Multirobot Simultaneous Localization and Mapping Using Manifold Representations

This paper describes a novel representation for two-dimensional maps, and shows how this representation may be applied to the problem of multirobot simultaneous localization and mapping. We are inspired by the notion of a manifold, which takes maps out of the two-dimensional plane and onto a surface embedded in a higher-dimensional space. The key advantage of the manifold representation is self-consistency: when closing loops, manifold maps do not suffer from the "cross over" problem exhibited in planar maps. This self-consistency, in turn, facilitates a number of important capabilities, including autonomous exploration, search, and retro-traverse. It also supports a very robust form of loop closure, in which pairs of robots act collectively to confirm or reject possible correspondence points. In this paper, we develop the basic formalism of the manifold representation, show how this may be applied to the multirobot simultaneous localization and mapping problem, and present experimental results obtained from teams of up to four robots in environments ranging in size from 400 to 900 m/sup 2/.     

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.     

Safe Multirobot Navigation Within Dynamics Constraints

This paper introduces a refinement of the classical sense-plan-act objective maximization method for setting agent goals, a real-time randomized path planner, a bounded acceleration motion control system, and a randomized velocity-space search for collision avoidance of multiple moving robotic agents. We have found this approach to work well for dynamic and unpredictable domains requiring real-time response and flexible coordination of multiple agents. First, the approach employs randomized search for objective maximization and motion planning, allowing real-time or any-time performance. Next, a novel cooperative safety algorithm is employed which respects agent dynamics limitations while also preventing collisions with static obstacles or other participating agents. An implementation of our multilayer approach has been tested and validated on real robots, forming the basis for an autonomous robotic soccer team.     

Market-Based Multirobot Coordination: A Survey and Analysis

Market-based multirobot coordination approaches have received significant attention and are growing in popularity within the robotics research community. They have been successfully implemented in a variety of domains ranging from mapping and exploration to robot soccer. The research literature on market-based approaches to coordination has now reached a critical mass that warrants a survey and analysis. This paper addresses this need for a survey of the relevant literature by providing an introduction to market-based multirobot coordination, a review and analysis of the state of the art in the field, and a discussion of remaining research challenges.     

Cluster Space Specification and Control of Mobile Multirobot Systems

The cluster space state representation of mobile multirobot systems is introduced as a means of enabling enhanced control of mobile multirobot systems. A conceptual framework is proposed for the selection of appropriate cluster space state variables for an n-robot system, the development of formal kinematics that associate the cluster space state variables with robot-specific variables, and the implementation of a cluster space control system architecture. The cluster space approach is then demonstrated for examples of two- and three-robot clusters consisting of differential drive robots operating in a plane. In these examples, we demonstrate cluster space variable selection, review the critical kinematic relationships, and present experimental results that demonstrate the ability of the systems to meet control specifications while allowing a single operator to easily specify and supervise the motion of the clusters.     

Mapping of Discrete Cosine Transforms onto Distributed Hardware Architectures

We present an algorithmically-aware, high-level partitioning methodology for discrete cosine transforms (DCT) targeted to distributed hardware architectures. The methodology relies on the exploration of alternate DCT formulations as part of the partition optimization process. To the best of our knowledge, no previously proposed DCT algorithm exists that is capable of consistently producing alternate regular formulations for an n-size DCT. Hence, a new Cooley-Tukey-like DCT factorization algorithm was developed to allow exploration of alternate formulations as part of the partitioning optimization process. The use of our factorization mechanism along with a greedy strategy to explore the space of equivalent DCT formulations yielded partitioning solutions with as much as 18% reduction in latency and 83% reduction in run-time as compared to previously proposed regular DCT formulations.

Distributed Mapping Management of Identifiers and Locators in LISP-based Mobile Networks

The Locator Identifier Separation Protocol (LISP) has been proposed as an identifier-locator separation scheme for scalable Internet routing. However, LISP was originally designed in the fixed network environment rather than in the mobile network environment. In particular, the existing LISP mobility schemes are based on a centralized map server that is used as an anchor point for mobile nodes, and thus intrinsically subject to some limitations in mobile environment. In this paper, we propose a distributed mapping management of Endpoint Identifiers (EIDs) and Locators (LOCs) in mobile LISP networks. We use Routing LOC (RLOC) and Local LOC (LLOC) as locators for mobile hosts. RLOC represents the IP address of the domain gateway, and LLOC is the IP address of the access router that a host is currently attached to. For EID-LOC mapping management, each network domain has a Distributed Map Server (DMS) over its gateway. Each DMS keeps track of the EID-LOC mapping information for mobile hosts in the distributed way. The proposed scheme is also a network-based approach, in which each access router, instead of a host, performs the mapping management operations. From the performance analysis, we can see that the proposed distributed scheme can give better performance than the existing schemes in terms of the signaling delays required for EID-LOC mapping update and query operations.     

Robust Finite-Time Consensus Tracking Algorithm for Multirobot Systems

This paper studies the finite-time consensus tracking control for multirobot systems. We prove that finite-time consensus tracking of multiagent systems can be achieved on the terminal sliding-mode surface. Also, we show that the proposed error function can be modified to achieve relative state deviation between agents. These results are then applied to the finite-time consensus tracking control of multirobot systems with input disturbances. Simulation results are presented to validate the analysis.     

Incremental Coevolution With Competitive and Cooperative Tasks in a Multirobot Environment

Coevolution has been receiving increased attention as a method for simultaneously developing the control structures of multiple agents. Our ultimate goal is the mutual development of skills through coevolution. The coevolutionary process is, however, often prone to settle into suboptimal strategies. The key to successful coevolution has thus far been unclear. This paper discusses how several robots can emerge cooperative and competitive behavior through coevolutionary processes. In order to realize successful coevolution, we propose two ideas: multiple schedules for incremental evolution and fitness sharing based on the method of importance sampling. To examine this issue, we conducted a series of computer simulations. We have chosen a simplified soccer game consisting of two or three robots as a testbed for analyzing a problem in which both competitive and cooperative tasks are involved. We show that the proposed fitness evaluation allows robots to evolve robust behaviors in cooperative and competitive situations.     

Building Multirobot Coalitions Through Automated Task Solution Synthesis

This paper presents a reasoning system that enables a group of heterogeneous robots to form coalitions to accomplish a multirobot task using tightly coupled sensor sharing. Our approach, which we call ASyMTRe, maps environmental sensors and perceptual and motor control schemas to the required flow of information through the multirobot system, automatically reconfiguring the connections of schemas within and across robots to synthesize valid and efficient multirobot behaviors for accomplishing a multirobot task. We present the centralized anytime ASyMTRe configuration algorithm, proving that the algorithm is correct, and formally addressing issues of completeness and optimality. We then present a distributed version of ASyMTRe, called ASyMTRe-D, which uses communication to enable distributed coalition formation. We validate the centralized approach by applying the ASyMTRe methodology to two application scenarios: multirobot transportation and multirobot box pushing. We then validate the ASyMTRe-D implementation in the multirobot transportation task, illustrating its fault-tolerance capabilities. The advantages of this new approach are that it: 1) enables robots to synthesize new task solutions using fundamentally different combinations of sensors and effectors for different coalition compositions and 2) provides a general mechanism for sharing sensory information across networked robots.     

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.     

A Fast Onboard Relative Positioning Module for Multirobot Systems

We present an onboard robotic module that can determine relative positions among miniature robots. The module uses high-frequency-modulated infrared emissions to enable nearby robots to determine the range, bearing, and message of the sender with a rapid update rate. A carrier sense multiple access protocol is employed for scalable operation. We describe a technique for calculating the range and bearing between robots, which can be generalized for use with more sophisticated relative positioning systems. Using this method, we characterize the accuracy of positioning between robots and identify different sources of imprecision. Finally, the utility of this module is clearly demonstrated with several robotic formation experiments, where precise multirobot formations are maintained throughout difficult maneuvers.     

用于石油物探的分布式光纤声波地震检波器

地震检波器是石油勘探中必不可少的工具,主要被用于地震波采集以获取地层信息,进而实现油藏勘察以及压裂采油技术的监控.研究了分布式光纤声波传感(DAS)系统及其在石油物探中的应用,深入分析了DAS技术的干涉式解调原理、算法和参数测试.此外,还进行了地面物探实验,通过外场实验获取了大量地震波信息,绘制出了清晰的地震剖面图,并...     

Neural-Network-Based Path Planning for a Multirobot System With Moving Obstacles

Recently, a coordinated hybrid agent (CHA) framework was proposed for the control of multiagent systems (MASs). It was demonstrated that an intelligent planner can be designed for the CHA framework to automatically generate desired actions for multiple robots in an MAS. However, in previous studies, only static obstacles in the workspace were considered. In this paper, a neural-network-based approach is proposed for a multirobot system with moving obstacles. A biologically inspired neural-network-based intelligent planner is designed for the coordination of MASs. A landscape of the neural activities for all neurons of a CHA agent contains information about the agent's local goal and moving obstacles. The proposed approach is able to plan the paths for multiple robots while avoiding moving obstacles. The proposed approach is simulated using both MATLAB and Vortex. The Vortex module executes control commands from the control system module, and provides the outputs describing the vehicle state and terrain information, which are, in turn, used in the control module to produce the control commands. Simulation results show that the developed intelligent planner of the CHA framework can control a large complex system so that coordination among agents can be achieved.      

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.     

Guest Editorial Introduction to the Focused Section on Mechatronics in Multirobot Systems

This Focused Section includes ten papers that cover advances in mechatronics that are applicable to multirobot systems. The introduction also includes highlights of related articles published in previous issues of the IEEE/ASME Transactions on Mechatronics.     

How Multirobot Systems Research will Accelerate our Understanding of Social Animal Behavior

Our understanding of social insect behavior has significantly influenced artificial intelligence (AI) and multirobot systems' research (e.g., ant algorithms and swarm robotics). In this work, however, we focus on the opposite question: "How can multirobot systems research contribute to the understanding of social animal behavior?" As we show, we are able to contribute at several levels. First, using algorithms that originated in the robotics community, we can track animals under observation to provide essential quantitative data for animal behavior research. Second, by developing and applying algorithms originating in speech recognition and computer vision, we can automatically label the behavior of animals under observation. In some cases the automatic labeling is more accurate and consistent than manual behavior identification. Our ultimate goal, however, is to automatically create, from observation, executable models of behavior. An executable model is a control program for an agent that can run in simulation (or on a robot). The representation for these executable models is drawn from research in multirobot systems programming. In this paper we present the algorithms we have developed for tracking, recognizing, and learning models of social animal behavior, details of their implementation, and quantitative experimental results using them to study social insects.     

Trellis Exploration Based Hybrid Precoding and Distributed Antenna Array Structures in Millimeter-Wave MIMO Systems

Wireless Personal Communications - This paper concerns a downlink single user millimeter wave MIMO system in which the base station is equipped with distributed antenna arrays. Assuming that the...     

探究信息化测绘环境下无人机摄影测量实践教学体系

无人机摄影测量实践教学工作中应结合信息化测绘环境的特点,构建完善的实践教学体系,培养学生的先进技能、实践操作能力,增强学生的职业能力、专业技能,为其后续的发展夯实基础。