Generalized Coordination of Multi-robot Systems

Area coverage is a well-known problem in multi robotic systems, and it is a typical requirement in various real-world applications. A common and popular approach in the robotic community is to use explicit forms of communication for task allocation and coordination. These approaches are susceptible to the loss of communication signal, and costly with high computational complexity. There are very few approaches which are focused on implicit forms of communication. In these approaches, robots rely only on their local information for task allocation and coordination. In this paper, a cooperative strategy is proposed by which a team of robots perform spraying a large field. The focus of this paper is to achieve task allocation and coordination using only the robots’ local information.

The coordination of multi-robot systems is becoming one of the most important areas of research in robotics, mostly because it is required by numerous complex applications. These applications range from intelligent transportation systems, search and rescue robots, and medical robots, to cosmology and astrophysics. The coordination of multi-robot systems is based upon cooperation. The actions performed by each robot take into account the actions executed by the others in such a way that the whole system can operate coherently and efficiently. Regardless of the application, coordination is the key to the successful design and implementation of multi-robot systems. The number of robots involved in the aforementioned applications is increasing along with advances in miniaturization and automation. Consequently, a large number of robots need to share a workspace. This crowded workspace introduces new challenges into the coordination problem by increasing the risk of collision. To take into account communication constraints and sensor ranges, robots rely on local information. Therefore, efficient but simple coordination algorithms are required. This thesis investigates decentralized approaches based on navigation functions for the coordination of multi-robot systems in crowded workspaces. Decentralization allows robots to rely on local information, guarantees scalability and enables real-time deployment. Navigation functions are a special category of potential functions. Their negated gradient vector-field is attractive towards the goal and repulsive with respect to fixed or moving obstacles to avoid collision. In the first part of the thesis, we present the multi-robot coordination problem using navigation functions in a game-theory based framework. We propose a motion model along with a control law that leads the robots to a Nash equilibrium. The existence of the Nash equilibrium enables navigation functions to be exploited for studying, building, and running coordination frameworks for multi-robot systems. In the second part, we address the coordination of autonomous vehicles at intersections. A novel decentralized navigation function is proposed. It guarantees collision-free crossing of autonomous vehicles modeled as first order dynamic systems. The inertia of the vehicles is also introduced in the navigation functions to ensure deadlock-free coordination. The proposed approach does not require adaptation of the road infrastructure and relies upon onboard vehicles sensor data. Compared with traffic lights and roundabouts, the proposed method significantly reduces the travel time and the number of stops, thus decreasing energy consumption and pollution emission. This provides a strong motivation to pursue efforts towards the deployment of autonomous vehicles on roads. In the third part of the thesis, we investigate a coordination framework for a large number of miniaturized fiber positioner robots. The fiber positioner robots are designed and built as parts of the next generation of telescopes enabling large spectroscopic surveys. The proposed decentralized framework ensures the collision-free coordination of the fiber positioners sharing a crowed workspace at the focal plate of the telescope. The dynamical (max speed) and the mechanical (limited actuation range) constraints of the positioners are taken into account in the proposed coordination approach, which significantly reduces the time to reach a new robot configuration.

The uniting idea of both parallel computing and multi-robot systems is that having multiple processors or robots working on a task decreases the processing time. Typically we desire a linear speedup, that is, doubling the number of processing units halves the execution time. Sometimes superlinear scalability is observed in parallel computing systems and more frequently in multi-robot and swarm systems. Superlinearity means each individual processing unit gets more efficient by increasing the system size—a desired and rather counterintuitive phenomenon.

The multi-robot system is composed of a collection of two or more autonomous robots. The multi-robot system aims to create robust, flexible, and scalable approaches to collaborate with all robots. In multi-robot systems, robots work together to achieve several specific tasks. Although multi-robot systems provide wide applications such as path planning, cooperative localization, and dynamic task allocation, the research in the multi-robot system field is still developing. This paper analyzes previous research on multi-robot systems and divides multi-robot systems into two taxonomies. In the first taxonomy, we review the centralized multi-robot system. In the second taxonomy, we review the distributed multi-robot system. Then we analyze two typical algorithms (ant colony algorithm and particle swarm algorithm) applied to distributed multi-robot systems and compare the centralized systems with distributed systems in terms of characteristics and applications. We conclude by discussing the current limits and future research directions in the multi-robot system.

This chapter describes the exploration of unknown environment using multiple robots. We designed this system in order to overcome the various disadvantages of exploration and mapping robots. When it comes to the mapping of large unknown environment, single robot would be inefficient and inaccurate. It leads to more exploration time and inaccurate mapping of the environment. There are multiple algorithms which already exist and are used but seeing their inefficiency we have worked on RRT that is random-exploration random tree. Multiple robots will be deployed in an unknown environment and based on which the robots will explore environment, to make robot communicate with each other we have implemented swarm algorithm so that robot communicate with each other in coordinated way. The Swarm algorithm will help robots explore the environment in a coordinated way because of which the two robots will never explore the same area repeatedly and hence will same time and will lead to more accurate mapping of the environment. To implement this solution, we used ROS as the middle ware, with the help of the ROS we can encounter and handle many real time parameters which will make our system more accurate and efficient. GAZEBO simulator is used in order to test and implement the system. This multi robot system will be very useful in the future smart city ecosystems.KeywordsMultiple robotsExplorationRRTRandom-explorationSwarmMiddle-ware,ROSGAZEBO

This paper presents a kind of kinematics arithmetic of a multi-robot coordination system, facing to manipulation of huge elliptical ship module. This multi-robot system can adjust the position and pose of the elliptical workpiece in 6 DOF, by the coordination of multiple 3 DOF robots. Based on the motion strategy, the inverse kinematics arithmetic was given, and also a simplified arithmetic applied to real time control. A numerical example illuminates the feasibility and the precision.

Multi-robot systems gain considerable attention due to lower cost, better robustness, and higher scalability as compared with single-robot systems. Cooperative object transport, as a well-known use case of multi-robot systems, shows great potential in real-world applications. The design and implementation of a multi-robot system involve many technologies, specifically, communication, coordination, task allocation methods, experimental platforms, and simulators. However, most of recent multi-robot system studies focus on coordination and task allocation problems, with little focus on communications among multiple robots. In this review, we focus on the communication, validation platform, and simulator of multi-robot systems, and discuss one of the important applications, cooperative object transport. First, we study the multi-robot system fundamentals and comprehensively review the multi-robot system communication technologies. Then, the multi-robot system validating platform, testbed, simulator, and middleware used in academia and industry are investigated. Finally, we discuss recent advances in cooperative object transport, and challenges and possible future research directions for multi-robot systems.

The involvement of Multi-robot Systems (MRS) in a vast range of missions such as search and rescue, patrolling, exploration and mapping of environments has increased over the years. The working dynamics between MRS and human operators has also evolved to include more human-in-the-loop approaches where the MRS becomes an extension of the human operator(s) to accomplish complex tasks as human-robot teams. This advancement requires a higher frequency of information sharing among the MRS and with the human operator(s) to provide situational awareness so that more informed decisions can be made rapidly. Thus, maintaining communication between the various actors of the team is key to ensure mission success. This paper explores the dynamic challenge of swarm reconfiguration to maintain communication among a system of dynamic nodes — i.e. multiple robots or humans — navigating in an urban environment. Proposed is a hierarchical robotic system that utilises a swarm of Small Supporting Unmanned Ground Vehicles (SSUGVs) as mobile communication breadcrumbs. These SSUGVs dynamically reconfigure themselves through swarm-inspired, path-constrained motion planning to maintain communication within the main system during exploration. The presented solution is verified successfully with hybrid simulations — i.e. a combination of real experimental data with simulated components on a mapping exploration task. The system is studied based on three metrics namely the system connectivity that measures the network connectivity of the overall system, the system response that measures the response of the system to a changing environment, and the average distance travelled of the system that indicates the activity level of the system.

With the increasing number of robots in industrial environments, scientists/technologists were often faced with issues on cooperation, coordination and collaboration among different robots and their self governance in the work space. This has led to the development of systems with several cooperative robotic agents. (Kim et al., 1997b). Generally, a system with several robotic agents (multi-robot system) is composed by two or more robots executing a task in a cooperative way (Arai and Ota, 1992). Coordination, collaboration and cooperation are three terms used without distinction when working with multi-agent and multi-robot systems. In this work, we adopt a definition proposed by Noreils (Noreils, 1993) in which cooperation occurs when several agents (or robots) are gathered together so as to perform a global task. Coordination and collaboration are two forms of cooperation (Botelho and Alami, 2000). Coordination occurs when an entity coordinates its activity with another, or it synchronizes its action with respect to the other entity, by exchanging information, signals, etc. And, collaboration occurs when two or more agents decompose a global task in subtasks to be performed by each specific agent. Generally, the solution for problems using multi-agent and multi-robot systems is divided into stages. When talking about autonomous robots, two of these stages are the task allocation stage and the task execution stage. Task allocation should be done so that all components (agents or robots) in the system are used and the problem is completely solved. The task execution stage itself should be performed so that the agents do not interfere to each other (coordination and/or collaboration) when solving the problem. Traditionally, both stages are carried out independently. In the task allocation stage, it is defined if the agents will collaborate to each other or if they will coordinate their activities. In the task execution stage, collaboration and/or coordination are effectively done. In the literature, each stage is implemented using different techniques. The task allocation stage can be implemented using centralized or decentralized approaches (Le Pape, 1990). Centralized approaches can be implemented as an optimization problem. Decentralized approaches generally use marked based approaches like the contract-net protocol (CNP) (Ulam et al., 2007) or other approaches derived from it (Botelho and Alami, 1999). The task execution stage can be implemented in many ways. It depends of the nature of interactions between agents (Collaboration or coordination) and if agents can modify or not their strategies (Static and dynamic strategies). For example it can be implemented using

Purpose Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements. Design/methodology/approach A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots. Findings The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system. Originality/value Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.

Robot mapping and exploration is basic to many robotic applications such as search and rescue operations in disaster scenarios, warehouse management, service robotics, patrolling and autonomous driving. With recent advances in robot navigation and sensor compactness, single robot systems can accurately model the environment and perform complex autonomous navigation tasks. On the other hand, multi-robot systems can speed up mapping and exploration tasks in emergency situations, such as rescue missions, by making use of distributed sensors, thereby increasing the range of exploration tasks to an extent that is not possible with a single robot. Each robot explores and maps different areas of the same environment that are finally merged and connected to make a global map. To build a map of an unknown environment, each robot must perform SLAM, or Simultaneous Localization and Mapping. A big challenge with a multi-robot SLAM system is the transfer of shared map information between multiple robots. There is a possibility of transferring individual measurement errors to the global map, resulting in excess computation and memory required to store such maps. To overcome this problem, we propose to use topological feature map representation that can store information into nodes and edges and does not have any large memory requirements. We present a combined metric-topological mapping approach to multi-robot SLAM. This method maintains a topological pose graph with sensor information stored in nodes and edges that can be optimized globally with reduced information sharing. By combining local metric and topological maps built by individual robots, the reduced graph structure can be merged and extended to map large areas effectively. To robustly merge local maps into global one, we used visual features from each robot that are matched in a distributed system. The graph node-edge structure is used for path planning and navigation. At the same time information sharing between robots results in optimized task distribution between multi-robots.

Using a local area network (LAN) to coordinate multiple industrial robots in a multirobot system (workcell) is studied. The properties of the multiple robot coordination task are first discussed. Then, a method is proposed to evaluate a pre-defined task decomposition and allocation scheme for the coordination task. In this method, a parameter called effective token passing ratio (ETPR) is defined to measure how efficient the scheme is when the token bus LAN is used as the communication medium. A lower bound of the ETPR is analytically determined based on time constraints imposed on the task. By using this method, one does not need to determine the communication delay and computation time of individual tasks (or subtasks) decomposed from the coordination task. Instead, one can compare the ETPR of the network with the lower bound. From the comparison, it can be determined whether the task decomposition and allocation scheme is appropriate. The probability that real-time coordination is successful is also computed. >

This paper presents task coordination scheme for multiple mobile robots in indoor environment. Multi-robot systems are gaining a lot of popularity for different applications in indoor and outdoor environments and for executing various tasks in different scenarios. The advantage with multi-robot systems is efficiency by which a task can be completed as compared to single robot system thus allowing more autonomy to the task at hand. In this paper, we present novel techniques for multi-robot system operating in indoor office like environment using semantic mapping. A mapping and navigation algorithm is discussed that uses topological and metric maps. We demonstrate through simulations and experiments, the coordination strategy and discuss the results.

Efficient task co-ordination is an important problem in multirobot systems. The explicit programming of each robot to perform specific tasks (e.g., cleaning) is too cumbersome and inefficient, as the areas to serve in a map may vary with time. Moreover, the number of available robots to serve may also vary, as some of the robots may be charging and not available. Improper task division can cause two or more robots to serve the same areas of the map, which is a waste of computation and resources. Hence, there is a need for a simpler scheme for the autonomous task co-ordination of multiple robots without the need for explicit programming. This paper presents a bioinspired algorithm that uses the attractive and repelling behavior of pheromones for autonomous task co-ordination. The proposed algorithm uses a node representation of navigational paths for autonomous exploration. This repelling mechanism also allows robots to capture areas or subareas of the map so that there is efficient task co-ordination, and robots work without interruption from other robots. We show through experiments that the proposed scheme enables multiple service robots to intelligently perform co-operative tasks without any explicit programming or commands.

In this paper, we explore the problem of rendezvous of synchronous multi-robot systems. Each robot has its own unique, bounded yet controllable sensing range which can be adjusted. The state of those robots within the sensing range can be estimated, which induces the directed network topology of the multi-robot systems. In particular, we consider multi-robot systems containing faulty robots which can behave arbitrarily. Our recent work [1] has addressed the problem of achieving rendezvous in the presence of faulty robots under a restrictive class of conditions on network topology. In this work, we presented a theoretically correct, but computationally intractable algorithm. We extend our past work by proposing a new approximate algorithm that is computationally efficient, and by showing that the proposed algorithm solves our problem given faulty robots in general configurations under mild assumptions on the network topology. Thus, the main contribution of this paper is to provide an efficient computational framework and analysis of robust rendezvous algorithm in the presence of faulty robots. Several simulation results are provided to demonstrate that our algorithm performs well in the face of both stationary and dynamic faults.