Adaptive action selection without explicit communication formultirobot box-pushing

The paper describes a novel action selection method for multiple mobile robots box-pushing in a dynamic environment. The robots are designed to need no explicit communication and be adaptive to dynamic environments by changing modules of behavior. The various control methods for a multirobot system have been studied both in centralized and decentralized approaches, however, they needed explicit communication such as a radio, though such communication is expensive and unstable. Furthermore, though it is a significant issue to develop adaptive action selection for a multirobot system to a dynamic environment, few studies have been done on it. Thus, we propose action selection without explicit communication for multirobot box-pushing which changes a suitable behavior set depending on a situation for adaptation to a dynamic environment. First, four situations are defined with two parameters: the existence of other robots and the task difficulty. Next, we propose an architecture of action selection which consists of a situation recognizer and sets of suitable behaviors to the situations and carefully design the suitable behaviors for each of the situations. Using the architecture, a mobile robot recognizes the current situation and activates the suitable behavior set to it. Then it acts with a behavior-based approach using the activated behaviors and can change the current situation when the environment changes. We fully implement our method on four real mobile robots and conduct various experiments in dynamic environments. As a result, we find out our approach is promising for designing adaptive multirobot box-pushing     

Autonomic mobile sensor network with self-coordinated task allocation and execution

This paper describes a distributed layered architecture for resource-constrained multirobot cooperation, which is utilized in autonomic mobile sensor network coverage. In the upper layer, a dynamic task allocation scheme self-organizes the robot coalitions to track efficiently across regions. It uses concepts of ant behavior to self-regulate the regional distributions of robots in proportion to that of the moving targets to be tracked in a nonstationary environment. As a result, the adverse effects of task interference between robots are minimized and network coverage is improved. In the lower task execution layer, the robots use self-organizing neural networks to coordinate their target tracking within a region. Both layers employ self-organization techniques, which exhibit autonomic properties such as self-configuring, self-optimizing, self-healing, and self-protecting. Quantitative comparisons with other tracking strategies such as static sensor placements, potential fields, and auction-based negotiation show that our layered approach can provide better coverage, greater robustness to sensor failures, and greater flexibility to respond to environmental changes.     

Distributed Multirobot Exploration and Mapping

Efficient exploration of unknown environments is a fundamental problem in mobile robotics. We present an approach to distributed multirobot mapping and exploration. Our system enables teams of robots to efficiently explore environments from different, unknown locations. In order to ensure consistency when combining their data into shared maps, the robots actively seek to verify their relative locations. Using shared maps, they coordinate their exploration strategies to maximize the efficiency of exploration. This system was evaluated under extremely realistic real-world conditions. An outside evaluation team found the system to be highly efficient and robust. The maps generated by our approach are consistently more accurate than those generated by manually measuring the locations and extensions of rooms and objects.     

Cooperation Issues and Distributed Sensing for Multirobot Systems

This paper considers the properties a multirobot system should exhibit to perform an assigned task cooperatively. Our experiments regard specifically the domain of RoboCup middle-size league (MSL) competitions. But the illustrated techniques can be usefully applied also to other service robotics fields like, for example, videosurveillance. Two issues are addressed in the paper. The former refers to the problem of dynamic role assignment in a team of robots. The latter concerns the problem of sharing the sensory information to cooperatively track moving objects. Both these problems have been extensively investigated over the past years by the MSL robot teams. In our paper, each individual robot has been designed to become reactively aware of the environment configuration. In addition, a dynamic role assignment policy among teammates is activated, based on the knowledge about the best behavior that the team is able to acquire through the shared sensorial information. We present the successful performance of the Artisti Veneti robot team at the MSL Challenge competitions of RoboCup-2003 to show the effectiveness of our proposed hybrid architecture, as well as some tests run in laboratory to validate the omnidirectional distributed vision system which allows us to share the information gathered by the omnidirectional cameras of our robots.     

Assignment of Dynamically Perceived Tasks by Token Passing in Multirobot Systems

The problem of assigning tasks to a group of robots acting in a dynamic environment is a fundamental issue for a multirobot system (MRS) and several techniques have been studied to address this problem. Such techniques usually rely on the assumption that tasks to be assigned are inserted into the system in a coherent fashion. In this work we consider a scenario where tasks to be accomplished are perceived by the robots during mission execution. This issue has a significative impact on the task allocation process and, at the same time, makes it strictly dependent on perception capabilities of robots. More specifically, we present an asynchronous distributed mechanism based on Token Passing for allocating tasks in a team of robots. We tested and evaluated our approach by means of experiments both in a simulated environment and with real robots; our scenario comprises a set of robots that must cooperatively collect a set of objects scattered in the working environment. Each object collection task requires the cooperation of two robots. The experiments in the simulation environment allowed us to extract quantitative data from several missions and in different operative conditions and to characterize in a statistical way the results of our approach, especially when the team size increases.     

Global localization of multirobot formations using ceiling vision SLAM strategy

Global localization is an important matter in multirobot formations, but the issue has not been sufficiently studied yet. In this paper, we successfully extend the single robot ceiling vision SLAM to multirobot formations for addressing global localization problem. Each robot is equipped with a monocular camera that looks upward to the ceiling. The monocular camera system used for ceiling observation appears to be more convenient than other active sensors such as laser and panoramic camera. A public global map shared by every robot is developed for positioning update. Two global localization strategies are proposed. The first strategy is to globally localize one robot only and then localize the others based on the relative poses amongst the robots. The second strategy is to globally localize all the robots simultaneously. The former requires less computational resource, and the later exhibits better localization performance. A feature-based matching approach is utilized to calculate the relative poses amongst the robots. Simulation experiments are finally performed to demonstrate the effectiveness of the proposed approach.     

The Conro modules for reconfigurable robots

The goal of the Conro Project is to build deployable modular robots that can reconfigure into different shapes such as snakes or hexapods. Each Conro module is, itself, a robot and hence a Conro robot is actually a multirobot system. In this paper we present an overview of the Conro modules, the design approach, an overview of the mechanical and electrical systems and a discussion on size versus power requirement of the module. Each module is self-contained; it has its own processor, power supply, communication system, sensors and actuators. The modules, although self-contained, were designed to work in groups, as part of a large modular robot. We conclude the paper by describing some of the robots that we have built using the Conro modules and describing the miniature custom-made Conro camera as an example of the type of sensors that can be carried as payload by these robots.     

Merging Occupancy Grid Maps From Multiple Robots

Mapping can potentially be speeded up in a significant way by using multiple robots exploring different parts of the environment. But the core question of multirobot mapping is how to integrate the data of the different robots into a single global map. A significant amount of research exists in the area of multirobot mapping that deals with techniques to estimate the relative robots poses at the start or during the mapping process. With map merging, the robots in contrast individually build local maps without any knowledge about their relative positions. The goal is then to identify regions of overlap at which the local maps can be joined together. A concrete approach to this idea is presented in form of a special similarity metric and a stochastic search algorithm. Given two maps m and m', the search algorithm transforms m' by rotations and translations to find a maximum overlap between m and m'. In doing so, the heuristic similarity metric guides the search algorithm toward optimal solutions. Results from experiments with up to six robots are presented based on simulated as well as real-world map data.     

递归时空信息融合技术及其在毫米波与红外目标识别中的应用

本文使用Dempster-Shafer技术讨论了递归时空信息融合的集中(或分配)算法。与Bayes算法相比,Dempster-Shafer技术具有较强的处理信息的不确定性的能力。集中算法是将所有信息汇集于中心处理器中进行处理;而分配算法则是依靠各分散的分处理器分担运算量,这样可增加计算能力。改进的算法可有效地应用于采用两种探测器的目标识别:毫米波辐射计、红外搜索和跟踪探测器。     

Controlling mobile robots in distributed intelligent sensor network

Mobile robots need sufficient sensors and information on the environment in order to navigate. In this paper, we propose a system of mobile robots, which is controlled in a distributed intelligent sensor network. In such a networked space, the environment is divided by distributed sensors. Each area is monitored by a distributed sensor device, which connects with other distributed sensor devices and robots throughout the network. As a result, the mobile robots are able to accomplish tasks simply by following orders from the sensor devices in the networked environment, although the mobile robots are not self-contained with information on the environment and sensors for self-positioning and control. We test several situations to verify the proposed system.     

Foresighted tree configuration games in resource constrained distributed stream mining sensors

We consider the problem of optimizing stream mining applications that are constructed as tree topologies of classifiers and deployed on a set of resource constrained and distributed processing nodes (or sensors). The optimization involves selecting appropriate false-alarm detection tradeoffs (operating points) for each classifier to minimize an end-to-end misclassification penalty, while satisfying resource constraints. We design distributed solutions, by defining tree configuration games, where individual classifiers configure themselves to maximize an appropriate local utility. We define the local utility functions and determine the information that needs to be exchanged across classifiers in order to design the distributed solutions. We analytically show that there is a unique pure strategy Nash equilibrium in operating points, which guarantees convergence of the proposed approach. We develop both myopic strategy, where the utility is purely local to the current classifier, and foresighted strategy, where the utility includes impact of classifier’s actions on successive classifiers. We analytically show that actions determined based on foresighted strategies improve the end-to-end performance of the classifier tree, by deriving an associated probability bound. We also investigate the impact of resource constraints on the classifier action selections for each strategy, and the corresponding application performance. We propose a learning-based approach, which enables each classifier to effectively adapt to the dynamic changes of resource constraints. We evaluate the performance of our solutions on an application for sports scene classification. We show that foresighted strategies result in better performance than myopic strategies in both resource unconstrained and resource constrained scenarios, and asymptotically approach the centralized optimal solution. We also show that the proposed distributed solutions outperform the centralized solution based on the Sequential Quadratic Programming on average in resource unconstrained scenarios.     

A Novel Distributed Sensor Fusion Algorithm for RSSI-Based Location Estimation Using the Unscented Kalman Filter

We address the Bayesian sensor fusion approach for distributed location estimation in the wireless sensor network. Assume each sensor transmits local calculation of target position to a fusion center, which then generates under a Bayesian framework the final estimated trajectory. We study received signal strength indication-based approach using the unscented Kalman filter for each sensor to compute local estimation, and propose a novel distributed algorithm which combines the soft outputs sent from selected sensors and computes the approximated Bayesian estimates to the true position. Simulation results demonstrate that the proposed soft combining method can achieve similar tracking performance as the centralized data fusion approach. The computational cost of the proposed algorithm is less than the centralized method especially in large scale sensor networks. In addition, it is straightforward to incorporate the proposed soft combining strategy with other Bayesian filters for the general purpose of data fusion.

     

Fair Coalitions for Power-Aware Routing in Wireless Networks

Several power-aware routing schemes have been developed for wireless networks under the assumption that nodes are willing to sacrifice their power reserves in the interest of the network as a whole. But, in several applications of practical utility, nodes are organized in groups, and as a result, a node is willing to sacrifice in the interest of other nodes in its group but not necessarily for nodes outside its group. Such groups arise naturally as sets of nodes associated with a single owner or task. We consider the premise that groups will share resources with other groups only if each group experiences a reduction in power consumption. Then, the groups may form a coalition in which they route each other's packets. We demonstrate that sharing between groups has different properties from sharing between individuals and investigate fair, mutually beneficial sharing between groups. In particular, we propose a Pareto-efficient condition for group sharing based on max-min fairness called fair coalition routing. We propose distributed algorithms for computing the fair coalition routing. Using these algorithms, we demonstrate that fair coalition routing allows different groups to mutually beneficially share their resources     

Task coalition formation and self‐adjustment in the wireless sensor networks

Coalition is an essential mechanism in the multi‐agent systems in the research of task‐oriented area. Self‐interested agents coordinate their behaviors in a coalition to pursue a common goal and obtain payoffs. We propose the clustering‐based coalition formation and self‐adjustment mechanisms for tasks in the wireless sensor network. Before coalition formation, the management center clusters attributes of sensors to reduce the scale of searching space during coalition formation. And then an improved MAX–MIN ant colony optimization algorithm is adopted to resolve the problem of coalition formation. If a coalition member fails to fulfill a task, it can sponsor a negotiation with some noncoalition nodes to execute coalition self‐repairing autonomously. The stimulus‐response mechanism of wasp colony is introduced to determine the probability of response to the task invitation to avoid consuming extra energy. Simulation results show that our model efficiently reduces energy consumption and network traffic, decreases the number of dead nodes, and prolongs the lifetime of the networks. Copyright © 2012 John Wiley & Sons, Ltd.     

Joint Subchannel,Rate and Power Allocation in OFDMA-Based Cognitive Wireless Mesh Network

In this paper, the problem of resource allocation in an Orthogonal Frequency Division Multiple Access-based Cognitive Wireless Mesh Network (CWMN) is addressed. The objective is to maximize the total utilities in a CWMN, which is defined as any increasing, concave and twice differentiable function of the end-to-end flow rate, by jointly allocating each link’s rate, power and subchannels under the constraints of multiple primary users’ Interference Temperature and multiple access interference. First, a centralized resource allocation algorithm is developed based on the Column Generation approach, and shown to be optimal. So it can perform as a criterion for designing other algorithms. Secondly, considering the applicability of algorithm in distributed system, a near-optimal distributed algorithm is proposed, which allocates subchannel based on routing information at first, and then jointly allocates the resource of rate and power. Finally, the simulation results validate the centralized and distributed algorithms, and show that better performance can be achieved than the conventional algorithm.     

Data fusion in ubiquitous networked robot systems for urban services

There is a clear trend in the use of robots to accomplish services that can help humans. In this paper, robots acting in urban environments are considered for the task of person guiding. Nowadays, it is common to have ubiquitous sensors integrated within the buildings, such as camera networks, and wireless communications like 3G or WiFi. Such infrastructure can be directly used by robotic platforms. The paper shows how combining the information from the robots and the sensors allows tracking failures to be overcome, by being more robust under occlusion, clutter, and lighting changes. The paper describes the algorithms for tracking with a set of fixed surveillance cameras and the algorithms for position tracking using the signal strength received by a wireless sensor network (WSN). Moreover, an algorithm to obtain estimations on the positions of people from cameras on board robots is described. The estimate from all these sources are then combined using a decentralized data fusion algorithm to provide an increase in performance. This scheme is scalable and can handle communication latencies and failures. We present results of the system operating in real time on a large outdoor environment, including 22 nonoverlapping cameras, WSN, and several robots.     

Distributed supervisory control for multiple robot autonomous navigation performing single-robot tasks

This paper proposes two new approaches based on the Supervisory Control Theory (SCT) of Discrete Event Systems (DES) for autonomous navigation of multiple robots with single-robot tasks being assigned by a centralized scheduler. The two planning strategies differ regarding their implementation, which can be centralized or distributed. Nevertheless, both approaches share the central objective of considering the scheduler and robots as DES, allowing the use of SCT to model and control the behavior of the whole multi-robotic system. Particularly, SCT is used to gather deliberative and reactive motion planning algorithms through a structured procedure, aiming to safely navigate in cluttered, dynamic, partially-known environments. The open-loop behavior of the autonomous navigation system is modeled for both approaches. Moreover, standard supervisors are obtained for the centralized planning approach. In addition, the supervisor localization method is considered to allow a distributed planning approach. Furthermore, as a case study, real experiments considering mobile robots are carried out to corroborate the proposed framework.     

Multiple Traveling Robot Problem: A Solution Based on Dynamic Task Selection and Robust Execution

The multiple traveling robot problem (MTRP), the real-world version of the well-known NP-hard multiple traveling salesman problem (MTSP), asks for finding routes of robots to visit a set of targets. Various objectives may be defined for this problem (e.g., minimization of total path length, time, etc.). The overall solution quality is dependent on both the quality of the solution constructed by the paths of robots and the efficient allocation of the targets to robots. Unpredictability of the exact processing times of tasks, unstable cost values during execution, and inconsistencies due to uncertain information further complicate MTRP. This paper presents a multirobot cooperation framework employing a dynamic task selection scheme to solve MTRP. The proposed framework carries out an incremental task allocation method that dynamically adapts to current conditions of the environment, thus handling diverse contingencies. Globally efficient solutions are obtained through mechanisms that result in the allocation of the most suitable tasks from dynamically generated priority-based rough schedules. Since the presented approach is for real-world task execution, computational requirements are kept at a minimum, and the framework is designed to be applicable on real robots even with limited capabilities. The efficiency and the robustness of the proposed scheme is evaluated through experiments both in simulations and on real robots.     

Manipulating rigid payloads with multiple robots using compliantgrippers

This paper describes an approach to nonmodel-based decentralized controls of multirobot systems utilizing structural flexibility in gripper design to avoid large unwanted internal forces acting on multirobot systems. It is proven in theory that a simple proportional and derivative (PD) position feedback plus gravity compensation controller can regulate the desired position/orientation of a payload manipulated by multiple robots with compliant grippers and simultaneously damp vibrations of compliant grippers. By adding a force feedforward control to the PD scheme, a hybrid position/force control scheme is further developed to control internal forces between robots and the payload in the particular directions, in the event that the compliance of grippers is low or negligible in these directions. Experiments conducted with two CRS A460 industrial robots manipulating a beam, using a rigid and a compliant gripper, confirm these theoretical predictions     

An Integrated Approach for Achieving Multirobot Task Formations

In this paper, a problem, called the initial formation problem, within the multirobot task allocation domain is addressed. This problem consists in deciding which robot should go to each of the positions of the formation in order to minimize an objective. Two different distributed algorithms that solve this problem are explained. The second algorithm presents a novel approach that uses cost means to model the cost distribution and improves the performance of the task allocation algorithm. Also, we present an approach that integrates distributed task allocation algorithms with a behavior-based architecture to control formations of robot teams. Finally, simulations and real experiments are used to analyze the formation behavior and provide performance metrics associated with implementation in realistic scenarios.