Task Allocation of Multi-Robot Systems Based on a Novel Explosive Immune Evolutionary Algorithm

Yuan, Ming; Jiang, Ya Feng; Shen, Yi; Ye, Zhao Li; Wang, Qi

doi:10.4028/www.scientific.net/amm.246-247.331

Cited by 9 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Now, some scientists have developed robots for dismantling explosive and burning devices. The British "trolley" [2,3] series, the German "TEODOR" [4] series and the American "Andros" [5,6] series have been developed and applied in battlefields or terrorist activities. In China, there are also the "Lizards" [7,8] series of EOD robots, designed by the Institute of Automation of the Chinese Academy of Sciences, and the "Super-D" [9,10] series of EOD robots developed by Shanghai Jiao Tong University, equipped in various subway stations, parks and other densely populated areas.…”

Section: Introductionmentioning

confidence: 99%

Research on Kinematics Analysis and Trajectory Planning of Novel EOD Manipulator

Zhao

Han

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

To address the problems of mismatch, poor flexibility and low accuracy of ordinary manipulators in the complex special deflagration work process, this paper proposes a new five-degree-of-freedom (5-DOF) folding deflagration manipulator. Firstly, the overall structure of the explosion-expulsion manipulator is introduced. The redundant degrees of freedom are formed by the parallel joint axes of the shoulder joint, elbow joint and wrist pitching joint, which increase the flexibility of the mechanism. Aiming at a complex system with multiple degrees of freedom and strong coupling of the manipulator, the virtual joint is introduced, the corresponding forward kinematics model is established by D–H method, and the inverse kinematics solution of the manipulator is derived by analytical method. In the MATLAB platform, the workspace of the manipulator is analyzed by Monte Carlo pseudo-random number method. The quintic polynomial interpolation method is used to simulate the deflagration task in joint space. Finally, the actual prototype experiment is carried out using the data obtained by simulation. The trajectory planning using the quintic polynomial interpolation method can ensure the smooth movement of the manipulator and high accuracy of operation. Furthermore, the trajectory is basically consistent with the simulation trajectory, which can realize the work requirements of putting the object into the explosion-proof tank. The new 5-DOF folding deflagration manipulator designed in this paper has stable motion and strong robustness, which can be used for deflagration during the COVID-19 epidemic.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on Kinematics Analysis and Trajectory Planning of Novel EOD Manipulator

Zhao

Han

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The reason is that foraging comprises some interesting and sophisticated sub-problems such as energy efficiency [2][3][4][5][6] , path and motion planning [7][8][9][10][11][12] , coordination 10,[13][14][15][16] , communication [17][18][19][20][21][22][23][24][25] , optimization 3, 7-9, 26-31 and task allocation [32][33][34][35][36][37][38][39][40][41][42][43] . Although in most cases these sub-problems are not completely independent, there have been a focus on a particular sub-problem in different researches in swarm robotics field.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Capital Market in Task Allocation in Multi-robot Foraging

Akbarimajd

Simzan

2014

IJCIS

View full text Add to dashboard Cite

Because of high speed, efficiency, robustness and flexibility of multi-agent systems, in recent years there has been an increasing interest in the art of these systems. Artificial market mechanisms are one of the well-known negotiation multi-agent protocols in multi-agent systems. In this paper artificial capital market as a new variant of market mechanism is introduced and employed in a multi-robot foraging problem. In this artificial capital market, the robots are going to benefit via investment on some assets, defined as doing foraging task. Each investment has a cost and an outcome. Limited initial capital of the investors constrains their investments. A negotiation protocol is proposed for decision making of the agents. Qualitative analysis reveals speed of convergence, near optimal solutions and robustness of the algorithm. Numerical analysis shows advantages of the proposed method over two previously developed heuristics in terms of four performance criteria.

show abstract

“…The immune genetic algorithm (IGA) [9] is a novel optimization algorithm based on artificial immune theory, and is designed on the basic framework of genetic algorithm by combing with immune operators (such as antibody stimulation and suppression, vaccine extraction and inoculation, and so on. ), which can effectively enhance the search efficiency and search precision of genetic algorithm.…”

Section: Immune Genetic Optimization Algorithmmentioning

confidence: 99%

Multi-Robot Formation Control Based on Leader-Follower Optimized by the IGA

Huang¹,

Yuan²,

Lv³

et al. 2017

IOSR JCE

Self Cite

View full text Add to dashboard Cite

Task Allocation of Multi-Robot Systems Based on a Novel Explosive Immune Evolutionary Algorithm

Cited by 9 publications

References 0 publications

Research on Kinematics Analysis and Trajectory Planning of Novel EOD Manipulator

Research on Kinematics Analysis and Trajectory Planning of Novel EOD Manipulator

Application of Artificial Capital Market in Task Allocation in Multi-robot Foraging

Multi-Robot Formation Control Based on Leader-Follower Optimized by the IGA

Contact Info

Product

Resources

About