A Distributed Self-healing Algorithm for Global Optimal Movement Synchronization of Multi-robot Formation Network

DCOP (Distributed Constraint Optimization Problem) is currently the most widely used in multi-robot communication algorithm problem, Adopt algorithm is a kind of algorithm which is relatively perfect; it can transport messages accurately under the harsh environment of communication. It is completely asynchronous algorithm, through analyzing algorithm for local agents to obtain the global optimal solution, in the comparison of each algorithm can see that ADOPT algorithm not only can well solve the problem of communication failure, and the algorithm set up mechanism of terminate detection, it can make the algorithm stops on the optimal solution, and effectively solve the deadlock problem. The text use ADOPT algorithm to analyze how to solve the problem of multi-robot communication failure, and improve the algorithm to take messages more accurately and effectively.

Download Full-text

A multi-robot allocation model for multi-object based on Global Optimal Evaluation of Revenue

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211060650 ◽

2021 ◽

Vol 18 (6) ◽

pp. 172988142110606

Author(s):

Xun Li ◽

Zhi Zhang ◽

Dan-Dan Wu ◽

Michel Medema ◽

Alexander Lavozik

Keyword(s):

Numerical Simulation ◽

Task Characteristics ◽

Allocation Model ◽

Object Based ◽

Optimal Value ◽

Fixed Set ◽

Finite Set ◽

Global Optimal ◽

Optimal Evaluation ◽

Multi Robot

The problem of global optimal evaluation for multi-robot allocation has gained attention constantly, especially in a multi-objective environment, but most algorithms based on swarm intelligence are difficult to give a convergent result. For solving the problem, we established a Global Optimal Evaluation of Revenue method of multi-robot for multi-tasks based on the real textile combing production workshop, consumption, and different task characteristics of mobile robots. The Global Optimal Evaluation of Revenue method could traversal calculates the profit of each robot corresponding to different tasks with global traversal over a finite set, then an optimization result can be converged to the global optimal value avoiding the problem that individual optimization easy to fall into local optimal results. In the numerical simulation, for fixed set of multi-object and multi-task, we used different numbers of robots allocation operation. We then compared with other methods: Hungarian, the auction method, and the method based on game theory. The results showed that Global Optimal Evaluation of Revenue reduced the number of robots used by at least 17%, and the delay time could be reduced by at least 16.23%.

Download Full-text

A comparative study of dynamical sequential and global optimal task reallocation methodology for distributed multi-robot system

2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) ◽

10.1109/urai.2011.6145982 ◽

2011 ◽

Author(s):

Guanghui Li ◽

Y. Tamura ◽

H. Asama

Keyword(s):

Comparative Study ◽

Robot System ◽

Global Optimal ◽

Task Reallocation ◽

Multi Robot

Download Full-text

Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms

Intelligent Service Robotics ◽

10.1007/s11370-021-00393-4 ◽

2021 ◽

Author(s):

J. G. Martin ◽

J. R. D. Frejo ◽

R. A. García ◽

E. F. Camacho

Keyword(s):

Genetic Algorithm ◽

Power Plant ◽

Branch And Bound ◽

Task Allocation ◽

Optimal Solution ◽

Computation Time ◽

Global Optimal Solution ◽

Global Optimal ◽

Robot Task ◽

Multi Robot

AbstractThe paper proposes the formulation of a single-task robot (ST), single-robot task (SR), time-extended assignment (TA), multi-robot task allocation (MRTA) problem with multiple, nonlinear criteria using discrete variables that drastically reduce the computation burden. Obtaining an allocation is addressed by a Branch and Bound (B&B) algorithm in low scale problems and by a genetic algorithm (GA) specifically developed for the proposed formulation in larger scale problems. The GA crossover and mutation strategies design ensure that the descendant allocations of each generation will maintain a certain level of feasibility, reducing greatly the range of possible descendants, and accelerating their convergence to a sub-optimal allocation. The proposed MRTA algorithms are simulated and analyzed in the context of a thermosolar power plant, for which the spatially distributed Direct Normal Irradiance (DNI) is estimated using a heterogeneous fleet composed of both aerial and ground unmanned vehicles. Three optimization criteria are simultaneously considered: distance traveled, time required to complete the task and energetic feasibility. Even though this paper uses a thermosolar power plant as a case study, the proposed algorithms can be applied to any MRTA problem that uses a multi-criteria and nonlinear cost function in an equivalent way. The performance and response of the proposed algorithms are compared for four different scenarios. The results show that the B&B algorithm can find the global optimal solution in a reasonable time for a case with four robots and six tasks. For larger problems, the genetic algorithm approaches the global optimal solution in much less computation time. Moreover, the trade-off between computation time and accuracy can be easily carried out by tuning the parameters of the genetic algorithm according to the available computational power.

Download Full-text

Research on the Multi-Robot Cooperative Pursuit Strategy Based on the Zero-Sum Game and Surrounding Points Adjustment

Machines ◽

10.3390/machines9090187 ◽

2021 ◽

Vol 9 (9) ◽

pp. 187

Author(s):

Gang Chen ◽

Wenqian Xu ◽

Zixing Li ◽

Yuqiang Liu ◽

Xin Liu

Keyword(s):

Success Rate ◽

Game Model ◽

Simulation Experiments ◽

Pursuit Problem ◽

Optimal Movement ◽

Two Stages ◽

Zero Sum ◽

Robot Position ◽

Pursuit Strategy ◽

Multi Robot

Making full use of the cooperation of multi-robots can improve the success rate of apursuit task. Therefore, this paper proposes a multi-robot cooperative pursuit strategy based on the zero-sum game and surrounding points adjustment. First, a mathematical description of the multi-robot pursuit problem is constructed, and the zero-sum game model is established considering the cooperation of the pursuit robots and the confrontation between the pursuit robots and the escape robot. By solving the game model, the optimal movement strategies of the pursuit robots and the escape robot are obtained. Then, the position adjustment method of the pursuit robots is studied based on the Hungarian algorithm, and the pursuit robots are controlled to surround the escape robot. Based on this, a multi-robot cooperative pursuit strategy is proposed that divides the pursuit process into two stages: pursuit robot position adjustment and game pursuit. Finally, the correctness and effectiveness of the multi-robot cooperative pursuit strategy are verified with simulation experiments. The multi-robot cooperative pursuit strategy allows the pursuit robots to capture the escape robot successfully without conflicts among the pursuit robots. It can be seen from the documented simulation experiments that the success rate of the pursuit task using the strategy proposed in this paper is 100%.

Download Full-text

Autonomous self-healing polyisoprene elastomers with high modulus and good toughness based on the synergy of dynamic ionic crosslinks and highly disordered crystals

Polymer Chemistry ◽

10.1039/d0py01034k ◽

2020 ◽

Vol 11 (41) ◽

pp. 6549-6558

Author(s):

Yohei Miwa ◽

Mayu Yamada ◽

Yu Shinke ◽

Shoichi Kutsumizu

Keyword(s):

Mechanical Properties ◽

Room Temperature ◽

Self Healing ◽

High Modulus ◽

Disordered Crystals ◽

Ionic Crosslinks

We designed a novel polyisoprene elastomer with high mechanical properties and autonomous self-healing capability at room temperature facilitated by the coexistence of dynamic ionic crosslinks and crystalline components that slowly reassembled.

Download Full-text