Assignment Model for Multirobot Systems  [PDF]
International Journal of Engineering , 2007,
Abstract: Multirobot systems (MRS) hold the promise of improved performance and increased fault tolerance for large-scale problems. A robot team can accomplish a given task more quickly than a single agent by executing them concurrently. A team can also make effective use of specialists designed for a single purpose rather than requiring that a single robot be a generalist. Multirobot coordination, however, is a complex problem. An empirical study is described in the present paper that sought general guidelines for task allocation strategies. Different task allocation strategies are identified, and demonstrated in the multi-robot environment. A simulation study of the methodology is carried out in a simulated grid world. The results show that there is no single strategy that produces best performance in all cases, and that the best task allocation strategy changes as a function of the noise in the system. This result is significant, and shows the need for further investigation of task allocation strategies.
An Adaptive -Based Formation Control for Multirobot Systems  [PDF]
Faridoon Shabani,Bijan Ranjbar,Ali Ghadamyari
ISRN Robotics , 2013, DOI: 10.5402/2013/192487
Abstract: We describe a decentralized formation problem for multiple robots, where an formation controller is proposed. The network of dynamic agents with external disturbances and uncertainties are discussed in formation problems. We first describe how to design social potential fields to obtain a formation with the shape of a polygon. Then, we provide a formal proof of the asymptotic stability of the system, based on the definition of a proper Lyapunov function and technique. The advantages of the proposed controller can be listed as robustness to input nonlinearity, external disturbances, and model uncertainties, while applicability on a group of any autonomous systems with -degrees of freedom. Finally, simulation results are demonstrated for a multiagent formation problem of a group of six robots, illustrating the effective attenuation of approximation error and external disturbances, even in the case of agent failure or leader tracking. 1. Introduction All around the world, nature presents examples of collective behavior in groups of insects, birds, and fishes. This behavior has produced sophisticated functions of the group that cannot be achieved by individual members [1, 2]. Therefore, the research on the coordination of robotic swarms has attracted considerable attention. Taking the advantages of distributed sensing and actuation, a robotic swarm can perform some cooperative tasks such as moving a large object that is usually not executable by a single robot [3–7]. Applications about the analysis and design of robotic swarms included autonomous unmanned aerial vehicles, congestion control of communication networks, and distributed sensor networks autonomous, and so forth [1, 2, 8–10]. In general, a robotic formation problem is defined as the organization of a swarm of agents into a particular shape in a 2D or 3D space [8]. This kind of control strategy can be applied into several different fields. For example, in the industrial field, this formation control strategy can be applied to a group of Automated Guided Vehicles (AGVs) moving in a warehouse for goods delivery. The main idea is to make a group of AGVs cooperatively deliver a certain amount of goods, moving in a formation. The creation of a formation with the desired shape is useful to precisely constrain the action zone of the AGVs, thus reducing the chance of collisions with other entities (e.g., human guided vehicles). In the literature, many different approaches to formation control can be found. The main existing approaches can be divided into two categories: centralized [11] and distributed
An Improved Reinforcement Learning Algorithm for Cooperative Behaviors of Mobile Robots  [PDF]
Yong Song,Yibin Li,Xiaoli Wang,Xin Ma,Jiuhong Ruan
Journal of Control Science and Engineering , 2014, DOI: 10.1155/2014/270548
Abstract: Reinforcement learning algorithm for multirobot will become very slow when the number of robots is increasing resulting in an exponential increase of state space. A sequential Q-learning based on knowledge sharing is presented. The rule repository of robots behaviors is firstly initialized in the process of reinforcement learning. Mobile robots obtain present environmental state by sensors. Then the state will be matched to determine if the relevant behavior rule has been stored in the database. If the rule is present, an action will be chosen in accordance with the knowledge and the rules, and the matching weight will be refined. Otherwise the new rule will be appended to the database. The robots learn according to a given sequence and share the behavior database. We examine the algorithm by multirobot following-surrounding behavior, and find that the improved algorithm can effectively accelerate the convergence speed. 1. Introduction In recent years, multirobot systems (MRSs) have received considerable attention because such systems possess some special capabilities such as more flexibility, adaptability, and efficiency in dealing with a complex task [1]. Multirobot learning is the process of acquiring new cooperative behaviors for a particular task by trial and error in the presence of other robots. The desired cooperative behaviors may emerge by local interactions among the robots which are with limited sensing capabilities. Multirobot system can perform more complex tasks via cooperation and coordination [2, 3]. Normally, multirobot learning method can be classified as collective swarm learning and intentionally cooperative learning based on the various levels of explicit communication. The collective swarm systems allow participating robots to learn swarm behaviors with only minimal explicit communication among robots [4, 5]. In these systems a large number of homogeneous mobile robots interact implicitly with each other based on the sharing environment. The robots are organized on the basis of local control laws, such as the stigmergy introduced by Garnier et al. [6]. Stigmergy is a mechanism of indirect interaction mediated by modifications of the sharing environment of agents [7]. The information coming from the local environment can guide the participating individual activity. The complex intelligent behavior emerges at the colony level from the local interactions that take place among individuals exhibiting simple behaviors. At present, the swarm behaviors are often modeled using methods inspired by biology. Along with the advent of
Interacting many-body systems as non-cooperative games  [PDF]
Chiu Fan Lee,Neil F. Johnson
Physics , 2002,
Abstract: We explore the possibility that physical phenomena arising from interacting multi-particle systems, can be usefully interpreted in terms of multi-player games. We show how non-cooperative phenomena can emerge from Ising Hamiltonians, even though the individual spins behave cooperatively. Our findings establish a mapping between two fundamental models from condensed matter physics and game theory.
Cooperative behaviour in complex systems  [PDF]
Márton Karsai
Physics , 2009,
Abstract: In my PhD thesis I studied cooperative phenomena arise in complex systems using the methods of statistical and computational physics. The aim of my work was also to study the critical behaviour of interacting many-body systems during their phase transitions and describe their universal features analytically and by means of numerical calculations. In order to do so I completed studies in four different subjects. My first investigated subject was a study of non-equilibrium phase transitions in weighted scale-free networks. The second problem I examined was the ferromagnetic random bond Potts model with large values of q on evolving scale-free networks which problem is equivalent to an optimal cooperation problem. The third examined problem was related to the large-q sate random bond Potts model also and I examined the critical density of clusters which touched a certain border of a perpendicular strip like geometry and expected to hold analytical forms deduced from conformal invariance. The last investigated problem was a study of the non-equilibrium dynamical behaviour of the antiferromagnetic Ising model on two-dimensional triangular lattice at zero temperature in the absence of external field and at the Kosterlitz-Thouless phase transition point.
A simple proof of monotonicity for linear cooperative systems of ODEs  [PDF]
Janusz Mierczyński
Mathematics , 2013,
Abstract: We present a simple proof of monotonicity for cooperative systems of linear ordinary differential equations, without having recourse to approximation by strongly cooperative systems.
Comparison principle for non - cooperative elliptic systems  [PDF]
Georgi Boyadzhiev
Mathematics , 2007,
Abstract: This paper presents some sufficient conditions for the validity of the comparison principle for the weak solutions of non - cooperative weakly coupled systems of elliptic second-order PDEs.
Mapping Cones are Operator Systems  [PDF]
Nathaniel Johnston,Erling St?rmer
Mathematics , 2011, DOI: 10.1112/blms/bds006
Abstract: We investigate the relationship between mapping cones and matrix ordered *-vector spaces (i.e., abstract operator systems). We show that to every mapping cone there is an associated operator system on the space of n-by-n complex matrices, and furthermore we show that the associated operator system is unique and has a certain homogeneity property. Conversely, we show that the cone of completely positive maps on any operator system with that homogeneity property is a mapping cone. We also consider several related problems, such as characterizing cones that are closed under composition on the right by completely positive maps, and cones that are also semigroups, in terms of operator systems.
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