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As we continue exploration of the Lunar and Martian surfaces and push farther into unknown and unstructured environments, employing a team of distributed heterogeneous robots will increase the odds of success by enabling more complex task planning that utilizes the complementary capabilities and synergy of the team members. A requirement to reap these potential benefits is effective cooperation and collaboration between the members of a robot team. Defining a metric for effective cooperation is a difficult task that will depend on the composition of the team, the task to be performed, and the working environment. This paper establishes a method for determining the reward criteria (figures of merit) that can be used for training the robot swarm through reinforcement learning techniques. A hierarchical framework of rewards is used which, at the lowest level, measures the success of an individual robot in performing its task. The success of all robots performing different subtasks is then measured using the Quantified Cooperation Assessment (QCA) metric which was introduced in our previous research of multi-robot collaboration. Finally, the mission-level success and overall reward is determined by weighting each task using its priority within the overall mission context. A common reward for each of the robotic teammates is then applied within the learning process, which emphasizes group performance over that of an individual and encourages cooperative behavior. This cooperation framework is trained in a grid-based environment representing an exploration mission on a planetary surface by a heterogenous team of robots consisting of a landing craft, a traditional rover, and several small agile robots. The robots are trained concurrently, but with individual policies developed for each agent resulting in a decentralized control scheme. Once trained, the control policies were evaluated in several test environments consisting of novel terrain maps and regions of interest. An average success rate of 90 % was seen in the test environments demonstrating the robustness of the trained policies. The robots have been trained to not only complete the mission but also perform it in a cooperative manner as well as show the scalable and resilient behavior. • Robotic team trained to locate volatiles in a simulated planetary environment • Cooperation among team members assessed with a quantifiable metric • Success achieved in novel environments not used in training • Decentralized control provided robustness to adding and removing team members [ABSTRACT FROM AUTHOR]

Human-like interactions with robots based on Conversational AI facilitate assistance and teamwork in various contexts. Those interactions are further enhanced by utilizing physical presence and context from the robot's hardware. Robot cooperation is also especially useful, when software or hardware resources have to be shared in a multi-robot system. Therefore, we propose a modular software architecture for multi-robot cooperation that extends the integration of Conversational AI into Socially Assistive Robots, previously suggested by authors. It utilizes a flow-based approach that involves shared repositories and direct or message-driven communication to convey natural language transcriptions among robots in order to support their cooperation. By experiments we evaluated the cooperation between NAOqi based robots and Furhat robot. Our experimental results demonstrate architecture's modularity and adaptability to different cloud services, along with its effectiveness for interactions involving multiple robots. [ABSTRACT FROM AUTHOR]

The paper puts forward an intelligent approach that deals with the computation of an optimal path with collision avoidance for the stick-carrying twin moving from a pre-assumed start position to a predefined goal position. It has been solved through the efficient implementation of modified cuckoo search, sine cosine algorithm, and particle swarm optimization to design a hybrid algorithm aimed at using the communal advantages of the search and position update ability of these algorithms. The benefits are realized by integrating the egg-laying behavior of the cuckoo species to achieve an efficient global search strategy with modified parameters, local search strategy of particle swarm optimization, and greedy approach of sine cosine algorithm. The proposed algorithm is validated using 10 standard benchmark functions, computer simulation using C language, and real robot platform using Epuck robot to illustrate minimal time, shortest distance, collision avoidance, path smoothness, synchronized action, and reduced energy usage in terms of the path traveled, execution time, the number of steps, and the number of turns in the static as well as the dynamic environment. [ABSTRACT FROM AUTHOR]

The research of multiple inspection robots' path simulation planning helps to improve the inspection ability and efficiency of the multi-robot system. This paper studies the problem of cooperative optimization and simulation of multiple robots for multiple inspections in intelligent manufacturing. A dynamic simulation model of the inspection robot is used to construct the state equation of the multirobot inspection simulation system. The square grid is used to decompose the intelligent manufacturing workshop area and simulate the workshop space. With reinforcement learning, a multi-robot patrol simulation system is created for full coverage path simulation planning. The results show the effectiveness of the system for cooperative optimization control and reasonable path planning. [ABSTRACT FROM AUTHOR]

In real-world environments, ranging from urban disastrous scenes to underground mining tunnels, autonomous mobile robots are being deployed in harsh and cluttered environments, having to deal with perception and communication issues that limit their facilitation for data sharing and coordination with other robots. In these scenarios, mobile robot inference can be used to increase spatial awareness and aid decision-making in order to complete tasks such as navigation, exploration, and mapping. This is advantageous as inference enables robots to plan with predicted information that is otherwise unobservable, thus, reducing the replanning efforts of robots by anticipating future states of both the environment and teammates during execution. While detailed reviews have explored the use of inference during human–robot interactions, to-date none have explored mobile robot inference in unknown environments and with cooperative teams. In this survey paper, we present the first extensive investigation of mobile robot inference problems in unknown environments with limited sensor and communication range and propose a new taxonomy to classify the different environment and task inference methods for single- and multi-robot systems. Furthermore, we identify the open research challenges within this emerging field and discuss future research directions to address them. [ABSTRACT FROM AUTHOR]

SLAM (simultaneous localization and mapping) technology has recently shown considerable forward progress; however, most of the mainstream SLAM technologies are currently based on laser- and vision-based fusion strategies. However, there are problems (e.g., a lack of geometric structure, no significant feature points in the surrounding environment, LiDAR degradation, and the longitudinal loss of constraints, as well as missing GPS signals within the pipeline) in special circumstances (e.g., in underground pipelines and tunnels), thus making it difficult to apply laser or vision SLAM techniques. To solve this issue, a multi-robot cooperation-based SLAM method is proposed in this study for pipeline environments, based on LIO-SAM. The proposed method can effectively perform SLAM tasks in spaces with high environmental similarity (e.g., tunnels), thus overcoming the limitation that existing SLAM methods have been poorly applied in pipeline environments due to the high environmental similarity. In this study, the laser-matching part of the LIO-SAM is removed, and a high-precision differential odometer, IMU inertial navigation sensor, and an ultrasonic sensor, which are not easily affected by the high similarity of the environment, are employed as the major sources of positioning information. Moreover, a front-to-back queue of two robots is trained in the pipeline environment; a unique period-creep method has been designed as a cooperation strategy between the two robots, and a multi-robot pose constraint factor (ultrasonic range factor) is built to constrain the robots' relative poses. On that basis, the robot queue can provide a mutual reference when traveling through the pipeline and fulfill its pose correction with high quality, thus achieving high positioning accuracy. To validate the method presented in this study, four experiments were designed, and SLAM testing was performed in common environments, as well as simple and complex urban pipeline environments. Next, error analysis was conducted using EVO. The experimental results suggest that the method proposed in this study is less susceptible to environmental effects than the existing methods due to the benefits of multi-robot cooperation. This applies to a common environment (e.g., a room) and can achieve a good performance; this means that a wide variety of piping environments can be established with high similarity. The average error of SLAM in the pipeline was 0.047 m, and the maximum error was 0.713 m, such that the proposed method shows the advantages of controllable cumulative error, high reliability, and robustness with an increase in the scale of the pipeline and with an extension of the operation time. [ABSTRACT FROM AUTHOR]

Combat with the novel corona virus (COVID-19) has become challenging for all the frontline warriors like, medic people, police and other service provider. Many technology and intelligent algorithms have been developed to set the boundary in its incremental growth. This paper proposed a concept to set the boundary on spreading of this disease among the medic people, who are directly exposed to the COVID-19 patient. To reduce their risk to be infected, we have designed the theoretical model of the medic robot to provide medical services to the confirmed case patient. This paper explains the deployment and execution of assigned work of medic robot for patient carrying, delivering food, medications and handling the emergency health services. The medic robots are divided into various group based on their works. The COVID-19 area is considered as a multi-robot environment, where multiple medic robots will work simultaneously. To achieve the multi-robot cooperation and collision avoidance we have implemented the simplest reinforcement learning approach i.e. the Q-learning approach. We have compared the result with respect to the improved-Q-learning approach. A comparative analysis based on parameters like simplicity, objective, deployed robot category and cooperation has been done with some other approaches mentioned in the literature. For simplicity as well as the time and space complexity purpose the results reveal that Q-learning approach is a better consideration. The proposed approach reduces the mortality rate by 2%. [ABSTRACT FROM AUTHOR]

Traditional multi-agent ${Q}$ -learning (MAQL) induced planning needs to evaluate computationally expensive Nash/correlated equilibrium (CE) at a given joint state during both learning and planning phases. This paper introduces a novel approach to adapt composite rewards of all the agents in one ${Q}$ -table in joint state-action space during learning, and uses these rewards to compute CE in an efficient way during the planning phases. Two schemes of MAQL have been proposed. If mutual cooperation among the agents leads to success of at least one agent and is enough to make the team successful, then scheme-I is employed. However, if an agent’s success is contingent upon other agents’ mutual cooperation as well as simultaneous success of the agents is mandatory then scheme-II is employed. New algorithms for multi-agent learning/planning have been proposed, centering on the said schemes. It is shown that the equilibrium obtained by the proposed algorithms and the traditional correlated ${Q}$ -learning are identical. In order to restrict the exploration within the feasible joint states, constraint versions of the said algorithms are also proposed. An analysis is included to demonstrate the significant saving of computational time and space by the proposed algorithms. In addition, convergence analysis of the proposed algorithms is done. Experiments have been undertaken to validate the performance of the proposed algorithms in multi-robot planning on both simulated and real platforms. [ABSTRACT FROM AUTHOR]

As a crucial component of large aircraft, the assembly efficiency of aircraft skins directly impacts production efficiency. To achieve efficient manufacturing of aircraft skins, this paper proposes a multiple mobile robot control algorithm based on screw theory. The robot arm is integrated into a rail or AGV to increase its motion space, creating a mobile robot assembly system. To address the redundant degrees of freedom problem caused by the mobile manipulator, this paper adopts the screw theory to describe the motion of multiple robots. Furthermore, to ensure the constraint of the motion between multiple robots, this paper proposes a multi-robot control method based on screw constraint. Rigid body constraints are assigned to the end of each mobile manipulator, and the motion is decomposed to the mobile platform and the robot arm. Finally, the cooperative motion control of multiple mobile manipulators is realized. The proposed algorithm is applied in the multi-mobile manipulator cooperative aircraft panel assembly task, achieving efficient assembly of aircraft panel and long truss. [ABSTRACT FROM AUTHOR]

Autonomous mobile robots (AMRs) are increasingly used in modern intralogistics systems as complexity and performance requirements become more stringent. One way to increase performance is to improve the operation and cooperation of multiple robots in their shared environment. The paper addresses these problems with a method for off-line route planning and on-line route execution. In the proposed approach, pre-computation of routes for frequent pick-up and drop-off locations limits the movements of AMRs to avoid conflict situations between them. The paper proposes a reinforcement learning approach where an agent builds the routes on a given layout while being rewarded according to different criteria based on the desired characteristics of the system. The results show that the proposed approach performs better in terms of throughput and reliability than the commonly used shortest-path-based approach for a large number of AMRs operating in the system. The use of the proposed approach is recommended when the need for high throughput requires the operation of a relatively large number of AMRs in relation to the size of the space in which the robots operate.

This paper considers dynamic coverage control of multiple power-constrained agents subject to 3D rigid body kinematics. The agents are deployed to patrol a domain until the entire space has reached a satisfactory level of coverage. This is achieved through the gathering of information by a forward-facing sensor footprint, modelled as an anisotropic spherical sector. Coverage and collision avoidance guarantees are met by a hybrid controller consisting of four operating modes: local coverage, global coverage, waypoint scan and subdomain transfer. Energy-aware methods are encoded into the global coverage state to shift the bulk of spatial redistribution onto less constrained agents. Additionally, a novel domain partitioning strategy is used that directs individual agents to explore within concentric hemispherical shells around a centralized charging station. This results in flight paths that are guaranteed to terminate at the charging station in the limit that agent batteries expire. The efficacy of this algorithm is presented through experimental trials with three agents in an indoor environment. Simulations are provided for ten agents. [ABSTRACT FROM AUTHOR]

Multi-robot task allocation (MRTA) is widely used in various fields and plays an important role in some complex task environments due to its ability to distribute parallel processing tasks. However, the multi-robot cooperative system is susceptible to actual environments or preferences of decision-makers. Therefore, providing enough solutions/schemes in the MRTA is important. To improve the reliability and feasibility of obtained solution set, an improved multimodal multi-objective differential evolution algorithm hybrid with a simulated annealing algorithm (IMMODE-SA) is proposed to solve MRTA problems in this study. In the proposed IMMODE-SA, a novel population initialization method is used to improve the population quality, and a redundant solution deletion method is employed to delete redundant solutions during the search process. Moreover, a simulated annealing algorithm is utilized to improve the exploitation capability in the last generation of evolutionary process. To verify the performance of the proposed algorithm, extensive simulation experiments are conducted on three MRTA instances. Experimental results show that the proposed algorithm performs better than other competitors on MRTA instances in terms of Hypervolume (HV). Also, the validity of the proposed algorithm is demonstrated via three experiments and experimental analysis results indicate that the IMMODE-SA can provide more equivalent optimal schemes to decision makers. Finally, it is crucial to solve MRTA problems with time window constraints. [ABSTRACT FROM AUTHOR]

Hitchhiking is a means of transportation gained by asking other people for a (free) ride. We developed a multi-robot system which is the first of its kind to incorporate hitchhiking in robotics, and discuss its advantages. Our method allows the hitchhiker robot to skip redundant computations in navigation like path planning, localization, obstacle avoidance, and map update by completely relying on the driver robot. This allows the hitchhiker robot, which performs only visual servoing, to save computation while navigating on the common path with the driver robot. The driver robot, in the proposed system performs all the heavy computations in navigation and updates the hitchhiker about the current localized positions and new obstacle positions in the map. The proposed system is robust to recover from 'driver-lost' scenario which occurs due to visual servoing failure. We demonstrate robot hitchhiking in real environments considering factors like service-time and task priority with different start and goal configurations of the driver and hitchhiker robots. We also discuss the admissible characteristics of the hitchhiker, when hitchhiking should be allowed and when not, through experimental results. [ABSTRACT FROM AUTHOR]

Surveillance is an interesting application for Unmanned Aerial Vehicles (UAVs). If a team of UAVs is considered, the objective is usually to act cooperatively to gather as much information as possible from a set of moving targets in the surveillance area. This is a decision-making problem with severe uncertainties involved: relying on imperfect sensors and models, UAVs need to select targets to monitor and determine the best actions to track them. Partially Observable Markov Decision Processes (POMDPs) are quite adequate for optimal decision-making under uncertainties, but they lack scalability in multi-UAV scenarios, becoming tractable only for toy problems. In this paper, we take a step forward to apply POMDP methods in real situations, where the team needs to adapt to the circumstances during the mission and foster cooperation among the team-members. We propose to split the original problem into simpler behaviors that can be modeled by scalable POMDPs. Then, those behaviors are auctioned during the mission among the UAVs, which follow different policies depending on the behavior assigned. We evaluate the performance of our approach with extensive simulations and propose an implementation with real quadcopters in a testbed scenario. [ABSTRACT FROM AUTHOR]

Robot navigation is a complex process that involves real-time localization, obstacle avoidance, map update, control, and path planning. Thus, it is also a computationally expensive process, especially in multi-robot systems. This paper presents a cooperative multi-robot navigation scheme in which a robot can ‘hitchhike’ another robot, i.e., two robots going to the same (or close) destination navigate together in a leader–follower system assisted by visual servoing. Although such cooperative navigation has many benefits compared to traditional approaches with separate navigation, there are many constraints to implementing such a system. A sensor network removes those constraints by enabling multiple robots to communicate with each other to exchange meaningful information such as their respective positions, goal and destination locations, and drastically improves the efficiency of symbiotic multi-robot navigation through hitchhiking. We show that the proposed system enables efficient navigation of multi-robots without loss of information in a sensor network. Efficiency improvements in terms of reduced waiting time of the hitchhiker, not missing potential drivers, best driver-profile match, and velocity tuning are discussed. Novel algorithms for partial hitchhiking, and multi-driver hitchhiking are proposed. A novel case of hitchhiking based simultaneous multi-robot teleoperation by a single operation is also proposed. All the proposed algorithms are verified by experiments in both simulation and real environment.

Realizing long-term autonomous missions involving teams of heterogeneous robots is a challenge. It requires mechanisms to make robots react to disturbances or failures that will arise during the mission, while trying to successfully achieve the mission in cooperation. This paper presents HiDDeN, a distributed deliberative architecture that manages the execution of a hierarchical plan. This plan has initially been computed offline, ensuring some military operational constraints of the mission. Each robot's supervisor then executes its own part of the plan, and reacts to failures using a hierarchical repair approach. This hierarchical repair has been designed with the sake of ensuring operational constraints, while reducing the need of communication between robots, as communication may be intermittent or even nonexistent when the robots operate in completely separate environments. HiDDeN's robustness and scalability is evaluated with simulations. Experiments with an autonomous helicopter and an autonomous underwater vehicle have been realized and are presented as the defining point of our contribution. [ABSTRACT FROM AUTHOR]

Cooperative exploration in unknown environments is fundamentally important in robotics, where the real-time path planning and proper task allocation strategies are the key issues for multi-robot cooperation. In this paper, a PSO-based approach, combined with a fuzzy obstacle avoidance module, is proposed for cooperative robots to accomplish target searching and foraging tasks in unknown environments. The proposed cooperation strategy for a multi-robot system makes use of the potential field function as the fitness function of PSO, while the proposed fuzzy obstacle-avoidance module improves the smoothness of robot trajectory. In the simulation studies, several scenarios with and without the fuzzy module are investigated. The robot trajectory smoothness improvement is demonstrated through the comparative studies. [ABSTRACT FROM AUTHOR]

We propose the IDeM-MRS learning formalism to be used by a group of robots for solving practical tasks in indoor environments. The formalism is inspired on the theory of social learning models for human beings that is traditionally developed in Psychology and Education fields. Our model can be used for coordination of the group, as for, allowing assimilation and accommodation of knowledge through experience exchange. Besides explaining the theoretical model itself, we formalize the mathematics involved with it in a very simple and straightforward fashion. Some issues are especially investigated such as the realistic representation of the multi-robot environment involving the global mission, the tasks belonging to the mission and the active set of robots. A way for task selection is proposed based on social learning theories and approaches that allow cooperative and efficient execution of tasks by robots. To this end, IDeM-MRS can be used in different types of missions varying from simple to complex. Experiments and results validate the efficiency of the formalism compared to a traditional empirical model. [ABSTRACT FROM AUTHOR]

Hitchhiking is a means of transportation gained by asking other people for a (free) ride. We developed a multi-robot system which is the first of its kind to incorporate hitchhiking in robotics, and discuss its advantages. Our method allows the hitchhiker robot to skip redundant computations in navigation like path planning, localization, obstacle avoidance, and map update by completely relying on the driver robot. This allows the hitchhiker robot, which performs only visual servoing, to save computation while navigating on the common path with the driver robot. The driver robot, in the proposed system performs all the heavy computations in navigation and updates the hitchhiker about the current localized positions and new obstacle positions in the map. The proposed system is robust to recover from `driver-lost’ scenario which occurs due to visual servoing failure. We demonstrate robot hitchhiking in real environments considering factors like service-time and task priority with different start and goal configurations of the driver and hitchhiker robots. We also discuss the admissible characteristics of the hitchhiker, when hitchhiking should be allowed and when not, through experimental results.

The use of multiple robots working cooperatively in a redundant way offers new possibilities in the execution of complex tasks in dynamic workspaces. The aim of this work is to increase the range of applicability of teleoperated systems by means of the automatic cooperation of multiple slave robots which, controlled by a human operator, act as if they were a unique robot: a Multi-Robot Cooperation Platform for Task-Oriented Teleoperation, MRCP. From the human operator commands, this robotic platform, the MRCP, dynamically selects the most suitable slave robot and manages, when necessary, a task transfer from one robot to another in order to achieve a smooth execution of teleoperated tasks. The result of the proposed methodology is an improved teleoperated system in terms of reachable workspace (volume, manoeuvrability and accessibility) and dexterity, thus widening its range of applicability. This approach allows human operators to focus their attention on the ongoing task more than on the teleoperated robots. [ABSTRACT FROM AUTHOR]

Technological advances in the robotic and ICT fields represent an effective solution to address specific societal problems to support ageing and independent life. One of the key factors for these technologies is that they have to be socially acceptable and believable to the end-users. This paper aimed to present some technological aspects that have been faced to develop the Robot-Era system, a multi-robotic system that is able to act in a socially believable way in the environments daily inhabited by humans, such as urban areas, buildings and homes. In particular, this paper focuses on two services-shopping delivery and garbage collection-showing preliminary results on experiments conducted with 35 elderly people. The analysis adopts an end-user-oriented perspective, considering some of the main attributes of acceptability: usability, attitude, anxiety, trust and quality of life. [ABSTRACT FROM AUTHOR]

A multi-agent reinforcement learning method based on action prediction of other agents is proposed. In the multi-agent system, the action selection of a learning agent must be effected by other agents' action. Therefore, joint-state and joint-action are involved in reinforcement learning system. The method of agent action prediction based on the probabilistic neural network is proposed. So the joint-action is formed and multi-agent reinforcement learning is implemented. Furthermore, the application of the proposed method in cooperation strategy learning of robot soccer is studied. Through mutually learning with environment, multiple robot system can master behavior strategy and realize multiple robots coordination and cooperation. [ABSTRACT FROM AUTHOR]

This paper presents a market-based approach for multi-robot cooperation. The approach uses auctioneering as the decision-making mechanism for assigning robotic tasks. A task may be carded out either by one robot or cooperatively by multiple robots, depending on the task requirements and the available robotic resources. The system autonomously determines the appropriate number of robots and selects the most suitable robots in the fleet to carry out the task, while accommodating partial or full failure of robots during the task execution, in an unknown, dynamic and unpredictable environment. The system does this efficiently by choosing the candidate robot that best matches the demands of the task. The feasibility of the developed scheme is demonstrated by implementing the approach on a team of simulated mobile robots that transport multiple objects to a goal location. [ABSTRACT FROM AUTHOR]

Target searching in complex unknown environments is a challenging aspect of multi-robot cooperation. In this paper, an improved particle swarm optimisation (PSO) based approach is proposed for a team of mobile robots to cooperatively search for targets in complex unknown environments. The improved cooperation rules for a multi-robot system are applied in the potential field function, which acts as the fitness function of the PSO. The main improvements are the district-difference degree and dynamic parameter tuning. In the simulation studies, various complex situations are investigated and compared to the previous research results. The results demonstrate that the proposed approach can enable the multi-robot system to accomplish the target searching tasks in complex unknown environments. [ABSTRACT FROM AUTHOR]

Planning under uncertainty faces a scalability problem when considering multi-robot teams, as the information space scales exponentially with the number of robots. To address this issue, this paper proposes to decentralize multi-robot partially observable Markov decision processes (POMDPs) while maintaining cooperation between robots by using POMDP policy auctions. Auctions provide a flexible way of coordinating individual policies modeled by POMDPs and have low communication requirements. In addition, communication models in the multi-agent POMDP literature severely mismatch with real inter-robot communication. We address this issue by exploiting a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. The paper presents two different applications: environmental monitoring with unmanned aerial vehicles (UAVs); and cooperative tracking, in which several robots have to jointly track a moving target of interest. The first one is used as a proof of concept and illustrates the proposed ideas through different simulations. The second one adds real multi-robot experiments, showcasing the flexibility and robust coordination that our techniques can provide. [ABSTRACT FROM AUTHOR]

In this paper, an approach is presented to automatically allocate a set of exploration tasks between a fleet of mobile robots. The approach combines a Road-Map technique and Markovian Decision Processes (MDPs). The addressed problem consists of exploring an area where a set of points of interest characterizes the main positions to be visited by the robots. This problem induces a long term horizon motion planning with a combinatorial explosion. The Road-Map allows the robots to represent their spatial knowledge as a graph of way-points connected by paths. It can be modified during the exploration mission requiring the robots to use on-line computations. By decomposing the Road-Map into regions, an MDP allows the current group leader to evaluate the interest of each robot in every single region. Using those values, the leader can assign the exploration tasks to the robots. [ABSTRACT FROM AUTHOR]

In this paper, immune systems and its relationships with multi-robot shepherding problems are discussed. The proposed algorithm is based on immune network theories that have many similarities with the multi-robot systems domain. The underlying immune-inspired cooperative mechanism of the algorithm is simulated and evaluated. The paper also describes a refinement of the memory-based immune network that enhances a robot's action-selection process. A refined model, which is based on the Immune Network T-cell-regulated - with Memory (INT-M) model, is applied to the dog-sheep scenario. The refinements involves the low-level behaviors of the robot dogs, namely shepherds' formation and shepherds' approach. These behaviors would make the shepherds form a line behind the group of sheep and also obey a safety zone of each flock, thus achieving better control of the flock and minimize flock separation occurrences. Simulation experiments are conducted on the Player/Stage robotics platform. [ABSTRACT FROM AUTHOR]

This paper develops a computer vision system for multi-robot cooperation in rescue operations. The work is focused on providing robust and fast vision capabilities to the robots to meet the expected level of performance. Enhanced versions of existing techniques and new techniques are utilized to develop an adaptive vision system architecture for use in an unstructured environment of multi-robot activity in an emergency scenario. Different types of object detection methods are selected in real time in the developed system according to the requirements of a robot. To validate the developed system for use in a multi-robot application, rigorous experiments are conducted in a typical unstructured environment. Features such as invariance of scale, rotation, illumination, and occlusion are tested with different types of objects, for various methods. Generally, good results are obtained. [ABSTRACT FROM AUTHOR]

Network robot system (NRS) is a new concept that integrates physical autonomous robots, environmental sensors, and human-robot interactions through network-based cooperation. The aim of this paper is to provide a ubiquitous and cooperative service framework for NRS. We first present foundational concepts of semantic map and service definition for the framework. Then, in order to generate feasible service configurations to fulfill tasks, we propose service configuration and reconfiguration algorithms, which dynamically search the appropriate service configurations for different tasks. Additionally, we put forward a service reasoning and enabling process to tackle the service unavailable problems. A cost evaluation function for service configuration is also proposed to facilitate the selection of suitable configurations. We tested and evaluated the framework in both simulation system and physical environment. Specifically, by separately varying the parameter settings, system performance was measured in three aspects: the success rate of tasks, the average waiting time per task, and the average cost per task. The experiment results indicate that the versatile service framework provides self-adaptive capability and utilizes available resources efficiently under a range of different scenarios. [ABSTRACT FROM AUTHOR]