scholarly journals A Control Strategy for Multi-Robot System Navigating in a Dynamic Environment

2015 ◽  
Vol 03 (11) ◽  
pp. 99-105
Author(s):  
Ting Wang ◽  
Ryad Chellali ◽  
Yang Yi ◽  
Wen Qin ◽  
Mingzhu Wei
Keyword(s):  
Control Strategy ◽  
Dynamic Environment ◽  
Robot System ◽  
Multi Robot

scholarly journals Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

2021 ◽  
Vol 50 (2) ◽  
pp. 357-374
Author(s):  
Novak Zagradjanin ◽  
Aleksandar Rodic ◽  
Dragan Pamucar ◽  
Bojan Pavkovic
Keyword(s):  
Path Planning ◽  
High Efficiency ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Dynamic Environment ◽  
Cloud Services ◽  
Robot System ◽  
Inference System ◽  
Crowded Environment ◽  
Multi Robot

This paper considers an autonomous cloud-based multi-robot system designed to execute highly repetitive tasksin a dynamic environment such as a modern megastore. Cloud level is intended for performing the most demandingoperations in order to unload the robots that are users of cloud services in this architecture. For path planningon global level D* Lite algorithm is applied, bearing in mind its high efficiency in dynamic environments. In orderto introduce smart cost map for further improvement of path planning in complex and crowded environment, implementationof fuzzy inference system and learning algorithm is proposed. The results indicate the possibility ofapplying a similar concept in different real-world robotics applications, in order to reduce the total paths length,as well as to minimize the risk in path planning related to the human-robot interactions.

Multi-Robot Cooperative Transportation of Objects Using Modified Q-Learning

2010 ◽  
Author(s):  
Pallege Gamini Dilupa Siriwardana ◽  
Clarence de Silva
Keyword(s):  
Autonomous Robots ◽  
Dynamic Environment ◽  
Learning Approach ◽  
Robot System ◽  
Q Learning ◽  
Time Performance ◽  
Object Pose Estimation ◽  
Object Transportation ◽  
Blob Tracking ◽  
Multi Robot

In cooperative multi-robot object transportation, several autonomous robots navigate cooperatively in either a static or a dynamic environment to transport an object to a goal location and orientation. The environment may consist of both fixed and removable obstacles and it will be subject to uncertainty and unforeseen changes within the environment. More than one robot may be required for handling heavy and large objects. This paper presents a modified Q-learning approach for object transportation utilizing cooperative and autonomous multiple mobile robots. A modified version of Q-learning is presented, which employs for effective robot coordination. A solution to the action selection conflicts of the robots is presented, which helps to improve the real time performance and robustness of the system. As required in the task, the paper presents an algorithm for object pose estimation, by utilizing the laser range finder and color blob tracking. The developed techniques are implemented in a multi-robot system in laboratory. Experimental results are presented to demonstrate the effectiveness of the developed multi-robot system and its underlying methodologies.

scholarly journals Adaptive Virtual Impedance Control of a Mobile Multi-Robot System

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 19
Author(s):  
Duanne Engelbrecht ◽  
Nico Steyn ◽  
Karim Djouani
Keyword(s):  
Impedance Control ◽  
Lower Layer ◽  
Dynamic Environment ◽  
Robot System ◽  
Collaborative Robots ◽  
Dynamic Obstacle Avoidance ◽  
Movement Stability ◽  
Dynamic Obstacle ◽  
Virtual Impedance ◽  
Multi Robot

The capabilities of collaborative robotics have transcended the conventional abilities of decentralised robots as it provides benefits such as scalability, flexibility and robustness. Collaborative robots can operate safely in complex human environments without being restricted by the safety cages or barriers that often accompany them. Collaborative robots can be used for various applications such as machine tending, packaging, process tasks and pick and place. This paper proposes an improvement of the current virtual impedance algorithm by developing an adaptive virtual impedance controlled mobile multi-robot system focused on dynamic obstacle avoidance with a controlled planar movement. The study includes the development of a mobile multi-robot platform whereby each robot plans a path individually without a supervisor. The proposed system would implement a two-layered hierarchy for robot path planning. The higher layer generates a trajectory from the current position to the desired position, and the lower layer develops a real-time strategy to follow the generated trajectory while avoiding static and dynamic obstacles. The key contribution of this paper is the adaptive virtual impedance controller for a multi-robot system that will maintain movement stability and improve the motion behaviour in a dynamic environment.

scholarly journals Reinforcement-Learning-Based Asynchronous Formation Control Scheme for Multiple Unmanned Surface Vehicles

2021 ◽  
Vol 11 (2) ◽  
pp. 546
Author(s):  
Jiajia Xie ◽  
Rui Zhou ◽  
Yuan Liu ◽  
Jun Luo ◽  
Shaorong Xie ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Formation Control ◽  
Rapid Development ◽  
Gradient Algorithm ◽  
Robot System ◽  
Physical Relationship ◽  
Unmanned Surface Vehicles ◽  
Main Challenge ◽  
Control Scheme ◽  
Multi Robot

The high performance and efficiency of multiple unmanned surface vehicles (multi-USV) promote the further civilian and military applications of coordinated USV. As the basis of multiple USVs’ cooperative work, considerable attention has been spent on developing the decentralized formation control of the USV swarm. Formation control of multiple USV belongs to the geometric problems of a multi-robot system. The main challenge is the way to generate and maintain the formation of a multi-robot system. The rapid development of reinforcement learning provides us with a new solution to deal with these problems. In this paper, we introduce a decentralized structure of the multi-USV system and employ reinforcement learning to deal with the formation control of a multi-USV system in a leader–follower topology. Therefore, we propose an asynchronous decentralized formation control scheme based on reinforcement learning for multiple USVs. First, a simplified USV model is established. Simultaneously, the formation shape model is built to provide formation parameters and to describe the physical relationship between USVs. Second, the advantage deep deterministic policy gradient algorithm (ADDPG) is proposed. Third, formation generation policies and formation maintenance policies based on the ADDPG are proposed to form and maintain the given geometry structure of the team of USVs during movement. Moreover, three new reward functions are designed and utilized to promote policy learning. Finally, various experiments are conducted to validate the performance of the proposed formation control scheme. Simulation results and contrast experiments demonstrate the efficiency and stability of the formation control scheme.

scholarly journals Research Progress on Synergistic Technologies of Agricultural Multi-Robots

2021 ◽  
Vol 11 (4) ◽  
pp. 1448
Author(s):  
Wenju Mao ◽  
Zhijie Liu ◽  
Heng Liu ◽  
Fuzeng Yang ◽  
Meirong Wang
Keyword(s):  
Path Planning ◽  
Formation Control ◽  
Research Progress ◽  
Hybrid Architecture ◽  
Labor Costs ◽  
Robot System ◽  
System Architectures ◽  
Research Results ◽  
Robot Systems ◽  
Multi Robot

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.

scholarly journals Optically tracked multi-robot system for keyhole neurosurgery

2011 ◽  
Author(s):  
Mirko Daniele Comparetti ◽  
Elena De Momi ◽  
Alberto Vaccarella ◽  
Matthias Riechmann ◽  
Giancarlo Ferrigno
Keyword(s):  
Robot System ◽  
Multi Robot
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