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Illustration of cooperative movement of a human and swarm robot H.Hashimoto is with Advanced Institute of Industrial Technology, Tokyo, Japan hashimoto@aiit.ac.jp S.Aso, S.Yokota, A.Sasaki and Y.Ohyama are with Tokyo University of Technology, Tokyo, Japan H.Kobayashi is with Osaka Institute of Technology, Osaka, Japan
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This paper proposes a method that a human and a swarm robot move cooperatively so as to maintain the swarm situation that the swarm robot surround the moving human. This paper is concerned with the control for maintaining the high stability of the swarm, and proposes a control algorithm for the robotic swarm in obstacle space. In this paper, the ro...
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Citations
... The main issue in the behavioral based method is detecting the target while avoiding obstacles in the region and the required behaviors are previously prescribed for the robots [21] [22]. ...
... In some cases, a repulsive force was applied to avoid collision between the robot and obstacles, see e.g. [21][28] [29]. In some other cases, researchers applied potential field for collision avoidance between agents, see e.g. ...
... In Hashimoto et al. (2008), the swarm consisting several robots can follow, surround a human and maintain stability while the human moves in obstacle environment. Each robot calculates its own virtual force from attraction and repulsion from its neighbouring robots. ...
... However it is difficult to form specified shape when viewed globally. Implementations of this method can be found in Hashimoto et al. (2008). ...
Social insects can achieve remarkable outcomes, various examples can be found in ants, bees, etc. Inspired by social insects, swarm robotic research considers coordinating a group of relatively simple and autonomous robots to finish tasks collaboratively based on direct or indirect interactions. Such systems can offer advantages of robustness, flexibility and scalability, just like social insects.
For many years, various researchers have endeavoured to design intelligent artificial swarms and many hardware-based swarm robots have been implemented. One assumption that made by a majority of swarm robotic researchers, particularly in software simulation is that a robotic swarm is a group of identical robots, there is no difference between any two of them. However, differences among hardware robots are unavoidable, which exist in robotic sensors, actuators, etc. These hardware differences, albeit small, can affect the robots’ response to the environment. Moreover, hardware differences can provoke robots’ heterogeneity which then profoundly influence swarm performance due to the non-linearity in the controller and uncertainty in the environment. Nevertheless, questions about how hardware differences influence swarm performance and how to make use of them remain a research challenge.
In this work, the issue of hardware variation in swarm robots is investigated. Specifically swarm robots with hardware variations are modelled and simulated in a line following scenario. It is found that even small hardware variations can result in behavioural heterogeneity. Although the variations can be compensated by the software controller in training, the hardware variations and resulting differences in training are amplified in the interactions between the robot and the environment.
To know how exactly hardware variation influence robotic behaviours, a novel approach, inspired by the chromatography method in chemistry, is proposed to sort swarm robots according to their hardware circumstances. This method is based on a large number of interactions between robots and the environment. Individual robot’s unique hardware circumstance determines its unique decision making and reaction during each robotic controlling step, and these unique microscopic reactions accumulate and contribute to the robot’s macroscopic behaviour. The behavioural sorting results show that the behaviour of an individual robot is not determined by a single parameter but by the combination of multiple hardware factors. Different combinations of hardware parameters can help robots achieve similar behaviours.
The efficiency of the behavioural sorting method is investigated, particularly the influence of the robot’s controller and environmental factor. By simulating various combinations of robots with different integration lengths of the controller and arenas with different pattern densities, it is discovered that if the robots’ ability to memorise previous events is coupled with the density of the sorting arena, better sorting results can be achieved.
This work is regarded as an initial investigation into the issue of unavoidable hardware differences between swarm robots. Given the research outcome and that real swarms will necessarily show hardware variations, it is therefore necessary to contemplate current swarm algorithms in the context of diverse robot populations. In addition, a new research field of swarm chromatography for sorting robotic behaviours to improve swarm efficiency is initiated.
In this study, the process of designing a motion optimization algorithm for swarm quadrotor robots is presented. Motions equations of swarm are written based on Lagrangian energy equations. A potential function is applied on the equations to optimize the swarm motion. The applied potential function enables each of the swarm members to move toward an independent target coordinate as motion starts and simultaneously connecting with other members. As a result, the necessity of having the members aggregated within an area close to the swarm center is eliminated. This algorithm is supposed to act on swarm of quadrotors; therefore a validated dynamic model of quadrotor and a designed controller are introduced to discuss the possible applications. The designed algorithm is then applied to grasp an object. In order to establish grasping, particle swarm optimization method is used. Finally, the algorithm is simulated in MATLAB for a two-member swarm of quadrotors for grasping the object. Simulation results indicate increased work space for the members along the motion path and reduced mission time.
The ability of mobile robots to work as a team in hard and hazardous environments and consequently their widespread use in various industries is a strong incentive for researchers to develop practical algorithm and methods for increasing the performance of mobile robots. The ability of autonomous decision-making for navigation and path planning is the important problem, which has been investigated by researchers to improve the performance of a team of mobile robots in a certain mission. The contribution of this study is classified as follows; In the first stage, we propose a decentralised motion control algorithm for the mobile robots to intercept an intruder entering (k-intercepting) or escaping (e-intercepting) a protected region. In continue, we propose a decentralized navigation strategy (dynamic-intercepting) for a multi-robot team known as predators to intercept the intruders or in the other words, preys, from escaping a siege ring which is created by the predators. A necessary and sufficient condition for the existence of a solution of this problem is obtained. At the second stage, we propose an intelligent game-based decision-making algorithm (IGD) for a fleet of mobile robots to maximize the probability of detection in a bounded region. We prove that the proposed decentralised cooperative and non-cooperative game-based decision-making algorithm enables each robot to make the best decision to choose the shortest path with minimum local information. Third, we propose a leader-follower based collision-free navigation control method for a fleet of mobile robots to traverse an unknown cluttered environment. Fourth, we propose a decentralised navigation algorithm for a team of multi-robot to traverse an area where occupied by multiple obstacles to trap a target. We prove that each individual team 3 member is able to traverse safely in the region, which is cluttered by many obstacles with any shapes to trap the target while using the sensors in some indefinite switching points and not continuously, which leads to saving energy consumption and increasing the battery life of the robots consequently. And finally, we propose a novel navigation strategy for a unicycle mobile robot in a cluttered area with moving obstacles based on virtual field force algorithm. The mathematical proof of the navigation laws and the computer simulations are provided to confirm the validity, robustness, and reliability of the proposed methods. 4
In this report, we propose a decentralised motion control algorithm for the mobile robots to intercept an intruder entering (k-intercepting) or escaping (e-intercepting) a protected region. In continuation, we propose a decentralized navigation strategy (dynamic-intercepting) for a multi-robot team known as predators to intercept the intruders or in the other words, preys, from escaping a siege ring which is created by the predators. A necessary and sufficient condition for the existence of a solution of this problem is obtained. Furthermore, we propose an intelligent game-based decision-making algorithm (IGD) for a fleet of mobile robots to maximize the probability of detection in a bounded region. We prove that the proposed decentralised cooperative and non-cooperative game-based decision-making algorithm enables each robot to make the best decision to choose the shortest path with minimum local information. Then we propose a leader-follower based collision-free navigation control method for a fleet of mobile robots to traverse an unknown cluttered environment where is occupied by multiple obstacles to trap a target. We prove that each individual team member is able to traverse safely in the region, which is cluttered by many obstacles with any shapes to trap the target while using the sensors in some indefinite switching points and not continuously, which leads to saving energy consumption and increasing the battery life of the robots consequently. And finally, we propose a novel navigation strategy for a unicycle mobile robot in a cluttered area with moving obstacles based on virtual field force algorithm. The mathematical proof of the navigation laws and the computer simulations are provided to confirm the validity, robustness, and reliability of the proposed methods.
This paper proposes networked swarm robotic systems with group based control scheme using spring damper impendence feature. The proposed algorithm is applied to keep system arrangement in unexpected situations based on the spring-damper impedance and fuzzy logic. Using the proposed scheme, each robot overcome collision problems efficiently. The structure of UBSR (UMPC Based Swarm Robot) system consists of user level, cognitive level, and executive level. This structure is designed to easily meet the different configuration requirements for other levels. Simulation results show an availability of the proposed method.
Applications for multi-agent robot systems have been extensively studied because of their flexibility under a wide variety of situations and there are various proposals for control methods. Our research focuses on the control of robot positions for a generalized task: the transportation of some object of unknown shape to some goal destination. We propose a method that places each robot at a position along the contour of the object based on the Active Counter Model, a method often utilized in image processing. With our ACM-inspired algorithm, robots can surround the target object without knowing its shape in advance. If object transportation by a multi-agent system which was less expensive and utilized exchangeable robots were realized it would have properties that would lend itself to applications in real world environments.
In this paper, we investigate the leader-follower based formation control of nonholonomic wheeled mobile robots. To this end, we first derive a dynamics model for the considered wheeled mobile robot using Lagrange's equations of motion. Then, using ADAMS multi body simulation software, the obtained dynamics of the wheeled system is verified. After that, we design a Fuzzy Logic Controller (FLC) for generating and keeping the desired formation. In this regard, the leader mobile robot is controlled to follow a reference path and the follower robots use the FLC to keep constant relative distance and constant angle with respect to the leader. The efficiency of this suggested formation controller has been proved using computer simulations on different situations and desired trajectories.
In this paper, a novel approach for obstacle avoidance of unmanned aircrafts is introduced. To avoid collision, a potential field whose vectors are rotating around the obstacle is proposed. Most approaches using potential field in the literature repulse unmanned vehicles to get them far from obstacles. In a case that a vehicle approaches an obstacle exactly in the opposite direction of the repulsive vectors, the vehicle may locate in a local minima position and its speed converges to zero. The proposed technique in this paper considers constrains on motion control of unmanned aircrafts. The introduced rotational potential field is adaptive with the attitude of an approaching unmanned aircraft and its relative position to the obstacle to avoid making it slow down and stick in local minima positions. The simulation results confirm the feasibility of the proposed approach.
