Development of the Multiple Robot Fish Cooperation System

The research on coordination of multiple biomimetic robot fish is focused on basic theory and key technology for systematic control and cooperation of future multiple underwater robots. By employing the behavior-based approach presented in this paper, many coordinate tasks can be achieved efficiently. Taking account of the motional characteristics of the fish and its environment, six primitive behaviors are introduced and the corresponding mechanism for behavior selection is designed. Coordinate motions of the fish are then accomplished by means of behavior based strategies involving role assignments, behavior selection and role transition. Two experiments of passing-hole and pushing-ball primarily show the feasibility and effectiveness of our approach

In this paper, we propose a device for cooperation of multiple autonomous mobile robots. A device which enables local comnaunication, called Intelligent Data Carrier (IDC) is proposed. In this paper, we propose an algorithm to improve efficiency of iterative transportation tasks by autonomous mobile robots. An autonomous mobile robot gives name of its latest visited destination to an IDC which is located on a junction in a work area. An IDC summarize the information from robots and tell a direction which it expects best way to a destination which a robot want to go. By the proposed algorithm and system, mobile robots construct information of navigation and improve the efficiency of a task automatically. We verified the effectof the proposed algorithm by simulations and experiments.

In this paper, a unique multiple mobile robots system is proposed to enable engineering students and engineers in the field to efficiently learn subsumption architecture and develop swarm intelligence. The subsumption architecture is known as one of the behaviour-based artificial intelligence. Each of multiple mobile robots within the system has three wheels driven by DC motors and six position sensitive detector (PSD) sensors. Network-based subsumption architecture is considered to realise a schooling behaviour by using only information from the PSD sensors. Further, a server supervisory control is introduced for poor hardware platforms with limitations of software development, i.e., the mobile robots can only behave based on the most simply subdivided reaction behaviours, i.e., reflex actions, generated from agents. Experimental results show interesting behaviour among the multiple mobile robots, such as following, avoidance and schooling.

This paper presents a high-level design approach to cooperative control of multiple mobile robots with local sensors. The method consists of two stages. In the first stage, we design global control plans for multiple robots, which include task allocation and task change plans to complete the goal. In the second stage, we design control logic and error recovery methods for an individual robot. Sensor information is integrated by merging similar sensor states together using AND or OR logic. Unique control commands are generated by eliminating logical conflicts using transition firing sequences in the Petri net models. Errors are recovered by changing control commands dynamically using reference and control models. To demonstrate the effectiveness of the proposed method, an object-searching task is evaluated. This task is to search an object such as a box using two robots, and consists of two sub-tasks, i.e., a wall-tracking task and a robot-tracking task. Simulation results of the object-searching task, the wall-tracking task, and the error recovery are presented.

Multiple mobile robot system under decentralized control has become more popular because of advantage of fault tolerance and extensibility. This paper focuses on a planning method for an interactive transportation task by multiple mobile robot system. In this task, task assignment is important to solve interaction among mobile robots. In this paper, we propose the self-generating task assignment algorithm in order to realize efficient sorting task. First, we considered parallelism in transportation task. Second, we defined variable of “Progress” in order to estimate value of tasks which can execute independently and in parallel. Using Progress, robots can select executable parallel task and generate task assignment based on efficiency. We developed multi-robot simulator and multi robot system in order to verify the effectiveness.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents a coordination method for multiple biomimetic robotic fish in underwater transport task. Based on our robotic fish prototype, we further investigate the coordination problems of multiple robotic fish. Considering the kinematic constraints of the robotic fish, and the unstructured and dynamic characteristics of the water environment, we utilize the limit cycle approach to control the posture of the fish and realize collision avoidance. To control the transport orientation in the particular underwater environment, the fuzzy logic method is adopted. Experimental results of transportation performed by three robotic fish validate the effectiveness of the proposed method. </para>

This article, concerned with a part of the research on distributed coordination work by multiple robots, discusses an algorithm for creating maps of unknown environments which are searched for and observed by multiple mobile robots, and on the results of a simulation experiment using this algorithm. This algorithm comprises a moving method, an observation method, and a task planning method which are intended to help the multiple mobile robots carry out an efficient search of unknown environments.

We present path-planning techniques for a multiple mobile robot system. The mobile robot has the shape of a cylinder, and its diameter, height, and weight are 8 cm, 15 cm, and 1.5 kg, respectively. The controller of the mobile robot is an MCS-51 chip, and it acquires detection signals from sensors through I/O pins. It receives commands from the supervising computer via a wireless RF interface, and transmits the status of the robots to the supervising computer via a wireless RF interface. The mobile robot system is a module-based system, and contains a controller module (including two DC motors and drivers), an obstacle detection module, a voice module, a wireless RF module, an encoder module, and a compass detection module. We propose an evaluation method to arrange the position of the multiple mobile robot system, and develop a path-planning interface on the supervising computer. In the experimental results, the mobile robots were able to receive commands from the supervising computer, and to move their next positions according to the proposed method.

The objective of this paper is to develop a localization system for cooperative multiple mobile robots, in which each robot is assumed to observe a set of known landmarks and equipped with an omnidirectional camera. If only a limited number of landmarks are available, it suffers from law accuracy. In this paper, it is assumed that a robot can detect other robots by using the omnidirectional camera and know their positions estimated by themselves and utilize the positions for its localization. In other words each robot can be viewed as a new mobile landmark. However, since the position information estimated by the robot itself is inaccurate, the estimated position of the robot cannot be high enough. This paper presents an investigation of the self localization error of a robot using Extended Kalman Filter to solve the localization problem with the insufficient landmarks and inaccurate position information.

This paper deals with a framework of decentralized approach to division of labor by multiple mobile robots. An iterative-transportation task by multiple robots with multiple sets of starts (pick-up place of the object) and goals (put down place) is considered as the task. On each route between a start and a goal, the efficiency of transportation improves when the number of robots increases. Due to jams, however, excessive number of robots on the same route causes inefficiency. We propose a control law of each robot to choose an appropriate route so as to optimize the total efficiency of the transportation, where each robot utilizes information which can be obtained only by local observation (without any explicit communication among robots). The proposed control is based on the estimation of the number of robots on the routes in the future. In simulation, it was verified that the proposed control law realized 96% efficiency of the fully centralized control by appropriately choosing the route, compared with the case where global information can be utilized.

In this paper, a novel DRL algorithm based on a DQN is proposed for multiple mobile robots to find optimized paths. The multiple robots’ states are the inputs of the DQN. The DQN estimates the Q-value of the agents’ actions. After selecting the action with the maximum Q-value, the multiple robots’ actions are calculated and sent to them. Then, the robots will explore the area and detect the obstacles. In the area, there are static obstacles. The robots should detect the static obstacles using a LiDAR sensor. The other moving robots are recognized as dynamic obstacles that need to be avoided. The robots will give feedback on the reward and the robots’ new states. A positive reward will be given when a robot successfully arrives at its goal point. If it is in a free space, zero reward will be given. If the robot collides with a static obstacle or other robots or reaches its start point, it will receive a negative reward. Multiple robots explore safe paths to the goals at the same time, in order to improve learning efficiency. If a robot collides with an obstacle or other robots, it will stop and wait for the other robots to complete their exploration tasks. The episode will end when all robots find safe paths to reach their goals or when all of them have collisions. This collaborative behavior can reduce the risk of collisions between robots, enhance overall efficiency, and help avoid multiple robots attempting to navigate through the same unsafe path simultaneously. Moreover, storage space is used to store the optimal safe paths of all robots. Finally, the multi-robots will learn the policy to find the optimized paths to go to the goal points. The goal of the simulations and experiment is to make multiple robots efficiently and safely move to their goal points.

An essential task for a multiple autonomous mobile robot system is to search for and to gather objects. To accomplish such a task effectively, it is essential that all the agents share common information on locations of objects, comrade agents and the place to deposit the objects. The authors have proposed a method called "COMPASS" to realize this only using two functions of each agent-to estimate the location of the objects by dead reckoning and to exchange the information with each other by one to one bidirectional proximity communication. In the paper, a new concept "figure of confidence (FOC)" is introduced to the COMPASS for higher performance and additional capability. The FOC is a self-estimated measure for accuracy of information in each agent. Using the FOC, agents can accelerate information sharing and handle multiple objects without significantly increasing complexity of the algorithm. The validity of FOC is confirmed through computer simulations.

This paper considers formation control of a group of wheeled mobile robots with uncertainty. Decentralized cooperative robust controllers are proposed in two steps. In the first step, cooperative control laws are proposed for multiple kinematic systems with the aid of results from graph theory such that a group of robots comes into a desired formation. In the second step, cooperative robust control laws for multiple uncertain dynamic systems are proposed with the aid of backstepping techniques and the passivity properties of the dynamic systems such that multiple robots comes into a desired formation. Since communication delay is inevitable in cooperative control, its effect on the proposed controllers is analyzed. Simulation results show the effectiveness of the proposed controllers.

This paper investigates finite-time tracking control problem of multiple non-holonomic mobile robots via visual servoing. It is assumed that the pinhole camera is fixed to the ceiling, and camera parameters are unknown. The desired reference trajectory is represented by a virtual leader whose states are available to only a subset of the followers, and the followers have only interaction. First, the camera-objective visual kinematic model is introduced by utilising the pinhole camera model for each mobile robot. Second, a unified tracking error system between camera-objective visual servoing model and desired reference trajectory is introduced. Third, based on the neighbour rule and by using finite-time control method, continuous distributed cooperative finite-time tracking control laws are designed for each mobile robot with unknown camera parameters, where the communication topology among the multiple mobile robots is assumed to be a directed graph. Rigorous proof shows that the group of mobile robots converges to the desired reference trajectory in finite time. Simulation example illustrates the effectiveness of our method.

This paper proposes an approach to the motion coordination of multiple industrial mobile robots (AGVs). The task of the robots is to cooperatively transport a common object. An ordinary mobile robot has uncertain dynamics, and its motion is constrained by a non-holonomic constraint. Furthermore, multiple robots with a common object form a closed-chain structure which would cause the severe dynamical interactions. The authors' approach overcomes the inherent problems by introducing a robot-mounted coupler with mechanical compliance which provides compliance between each robot and the object. The hierarchical coordinative control scheme is presented to perform accurate object-transportation based on the synchronous motions of the robots. Simulation and fundamental experiment by using a commercially available AGV demonstrate the effectiveness of the authors' approach.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>