Статті в журналах: "Formations multi-robots"

1

Barca, Jan Carlo, Eugene Eu-Juin Lee, and Ahmet Sekercioglu. "Flexible Morphogenesis based Formation Control for Multi-Robot Systems." IAES International Journal of Robotics and Automation (IJRA) 2, no. 1 (March 1, 2013): 26. http://dx.doi.org/10.11591/ijra.v2i1.pp26-34.

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Inspired by how biological cells communicate with each other at a cell-to-cell level; morphogenesis emerged to be an effective way for local communication between homogenous robots in multi-robot systems. In this paper, we present the first steps towards a scalable morphogenesis style formation control technique, which address the drawbacks associated with current morphogenesis type formation control techniques, including their inability to distribute robots evenly across target shapes. A series of experiments, which demonstrate that the proposed technique enables groups of non-holonomic ground moving robots to generate formations in less than 9 seconds with three robots and less than 22 seconds with five robots, is also presented. These experiments furthermore reveal that the proposed technique enables groups of robots to generate formations without significantly increasing the total travel distance when faced with obstacles. This work is an important contribution to multi-robot control theory as history has shown that the success of groups often depends on efficient and robust formation control.

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2

Rashid, Abdulmuttalib, Abduladhem Ali, and Mattia Frasca. "Polygon Shape Formation for Multi-Mobile Robots in a Global Knowledge Environment." Iraqi Journal for Electrical and Electronic Engineering 15, no. 1 (June 1, 2019): 76–88. http://dx.doi.org/10.37917/ijeee.15.1.8.

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In coordination of a group of mobile robots in a real environment, the formation is an important task. Multimobile robot formations in global knowledge environments are achieved using small robots with small hardware capabilities. To perform formation, localization, orientation, path planning and obstacle and collision avoidance should be accomplished. Finally, several static and dynamic strategies for polygon shape formation are implemented. For these formations minimizing the energy spent by the robots or the time for achieving the task, have been investigated. These strategies have better efficiency in completing the formation, since they use the cluster matching algorithm instead of the triangulation algorithm.

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3

Cao, Kai, Yangquan Chen, Song Gao, Hang Zhang, and Haixin Dang. "Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management." Applied Sciences 12, no. 2 (January 12, 2022): 755. http://dx.doi.org/10.3390/app12020755.

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In view of the low formation redundancy in the traditional rigid formation algorithm and its difficulty in dynamically adapting to the external environment, this study considers the use of the CVT (centroidal Voronoi tessellation) algorithm to control multiple robots to form the desired formation. This method significantly increases the complexity of the multi-robot system, its structural redundancy, and its internal carrying capacity. First, we used the CVT algorithm to complete the Voronoi division of the global map, and then changed the centroid position of the Voronoi cell by adjusting the density function. When the algorithm converged, it could ensure that the position of the generated point was the centroid of each Voronoi cell and control the robot to track the position of the generated point to form the desired formation. The use of traditional formations requires less consideration of the impact of the actual environment on the health of robots, the overall mission performance of the formation, and the future reliability. We propose a health optimization management algorithm based on minor changes to the original framework to minimize the health loss of robots and reduce the impact of environmental restrictions on formation sites, thereby improving the robustness of the formation system. Simulation and robot formation experiments proved that the CVT algorithm could control the robots to quickly generate formations, easily switch formations dynamically, and solve the formation maintenance problem in obstacle scenarios. Furthermore, the health optimization management algorithm could maximize the life of unhealthy robots, making the formation more robust when performing tasks in different scenarios.

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Lin, Y. C., C. H. Chen, K. L. Su, and J. H. Guo. "Image Recognition Method Applying in Formation Control of Mobile Robots." Applied Mechanics and Materials 190-191 (July 2012): 693–98. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.693.

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The article develops multi-pattern formation exchange using A* searching algorithm, and programs the shortest motion paths for mobile robots. The system contains an image recognition system, a motion platform, some wireless RF modules and five mobile robots. We use Otsu algorithm to recognize the variety 2D bar code to classify variety pattern, and control five mobile robots to execute formation exchange, and present the movement scenario on the motion platform. We have been developed some pattern formations according to game applications, such as hook pattern formation, T pattern formation, L pattern formation, rectangle pattern formation, sward pattern formation and so on, and develop the user interface of the multi-robot system to program motion paths for variety pattern formation exchange on the supervised computer. The supervised computer programs pattern formation exchange according to the image recognition results, and controls mobile robots moving on the motion platform via wireless RF interface. In the experimental results, mobile robots can receive the pattern formation command from the supervised computer, and change the original pattern formation to the assigned pattern formation on the motion platform, and avoid other mobile robots on real-time.

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5

Dasgupta, Prithviraj, Taylor Whipple, and Ke Cheng. "Effects of Multi-Robot Team Formations on Distributed Area Coverage." International Journal of Swarm Intelligence Research 2, no. 1 (January 2011): 44–69. http://dx.doi.org/10.4018/jsir.2011010103.

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This paper examines the problem of distributed coverage of an initially unknown environment using a multi-robot system. Specifically, focus is on a coverage technique for coordinating teams of multiple mobile robots that are deployed and maintained in a certain formation while covering the environment. The technique is analyzed theoretically and experimentally to verify its operation and performance within the Webots robot simulator, as well as on physical robots. Experimental results show that the described coverage technique with robot teams moving in formation can perform comparably with a technique where the robots move individually while covering the environment. The authors also quantify the effect of various parameters of the system, such as the size of the robot teams, the presence of localization, and wheel slip noise, as well as environment related features like the size of the environment and the presence of obstacles and walls on the performance of the area coverage operation.

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6

Seng, Whye Leon, Jan Carlo Barca, and Y. Ahmet Şekercioğlu. "Distributed formation control of networked mobile robots in environments with obstacles." Robotica 34, no. 6 (October 15, 2014): 1403–15. http://dx.doi.org/10.1017/s0263574714002380.

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SUMMARYA distributed control mechanism for ground moving nonholonomic robots is proposed. It enables a group of mobile robots to autonomously manage formation shapes while navigating through environments with obstacles. The mechanism consists of two stages, with the first being formation control that allows basic formation shapes to be maintained without the need of any inter-robot communication. It is followed by obstacle avoidance, which is designed with maintaining the formation in mind. Every robot is capable of performing basic obstacle avoidance by itself. However, to ensure that the formation shape is maintained, formation scaling is implemented. If the formation fails to hold its shape when navigating through environments with obstacles, formation morphing has been incorporated to preserve the interconnectivity of the robots, thus reducing the possibility of losing robots from the formation.The algorithm has been implemented on a nonholonomic multi-robot system for empirical analysis. Experimental results demonstrate formations completing an obstacle course within 12 s with zero collisions. Furthermore, the system is capable of withstanding up to 25% sensor noise.

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7

Kuppan Chetty, R. M., M. Singaperumal, and T. Nagarajan. "Behavior Based Multi Robot Formations with Active Obstacle Avoidance Based on Switching Control Strategy." Advanced Materials Research 433-440 (January 2012): 6630–35. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6630.

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This paper considers the problem of formation control and obstacle avoidance for a group of non-holonomic mobile robots in a leader referenced model based on reactive switching control strategy. Three important issues related to the multi robot formation namely distributed formation control framework, dynamic role switching algorithm and real time implementations are investigated. The switching control strategy combines together formation planning, navigation and active obstacle avoidance in a layered control framework composed of functional behaviors based on the relative motion states of the robots employed in the group. Dynamic role switching mechanism incorporated in this work to tackles the problem of obstacle avoidance in the follower path. The proposed approach is validated through laboratory experiments using commercially available robot research platforms and the results obtained are discussed.

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8

Guo, J. Hung, Yung Chin Lin, Kuo Lan Su, and Bo Yi Li. "Motion Planning of Multiple Pattern Formation for Mobile Robots." Applied Mechanics and Materials 284-287 (January 2013): 1877–82. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1877.

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The article designs the multiple pattern formation controls of the multi-robot system according to two arms’ gesture of the player, and uses flood fill searching algorithm and A* searching algorithm to program the motion paths. The inertia module detects two arms’ gesture of the player. We use the inertia module to be embedded in the two arms, and use mobile robots to present the movement scenario of pattern formation exchange on the grid based motion platform. We have been developed some pattern formations applying in the war game, such as rectangle pattern formation, long snake pattern formation, L pattern formation, sword pattern formation, cone pattern formation and so on. We develop the user interface for variety pattern formation exchange according to the minimum displacement on the supervised computer. The mobile robot receives the command from the supervised compute, and transmits the status of environment to the supervised computer via wireless RF interface. Players can use variety arms’ gesture to control the multiple mobile robots to executed pattern formation exchange. In the experimental results, the supervised computer can decides the arm gesture using fusion algorithms. Mobile robots can receive the pattern formation command from the supervised computer, and change the original pattern formation to the assigned pattern formation on the motion platform, and avoid other mobile robots.

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9

De La Cruz, Celso, and Ricardo Carelli. "Dynamic model based formation control and obstacle avoidance of multi-robot systems." Robotica 26, no. 3 (May 2008): 345–56. http://dx.doi.org/10.1017/s0263574707004092.

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SUMMARYThis work presents, first, a complete dynamic model of a unicycle-like mobile robot that takes part in a multi-robot formation. A linear parameterization of this model is performed in order to identify the model parameters. Then, the robot model is input-output feedback linearized. On a second stage, for the multi-robot system, a model is obtained by arranging into a single equation all the feedback linearized robot models. This multi-robot model is expressed in terms of formation states by applying a coordinate transformation. The inverse dynamics technique is then applied to design a formation control. The controller can be applied both to positioning and to tracking desired robot formations. The formation control can be centralized or decentralized and scalable to any number of robots. A strategy for rigid formation obstacle avoidance is also proposed. Experimental results validate the control system design.

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10

Zhuang, Hongchao, Kailun Dong, Ning Wang, and Lei Dong. "Multi-Robot Leader Grouping Consistent Formation Control Method Research with Low Convergence Time Based on Nonholonomic Constraints." Applied Sciences 12, no. 5 (February 22, 2022): 2300. http://dx.doi.org/10.3390/app12052300.

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Aiming at the formation and maintenance of the multiple formations of nonholonomic constrained multi-robots, a leader-follower formation control method under the grouping consistency is proposed on the trajectory tracking of a nonholonomic constrained mobile robots with the low convergence time. The distributed control structure in the leader-follower formation is adopted. The multi-robot cooperative formation is realized by using the consistency algorithm of graph theory. According to the graph theory, the communication topology matrixes are designed by the consistency algorithm. The mathematical model of nonholonomic constrained robot is established with the wheeled structure as the mobile structure under the nonholonomic constraints. Then the navigation following model is transformed into the error model of a local coordinate system through the global coordinate transformation. The formation control law of multi-robot cooperative motion is put forward based on the leader-follower model. Its convergence is proved by the Lyapunov function. By setting the reasonable communication protocol parameters, the MATLAB software (Natick, MA, USA, R2016b) is employed on the simulation verification and result comparison. Through the comparison of the two leader formation control methods, the convergence time of the algorithm in this article can be 25% less than that of PFC. The effectiveness and feasibility of the formation control law are verified under the leader-follower method. The proposed control method lays a foundation for reducing the convergence time to improve the multi-robot cooperative motion under nonholonomic constraints.

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11

TIAN, WEN-QIANG, DAN GAO, and YING-GUAN WANG. "SELF-ORGANIZED COLLECTIVE CRYSTAL-LIKE FORMATIONS OF THE ATTRACTIVE/REPULSIVE SWARMING SYSTEM." Modern Physics Letters B 28, no. 01 (December 23, 2013): 1450003. http://dx.doi.org/10.1142/s0217984914500031.

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In this paper, an adaptive attractive/repulsive (A/R) swarming model is proposed to explore the role of self-organized formation in swarming systems. By defining the adjustable A/R range γi, which is affected by the localized steady state of agents, the standard collective crystal-like swarming formations are straightforwardly unfolded in different scale. Meanwhile, with numerical simulations and analyses, the results show that the adaptive A/R swarming model provides an effective solution to the current existing dilemma of the collective liquid-like formation with unexpected neighbor distances and the split crystal-like formation. The actual neighbor distance of the adaptive A/R model could converge to the expected neighbor distance as planned, based on the different settings of the expected neighbor distance and the A/R range. Moreover, such adjustable A/R swarming formations may find their potential applications such as the formation of self-organized multi-robots and unmanned aerial vehicles, the automatic networking of sensors, etc.

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12

Shenoy, Meetha V., and K. R. Anupama. "DTTA - Distributed, Time-division Multiple Access based Task Allocation Framework for Swarm Robots." Defence Science Journal 67, no. 3 (April 25, 2017): 316. http://dx.doi.org/10.14429/dsj.67.10955.

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Swarm robotic systems, unlike traditional multi-robotic systems, deploy number of cost effective robots which can co-operate, aggregate to form patterns/formations and accomplish missions beyond the capabilities of individual robot. In the event of fire, mine collapse or disasters like earthquake, swarm of robots can enter the area, conduct rescue operations, collect images and convey locations of interest to the rescue team and enable them to plan their approach in advance. Task allocation among members of the swarm is a critical and challenging problem to be addressed. DTTA- a distributed, Time-division multiple access (TDMA) based task allocation framework is proposed for swarm of robots which can be utilised to solve any of the 8 different types of task allocation problem identified by Gerkey and Mataric´. DTTA is reactive and supports task migration via extended task assignments to complete the mission in case of failure of the assigned robot to complete the task. DTTA can be utilised for any kind of robot in land or for co-operative systems comprising of land robots and air-borne drones. Dependencies with other layers of the protocol stack were identified and a quantitative analysis of communication and computational complexity is provided. To our knowledge this is the first work to be reported on task allocation for clustered scalable networks suitable for handling all 8 types of multi-robot task allocation problem. Effectiveness and feasibility of deploying DTTA in real world scenarios is demonstrated by testing the framework for two diverse application scenarios.

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13

Vanualailai, Jito. "Stable emergent formations for a swarm of autonomous car-like vehicles." International Journal of Advanced Robotic Systems 16, no. 5 (September 1, 2019): 172988141984978. http://dx.doi.org/10.1177/1729881419849780.

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A biological swarm is an ideal multi-agent system that collectively self-organizes into bounded, if not stable, formations. A mathematical model, developed appropriately from some principle of swarming, should enable one, therefore, to study formation strategies for multiple autonomous robots. In this article, based on the hypothesis that swarming is an interplay between long-range attraction and short-range repulsion between the individuals in the swarm, a planar individual-based or Lagrangian swarm model is constructed using the Direct Method of Lyapunov. While attraction ensures the swarm is cohesive, meaning that the individuals in the swarm remain close to each other at all times, repulsion ensures that the swarm is well-spaced, meaning that no two individuals in the swarm occupy the same space at the same time. Via a novel Lyapunov-like function with attractive and repulsive components, the article establishes the global existence, uniqueness, and boundedness of solutions about the centroid. This paves the way to prove that the swarm model, governed by a system of first-order ordinary differential equations (ODEs), is cohesive and well-spaced. The article goes on to show that the artificial swarm can collectively self-organize into two stable formations: (i) a constant arrangement about the centroid when the system has equilibrium points, and (ii) a highly parallel formation when the system does not have equilibrium points. Computer simulations not only illustrate these but also reveal other emergent patterns such as swirling structures and random-like walks. As an application, we tailor the model accordingly and propose new autonomous steering laws giving rise to pattern-forming for multiple nonholonomic car-like vehicles.

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14

Salehizadeh, Mohammad, and Eric Diller. "Three-dimensional independent control of multiple magnetic microrobots via inter-agent forces." International Journal of Robotics Research 39, no. 12 (August 5, 2020): 1377–96. http://dx.doi.org/10.1177/0278364920933655.

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This article presents a method to independently control the position of multiple microscale magnetic robots in three dimensions, operating in close proximity to each other. Having multiple magnetic microrobots work together in close proximity is difficult due to magnetic interactions between the robots, and here we aim to control those interactions for the creation of desired multi-agent formations in three dimensions. Based on the fact that all magnetic agents orient to the global input magnetic field, the local attraction–repulsion forces between nearby agents can be regulated. For the first time, 3D manipulation of two microgripping magnetic microrobots is demonstrated. We also mathematically and experimentally prove that the center-of-mass external magnetic pulling of the multi-agent system is possible in three dimensions with an underactuated magnetic field generator. Here we utilize the controlled interaction magnetic forces between two spherical agents to steer them along two prescribed paths. We apply our method to independently control the motion of a pair of magnetic microgrippers as functional microrobot candidates each equipped with a five-degree-of-freedom motion mechanism and a grasp–release mechanism for targeted cargo delivery. A proportional controller and an optimization-based controller are introduced and compared, with potential to control more than two magnetic agents in three dimensions. Average tracking errors of less than 141 and 165 micrometers are accomplished for the regulation of agents’ positions using optimization-based and proportional controllers, respectively, for spherical agents with approximate nominal radius of 500 micrometers operating within several body-lengths of each other.

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15

Eu, Kok Seng, Kian Meng Yap, and Tiam Hee Tee. "Supporting Odour Source Localisation of Multi-Sniffer Robots with up-Wind Formation Repeater Network Topology." Journal of Advances in Computer Networks 3, no. 1 (2015): 24–27. http://dx.doi.org/10.7763/jacn.2015.v3.136.

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Cai, Yun Fei, Zhen Min Tang, and Chun Xia Zhao. "Multi-EKF Localization Algorithm for Multi-Robots Formation Navigation." Advanced Materials Research 383-390 (November 2011): 5648–55. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5648.

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This research presents a dynamic multi-robots formation navigation algorithm named multi-EKF localization algorithm, which derived from single EKF. In this algorithm, all formation members are treated as landmarks with known association. When the formation is running, the traditional EKF is used for individual robot to get its own localization, and the proposed multi-EKF is used to get the position of the whole formation. By calculating the joint probability distribution, the mean and covariance of the formation position are achieved, which is used to guide and constrain the individual robot localization adjustment. With this method, the multiple mobile robots system shows more stable and robust on formation navigation. The simulation and physical experiment results show the feasibility, efficiency and stability of the proposed algorithm.

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17

Gao, Xi Na, and Li Juan Wu. "Multi-Robot Formation Control Based on the Artificial Potential Field Method." Applied Mechanics and Materials 519-520 (February 2014): 1360–63. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1360.

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The artificial potential field method is one of multi-robot formation control methods. In this paper we make a study on multi-robot formation control based on the artificial potential field method and the leader-follower method. The robots are set leader robot and follower robots respectively. According to the known ideal distance between the leader and follower, we adjust the repulsiveness or attractiveness to maintain multi-robot formation. Multi-robots obstacle avoidance is adopted the artificial potential field method. In this paper the triangle formation is taken as an example. At last the simulation result proves the validity of this algorithm.

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Mao, Wenju, Zhijie Liu, Heng Liu, Fuzeng Yang, and Meirong Wang. "Research Progress on Synergistic Technologies of Agricultural Multi-Robots." Applied Sciences 11, no. 4 (February 5, 2021): 1448. http://dx.doi.org/10.3390/app11041448.

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Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation, path planning, formation control, and communication, and summarizes the technological progress and development characteristics of these five technologies. Finally, because of these development characteristics, it is shown that the trends and research focus for agricultural multi-robots are to optimize the existing technologies and apply them to a variety of agricultural multi-robots, such as building a hybrid architecture of multi-robot systems, SLAM (simultaneous localization and mapping), cooperation learning of robots, hybrid path planning and formation reconstruction. While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development demand, we conclude that it is realistic to expect automated multi-robot systems in the future.

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19

Wee, Sung-Gil, Yanyan Dai, Tae Hun Kang, and Suk-Gyu Lee. "Variable formation control of multiple robots via VRc and formation switching to accommodate large heading changes by leader robot." Advances in Mechanical Engineering 11, no. 6 (June 2019): 168781401985733. http://dx.doi.org/10.1177/1687814019857339.

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This article describes a novel multi-robot formation control based on a switching technique that allows follower robots to maintain formation when the leader robot’s direction changes rapidly or unexpectedly. The formation pattern is determined using Virtual Robot’s Center of the multi-robot formation. To avoid collision, the formation of robots reformed in optimal size by estimating the distance between the robot and an obstacle in real time. When the leader robot suddenly changes its direction, waypoints of follower robots are switched and the formation is quickly reconstructed. This prevents follower robots from colliding with each other and reduces their radius of movement and allows them to follow the leader robot at higher speed. The proposed method which is inherently a flexible control of multi-robot formation guarantees collision avoidance and prevents sudden changes in waypoints of the system by gradually changing its size. The validity of the proposed method is demonstrated via simulation and experimental results.

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Chen, Wenzhou, Shizheng Zhou, Zaisheng Pan, Huixian Zheng, and Yong Liu. "Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning." Applied Sciences 9, no. 20 (October 9, 2019): 4198. http://dx.doi.org/10.3390/app9204198.

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Compared with the single robot system, a multi-robot system has higher efficiency and fault tolerance. The multi-robot system has great potential in some application scenarios, such as the robot search, rescue and escort tasks, and so on. Deep reinforcement learning provides a potential framework for multi-robot formation and collaborative navigation. This paper mainly studies the collaborative formation and navigation of multi-robots by using the deep reinforcement learning algorithm. The proposed method improves the classical Deep Deterministic Policy Gradient (DDPG) to address the single robot mapless navigation task. We also extend the single-robot Deep Deterministic Policy Gradient algorithm to the multi-robot system, and obtain the Parallel Deep Deterministic Policy Gradient (PDDPG). By utilizing the 2D lidar sensor, the group of robots can accomplish the formation construction task and the collaborative formation navigation task. The experiment results in a Gazebo simulation platform illustrates that our method is capable of guiding mobile robots to construct the formation and keep the formation during group navigation, directly through raw lidar data inputs.

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HSU, HARRY CHIA-HUNG, and ALAN LIU. "APPLYING A TAXONOMY OF FORMATION CONTROL IN DEVELOPING A ROBOTIC SYSTEM." International Journal on Artificial Intelligence Tools 16, no. 04 (August 2007): 565–82. http://dx.doi.org/10.1142/s0218213007003436.

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Designing cooperative multi-robot systems (MRS) requires expert knowledge both in control and artificial intelligence. Formation control is an important research within the research field of MRS. Since many researchers use different ways in approaching formation control, we try to give a taxonomy in order to help researchers design formation systems in a systematical way. We can analyze formation structures in two categories: control abstraction and robot distinguishability. The control abstraction can be divided into three layers: formation shape, reference type, and robotic control. Furthermore, robots can be classified as anonymous robots or identification robots depending on whether robots are distinguishable according to their inner states. We use this taxonomy to analyze some ground-based formation systems and to state current challenges of formation control. Such information becomes the design know-how in developing a formation system, and a case study of designing a multi-team formation system is introduced to demonstrate the usefulness of the taxonomy.

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Baasandorj, Bayanjargal, Aamir Reyaz, Batmunkh Battulga, Deok Jin Lee, and Kil To Chong. "Formation of Multiple-Robots Using Vision Based Approach." Applied Mechanics and Materials 419 (October 2013): 768–73. http://dx.doi.org/10.4028/www.scientific.net/amm.419.768.

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Multi-robots system has grown enormously with a large variety of topics being addressed. It is an important research area within the robotics and artificial intelligence. By using the vision based approach this paper deals with the formation of multiple-robots. Three NXT robots were used in the experiment and all the three robots work together as one virtual mobile robot. In addition to these things we also used TCP/IP socket, ArToolKit, NXT robot, Bluetooth communication device. And for programming C++ was used. Results achieved from the experiment were highly successful.

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Falconi, Riccardo, Lorenzo Sabattini, Cristian Secchi, Cesare Fantuzzi, and Claudio Melchiorri. "Edge-weighted consensus-based formation control strategy with collision avoidance." Robotica 33, no. 2 (February 28, 2014): 332–47. http://dx.doi.org/10.1017/s0263574714000368.

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SUMMARYIn this paper, a consensus-based control strategy is presented to gather formation for a group of differential-wheeled robots. The formation shape and the avoidance of collisions between robots are obtained by exploiting the properties of weighted graphs. Since mobile robots are supposed to move in unknown environments, the presented approach to multi-robot coordination has been extended in order to include obstacle avoidance. The effectiveness of the proposed control strategy has been demonstrated by means of analytical proofs. Moreover, results of simulations and experiments on real robots are provided for validation purposes.

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TAKASHIMA, Koichi, Yuichi TAZAKI, and Tatsuya SUZUKI. "2A1-W06 Formation Control Considering Uncertainty of Positions of Multi Mobile Robots(Cooperation Control of Multi Robots)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2014 (2014): _2A1—W06_1—_2A1—W06_4. http://dx.doi.org/10.1299/jsmermd.2014._2a1-w06_1.

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25

ZHANG, Han-dong, Li HUANG, and Yu-wan CEN. "Improved approach of hybrid formation for multi-mobile robots." Journal of Computer Applications 32, no. 6 (August 26, 2013): 1955–57. http://dx.doi.org/10.3724/sp.j.1087.2012.01955.

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26

Hsu, Harry Chia-Hung, and Alan Liu. "Multiagent-Based Multi-team Formation Control for Mobile Robots." Journal of Intelligent and Robotic Systems 42, no. 4 (April 2005): 337–60. http://dx.doi.org/10.1007/s10846-005-2965-7.

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27

Dong, Youwei, and Ahmed Rahmani. "FORMATION CONTROL OF MULTIPLE UNICYCLE-TYPE ROBOTS USING LIE GROUP." Acta Polytechnica 56, no. 1 (February 29, 2016): 1. http://dx.doi.org/10.14311/app.2016.56.0001.

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In this paper the formation control of a multi-robots system is investigated. The proposed control law, based on Lie group theory, is applied to control the formation of a group of unicycle-type robots. The communication topology is supposed to be a rooted directed acyclic graph and fixed. Some numerical simulations using Matlab are made to validate our results.

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Yu, Yue, Xiangru Chen, Zhaohan Lu, Fengxia Li, and Bo Zhang. "Obstacle avoidance behavior of swarm robots based on aggregation and disaggregation method." SIMULATION 93, no. 11 (June 7, 2017): 885–98. http://dx.doi.org/10.1177/0037549717711281.

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One of the current challenges in the development of swarm robots is making them realize obstacle avoidance while keeping formation. This paper proposes a new obstacle avoidance method for multiple robots based on the aggregation and disaggregation approach. First, the formation-based multi-resolution models of swarm robots are presented and the formation attribute-based consistency mapping functions are designed. Then, obstacle avoidance behavior based on aggregation and disaggregation method is presented and the basic obstacle avoidance behaviors are designed. Finally, the simulation results are presented to show the feasibility of the proposed method.

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29

Chen, Yu-Ting, Chian-Song Chiu, and Ya-Ting Lee. "Grey Estimator-Based Tracking Controller Applied to Swarm Robot Formation." Axioms 10, no. 4 (November 9, 2021): 298. http://dx.doi.org/10.3390/axioms10040298.

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Mobile robots are widely used in many applications, while various types of mobile robots and their control researches have been proposed in literature. Since swarm robots have higher flexibility and capacity for teamwork, this paper presents a grey estimator-based tracking controller for formation trajectory tracking of swarm robots. First, wheel-type mobile robots are used and modeled for the controller design. Then, a grey dynamic estimator is developed to estimate the environmental disturbance and model uncertainty for linear feedback compensation. As a result, the asymptotic trajectory tracking is assured, so that the application on the swarm robot formation is achieved for a multi-agent system. The computational complexity is slightly reduced by the design. Finally, in order to verify the reliability of swarm robot formation, several types of formation are maintained by the grey estimator-based feedback linearization controller.

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30

Hamed, Oussama, Mohamed Hamlich, and Mohamed Ennaji. "Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (January 1, 2022): 159. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp159-171.

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The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.

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31

SATO, Kazuya, and Hirokazu TANAKA. "A Formation Control of Multi Robots Using Simple Control Structure." Transactions of the Society of Instrument and Control Engineers 50, no. 2 (2014): 125–31. http://dx.doi.org/10.9746/sicetr.50.125.

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32

Yoon, Suk-Min, Tae-Kyeong Yeu, Sup Hong, and Sang-Bong Kim. "Basic Control Algorithm for Parallel Formation of Multi-mining Robots." Ocean and Polar Research 36, no. 4 (December 30, 2014): 465–73. http://dx.doi.org/10.4217/opr.2014.36.4.465.

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33

Zhang, Shuai, Mingyong Liu, Xiaokang Lei, Yunke Huang, and Feihu Zhang. "Multi-target trapping with swarm robots based on pattern formation." Robotics and Autonomous Systems 106 (August 2018): 1–13. http://dx.doi.org/10.1016/j.robot.2018.04.008.

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34

Hamed, Oussama, and Mohamed Hamlich. "Hybrid Formation Control for Multi-Robot Hunters Based on Multi-Agent Deep Deterministic Policy Gradient." MENDEL 27, no. 2 (December 21, 2021): 23–29. http://dx.doi.org/10.13164/mendel.2021.2.023.

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Анотація:

The cooperation between mobile robots is one of the most important topics of interest to researchers, especially in the many areas in which it can be applied. Hunting a moving target with random behavior is an application that requires robust cooperation between several robots in the multi-robot system. This paper proposed a hybrid formation control for hunting a dynamic target which is based on wolves’ hunting behavior in order to search and capture the prey quickly and avoid its escape and Multi Agent Deep Deterministic Policy Gradient (MADDPG) to plan an optimal accessible path to the desired position. The validity and the effectiveness of the proposed formation control are demonstrated with simulation results.

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35

Elkilany, Basma Gh, A. A. Abouelsoud, Ahmed M. R. Fathelbab, and Hiroyuki Ishii. "Potential Field Method Parameters Tuning Using Fuzzy Inference System for Adaptive Formation Control of Multi-Mobile Robots." Robotics 9, no. 1 (February 25, 2020): 10. http://dx.doi.org/10.3390/robotics9010010.

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Nowadays, employing more than one single robot in complex tasks or dangerous environments is highly required. Thus, the formation of multi-mobile robots is an active field. One famous method for formation control is the Potential Field Method due to its simplicity and efficiency in dynamic environments. Therefore, we propose a Fuzzy Inference tuning of the potential field parameters to overcome its limitations. We implement the modified method with tuned parameters on MATLAB and apply it to three TurtleBot3 burger model robots. Then, several real-time experiments are carried out to confirm the applicability and validity of the modified potential filed method to achieve the robots’ tasks. The results assert that the TurtleBot3 robots can escape from a local minimum, pass through a narrow passage, and pass between two closely placed obstacles.

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36

Wan, Shuo, Jiaxun Lu, Pingyi Fan, and Khaled Letaief. "Information Theory in Formation Control: An Error Analysis to Multi-Robot Formation." Entropy 20, no. 8 (August 20, 2018): 618. http://dx.doi.org/10.3390/e20080618.

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Multi-robot formation control makes prerequisites for a team of robots to execute complex tasks cooperatively, which has been widely applied in both civilian and military scenarios. However, the limited precision of sensors and controllers may inevitably cause position errors in the finally achieved formation, which will affect the tasks undertaken. In this paper, the formation error is analyzed from the viewpoint of information theory. The desired position and the actually achieved position are viewed as two random variables. By calculating the mutual information between them, a lower bound of the formation error is derived. The results provide insights for the estimation of possible formation errors in the multi-robot system, which can assist designers to choose sensors and controllers with proper precision.

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37

Hichri, Bassem, Lounis Adouane, Jean-Christophe Fauroux, Youcef Mezouar, and Ioan Doroftei. "Flexible co-manipulation and transportation with mobile multi-robot system." Assembly Automation 39, no. 3 (August 5, 2019): 422–31. http://dx.doi.org/10.1108/aa-12-2017-190.

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Purpose The purpose of this paper is to address optimal positioning of a group of mobile robots for a successful manipulation and transportation of payloads of any shape. Design/methodology/approach The chosen methodology to achieve optimal positioning of the robots around the payload to lift it and to transport it while maintaining a geometric multi-robot formation is presented. This appropriate configuration of the set of robots is obtained by combining constraints ensuring stable and safe lifting and transport of the payload. A suitable control law is then used to track a virtual structure in which each elementary robot has to keep its desired position with respect to the payload. Findings An optimal positioning of mobile robots around a payload to ensure stable co-manipulation and transportation task according to stability multi-criteria constraints. Simulation and experimental results validate the proposed control architecture and strategy for a successful transportation task based on virtual structure navigation approach. Originality/value This paper presents a new strategy for co-manipulation and co-transportation task based on a virtual structure navigation approach. An algorithm for optimal positioning of mobile robots around a payload of any mass and shape is proposed while ensuring stability during the whole process by respecting multi-criteria task stability constraints.

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38

Wei, Heng, Qiang Lv, Nanxun Duo, GuoSheng Wang, and Bing Liang. "Consensus Algorithms Based Multi-Robot Formation Control under Noise and Time Delay Conditions." Applied Sciences 9, no. 5 (March 11, 2019): 1004. http://dx.doi.org/10.3390/app9051004.

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In recent years, the formation control of multi-mobile robots has been widely investigated by researchers. With increasing numbers of robots in the formation, distributed formation control has become the development trend of multi-mobile robot formation control, and the consensus problem is the most basic problem in the distributed multi-mobile robot control algorithm. Therefore, it is very important to analyze the consensus of multi-mobile robot systems. There are already mature and sophisticated strategies solving the consensus problem in ideal environments. However, in practical applications, uncertain factors like communication noise, communication delay and measurement errors will still lead to many problems in multi-robot formation control. In this paper, the consensus problem of second-order multi-robot systems with multiple time delays and noises is analyzed. The characteristic equation of the system is transformed into a quadratic polynomial of pure imaginary eigenvalues using the frequency domain analysis method, and then the critical stability state of the maximum time delay under noisy conditions is obtained. When all robot delays are less than the maximum time delay, the system can be stabilized and achieve consensus. Compared with the traditional Lyapunov method, this algorithm has lower conservativeness, and it is easier to extend the results to higher-order multi-robot systems. Finally, the results are verified by numerical simulation using MATLAB/Simulink. At the same time, a multi-mobile robot platform is built, and the proposed algorithm is applied to an actual multi-robot system. The experimental results show that the proposed algorithm is finally able to achieve the consensus of the second-order multi-robot system under delay and noise interference.

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39

Morozova, N. S. "Formation motion control for a multi-agent system simulating autonomous robots." Moscow University Computational Mathematics and Cybernetics 39, no. 4 (October 2015): 175–83. http://dx.doi.org/10.3103/s027864191504007x.

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40

KATO, Tatsuya, Keigo WATANABE, and Shoichi MAEYAMA. "2A2-B30 Formation Method for Multi-robot System with Heterogeneous Robots." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2010 (2010): _2A2—B30_1—_2A2—B30_4. http://dx.doi.org/10.1299/jsmermd.2010._2a2-b30_1.

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41

Ohashi, T., S. Ichikawa, and F. Hara. "2P1-F8 Study on multi-robots colony formation with local communication." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2001 (2001): 59. http://dx.doi.org/10.1299/jsmermd.2001.59_8.

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42

Liu, Andong, Rongchao Zhang, Wen-an Zhang, and You Teng. "Nash-optimization distributed model predictive control for multi mobile robots formation." Peer-to-Peer Networking and Applications 10, no. 3 (July 18, 2016): 688–96. http://dx.doi.org/10.1007/s12083-016-0479-7.

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43

Sarkar, Soumic, and Indra Narayan Kar. "Formation of multiple groups of mobile robots: multi-timescale convergence perspective." Nonlinear Dynamics 85, no. 4 (May 27, 2016): 2611–27. http://dx.doi.org/10.1007/s11071-016-2848-4.

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44

Zhang, Yu Li, Xiao Ping Ma, and Yan Zi Miao. "A Virtual Physics-Based Approach to Chemical Source Localization Using Mobile Robots." Applied Mechanics and Materials 263-266 (December 2012): 674–79. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.674.

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This paper presents a multi-robot cooperation strategy with virtual-physics force which includes three kinds of effort: structure formation force, goal force, and obstacle avoidant force. For swarm formation, a virtual robot is located at the center of the polygon. The innovative contribution of the strategy is that robots only having two kinds of forces: structure formation force and obstacle avoidant force while virtual robot having one kind of force: goal force. The motion of the virtual robot depends on the goal force which obtained from the sensor observations of the robots. Once the virtual robot moved to a new place, robots would also move with the forces acted on themselves as a single body and maintain a regular polygon formation. Simulation experiments are carried out, and the results show that the proposed strategy can effectively navigate the mobile robotics swarms to the chemical source in an indoor arena.

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45

Zhou, Meng, Zihao Wang, Jing Wang, and Zhe Dong. "A Hybrid Path Planning and Formation Control Strategy of Multi-Robots in a Dynamic Environment." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 3 (May 20, 2022): 342–54. http://dx.doi.org/10.20965/jaciii.2022.p0342.

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This paper proposes a hybrid path planning and formation control strategy for multi-robots in a dynamic environment. Under a leader-follower formation structure, the followers can track the motion of one leader after the leader’s path is determined. First, a hybrid path planning strategy that contains global path planning and local path planning of the leader is investigated, in which an improved hybrid grey wolf optimizer with whale optimizer algorithm (GWO-WOA) is designed for the global path planning in a given map, meanwhile, a dynamic window approach (DWA) is fused for the local path planning to avoid dynamic obstacles. Then, a leader-follower formation control algorithm is proposed for multiple mobile robots. The followers are controlled to track their corresponding virtual robots which are generated according to the leader’s position and the formation. Finally, simulation experiments are given to demonstrate the feasibility and effectiveness of the proposed algorithm in different environments.

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46

Pan, Zhenhua, Dongfang Li, Kun Yang, and Hongbin Deng. "Multi-Robot Obstacle Avoidance Based on the Improved Artificial Potential Field and PID Adaptive Tracking Control Algorithm." Robotica 37, no. 11 (April 16, 2019): 1883–903. http://dx.doi.org/10.1017/s026357471900033x.

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SummaryAs for the obstacle avoidance and formation control for the multi-robot systems, this paper presents an obstacle-avoidance method based on the improved artificial potential field (IAPF) and PID adaptive tracking control algorithm. In order to analyze the dynamics and kinematics of the robot, the mathematical model of the robot is built. Then we construct the motion situational awareness map (MSAM), which can map the environment information around the robot on the MSAM. Based on the MSAM, the IAPF functions are established. We employ the rotating potential field to solve the local minima and oscillations. As for collisions between robots, we build the repulsive potential function and priority model among the robots. Afterwards, the PID adaptive tracking algorithm is utilized to multi-robot formation control. To demonstrate the validity of the proposed method, a series of simulation results confirm that the approaches proposed in this paper can successfully address the obstacle- and collision-avoidance problem while reaching formation.

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47

Nazarova, A. V., and Huo Jianwen. "Application of Distributed Robotic Systems in Earthquakes: Search, Planning and Control Abstract." Mekhatronika, Avtomatizatsiya, Upravlenie 22, no. 11 (November 9, 2021): 577–84. http://dx.doi.org/10.17587/mau.22.577-584.

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In order to search and rescue injured during earthquake, we proposed a method for multi-robots motion planning and distributed control in this paper. At first, we have created two probabilistic search models to considering the search area and the characteristics of sensors, which we used to search the injured targets. And after finding the targets, they are assigned to the mobile robots on the land to afford emergency rescue. In order to reach to the targets, a path planning method based on map matching is proposed. There are three parts here. Firstly, to obtain the global and local map: continuous ground images are first collected using the UAV’s vision system, and subsequently, a global map of the ground environment is created by processing the collected images. The local map of the ground environment is obtained using the 2D laser radar sensor of the leader (UGA). Established the coordinate conversion relationship between UAV and UGV, unknown values during map matching are determined via the least square method. Secondly, our robots moved by group (leader-follower). The leader’s path was planned globally and locally. The other multi-robots moved along the path planned by the leader. Thirdly, in order to plan and coordinate the robots in the group, the finite state machine is used to describe the logical level of control system for each robot in the group. After that, at the tactical level of the control system, the movement control law of formation maintaining mode and formation switching mode is designed.

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48

MIYAMAE, Takehiro, Sumiaki ICHIKAWA, and Fumio HARA. "Stability Analysis of Circle Formation Generated by Multi-Robots with Local Vision." Transactions of the Japan Society of Mechanical Engineers Series C 69, no. 677 (2003): 156–63. http://dx.doi.org/10.1299/kikaic.69.156.

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49

T. Rashid, Abdulmuttalib, and Abduladhem A. Ali. "Polygon Shape Formation for Multi-Mobile Robots in a Local Knowledge Environment." Basrah journal of engineering science 19, no. 2 (June 1, 2019): 39–46. http://dx.doi.org/10.33971/bjes.19.2.6.

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50

Hadi, Sirojul, Muhammad Rivai, and Djoko Purwanto. "Leader-Follower Formation System of Multi-Mobile Robots for Gas Source Searching." Journal of Physics: Conference Series 1201 (May 2019): 012002. http://dx.doi.org/10.1088/1742-6596/1201/1/012002.

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