Target tracking and obstacle avoidance for multi-agent systems

Yan, Jing; Guan, Xinping; Tan, Fuxiao

doi:10.1007/s11633-010-0539-z

Cited by 30 publications

(13 citation statements)

References 13 publications

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“…Pruning has been used both in Blackboard Architecture / Expert System and other domains. A conceptually simple example of pruning is its use in search space reduction for path planning [47], where unneeded paths are removed and not repeatedly considered. Early work on expert system pruning was performed by Giuliano and Jones [48].…”

Section: Optimizationmentioning

confidence: 99%

Using a blackboard architecture or expert system to identify obfuscated targets from symptoms

Straub

2015

SPIE Proceedings

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A variety of techniques exist for enhancing or inferring the existence and characteristics of an obscured or partially concealed target. Targets, however, may be completely blocked from view, presenting nothing to enhance and no image area to extend inferentially. Despite the difficulty, concealed (particularly intentionally) targets may be the most important to detect. This paper proposes a technique for using a Blackboard Architecture or Expert system to infer a target's existence from symptoms (maneuvers of other units, water and soil deformation, etc.) and discusses the differences between the two approaches (Blackboard Architecture and expert system) for doing so.

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Section: Optimizationmentioning

confidence: 99%

Using a blackboard architecture or expert system to identify obfuscated targets from symptoms

Straub

2015

SPIE Proceedings

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“…Laplacian matrix [13,14] . This matrix is used to describe the graph associated with information exchanges in a network of agents, and is defined as…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

Target tracking and obstacle avoidance for multi-agent networks with input constraints

Yan

Guan

Luo

et al. 2011

Int. J. Autom. Comput.

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In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach.

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“…The tracking problem is an important topic in the study of multiagent systems as stated in Luo et al (2010), Yan, Guan, and Tan (2010), Hong, Hu, and Gao (2006), Jiang, Liu, and Wang (2014). The objective of tracking is to design distributed control laws to drive the group of agents to reach the desired trajectory of a target agent.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of tracking is to design distributed control laws to drive the group of agents to reach the desired trajectory of a target agent. So far, most existing studies on the tracking problem focus on the case where agents asymptotically achieve agreement (Chen & Li, 2014;Dong, Chai, & Zhang, 2014;Dong, Chai, Zhang, & Nguang, 2014a, 2014bDong, Chai, Zhang, Nguang, & Wen, 2015;Hong et al, 2006;Wen, Duan, Chen, & Yu, 2014;Yan et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Finite interval tracking algorithm for nonlinear multi-agent systems with communication delays

Dong

Chai

Zhang

et al. 2015

International Journal of Systems Science

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We propose an iterative learning control (ILC) tracking strategy to solve the tracking problem of multi-agent systems with nonlinear dynamics and time-varying communication delays. The distributed tracking strategy, in which each tracking agent only utilises its own and neighbours' information, enables the tracking agents successfully track a maneuvering target in a finite time interval although with presence of time delays. Compared with the existing related work, the quantitative relationship between the boundary of tracking errors and the estimation of time delays is derived. Furthermore, in many practical control problems, identical initialisation condition may not be satisfied, which is called initial-shift problem. Hence, a forgetting factor is introduced to deal with that problem. It is proved that the presented results are effective via conducting numerical examples.

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Target tracking and obstacle avoidance for multi-agent systems

Cited by 30 publications

References 13 publications

Using a blackboard architecture or expert system to identify obfuscated targets from symptoms

Using a blackboard architecture or expert system to identify obfuscated targets from symptoms

Target tracking and obstacle avoidance for multi-agent networks with input constraints

Finite interval tracking algorithm for nonlinear multi-agent systems with communication delays

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