Control method for multi-robot system

Abstract

The present invention discloses a control method for multi-robot system, equitable distribution method of single resources based on fuzzy control, belonging to resources dynamic assignment management and quality of service management field of multi-robot system. In the method, providing an equitable high-efficiency resources distribution method based on competition application of every intelligentagent to sharing resources; simultaneity accelerating system implementing equitable distribution speed; the work output gain control parameter of the fuzzy controller in system is able to renovate dynamically in permissive range according to information of on-line searching and feedback real time, enhancing robust performance of the system and improving each competition applied QoS.

Description

Technical field [0001] The invention belongs to the control field of a multi-robot system, and specifically relates to a control method oriented to a multi-robot system. Background technique [0002] Research on multi-robot systems began in the 1970s. It is an emerging research field, which is still in its infancy. With the rapid development of network, automatic control and artificial intelligence technology, its research scope and content continue to deepen and expand. Facing the complex and unknown working environment and increasingly heavy tasks, the multi-robot system has great application prospects in the fields of military, aerospace, industrial manufacturing, natural exploration, disaster prevention and treatment, and service industry, attracting a large number of scholars Research interest in multi-robot systems. It has unparalleled advantages of a single robot, such as parallelism, flexibility, robustness, etc. Since there is information sharing and exchange in a networ...

AI Technical Summary

Problems solved by technology

Among them, the SRP fairness method can ensure high resource utilization and achieve average distribution. It can also ensure the stable performance of each node when the system is overloaded, but it is easy to cause network performance when resources are limited and congestion is prone to occur. bottleneck

Compared with other methods, the DWRR method has the advantages of being simple, easy to implement on hardware devices, providing good fairness and ensuring good QoS requirements for each data flow, etc., and has been widely used. Low latency requirements, but may cause large latency jitter, not suitable for real-time data flow

[0006] Another example is the quality-of-service-based Q-RAM resource allocation method proposed by foreign scholars Rajkumar et al. earlier. This method can achieve the optimal overall system effect and meet different applications under the condition that the minimum requirements of each application in the system are met. In the implementation process, it regards resource allocation as a general nonlinear or overall planning problem for offline processing, so it is also not practical for dynamic systems, and has weak ability to deal with uncertain situations in the system. Moreover, it cannot be used in low-performance embedded devices; Japan's Fumiko Harada proposed an adaptive allocation method for resources. Competitive applications in a resource-limited environment have a better effect on optimizing the system's overall fair service quality, and at the same time do not require QoS for each task. The prior value of the value, and realize the adaptive control of resources in the real-time system through real-time feedback. It is easy to use in low-performance embedded devices, so it has strong applicability, but it is in the process of implementing resource allocation. For gain control parameters The setting of is subject to many restrictions; at the same time, once this parameter is set, it will not change any more, but the real-time system is changing all the time, so in the case of new applications, this fixed parameter may not be suitable for the new environment; In terms of the speed of realizing multi-task fair scheduling, users are not satisfied with it; when under strong external interference, the system is not strong enough to deal with uncertainties, and the robustness is not good enough

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