Target tracking by grey prediction theory and look-ahead fuzzy logic control

Luo, Ren C.; Chen, Tse Min

doi:10.1109/robot.1999.772521

Cited by 25 publications

(12 citation statements)

References 6 publications

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“…The algorithms offuzzy logic in robotics can be classified according to the particular robotic tasks and hierarchical control levels [364], [347], [319]: a) Tracking control of robot trajectories [232], [270], [255], [117], [298], [216], [348], [277], [161], [336], [292], [394] -executive (adaptation) control level b) Force control and hybrid position/force control for robotic contact tasks [350], [301], [ 111 ], [162], [303], [221 ], [129] -executive (adaptation) control level c) Motion control of mobile robots and autonomous unmanned vehicles [142], [175], [141], [332], [7], [382], [391], [50], [120], [227], [100], [346], [286], [99], [374] , [265] , [6], [252], [228], [67], [299] -executive (adaptation), tactical (skill) and strategical (learning) control level d) Control of locomotion, rehabilitation and special robotic systems …”

Section: Fuzzy Algorithms In Roboticsmentioning

confidence: 99%

Intelligent Control of Robotic Systems

Katić¹,

Vukobratović²

2003

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Section: Fuzzy Algorithms In Roboticsmentioning

confidence: 99%

Intelligent Control of Robotic Systems

Katić¹,

Vukobratović²

2003

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“…To overcome the drawbacks of MM-based algorithms, in this paper, we incorporate the grey prediction [30,31], which is a modelfree method and requires no a priori dynamic model of target, into the standard particle filter (SPF) for maneuvering target tracking. The proposed grey prediction based particle filter (GP-PF) has both the inherent advantages of model-based and model-free system, and thus can improve the maneuvering target tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Grey Prediction Based Particle Filter for Maneuvering Target Tracking

Chen¹,

Shi²,

Hong³

et al. 2009

PIER

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Abstract-For maneuvering target tracking, we propose a novel grey prediction based particle filter (GP-PF), which incorporates the grey prediction algorithm into the standard particle filter (SPF). The basic idea of the GP-PF is that new particles are sampled by both the state transition prior and the grey prediction algorithm. Since the grey prediction algorithm is a kind of model-free method and is able to predict the system state based on historical measurements other than establishing a priori dynamic model, the GP-PF can significantly alleviate the sample degeneracy problem which is common in SPF, especially when it is used for maneuvering target tracking. Simulations are conducted in the context of two typical maneuvering motion scenarios and the results indicate that the overall performance of the proposed GP-PF is better than the SPF and the multiple model particle filter (MMPF) when the tracking accuracy, computational complexity and tracking lost probability are considered. The performance improvements can be attributed to that the GP-PF has both model-based and model-free features.

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“…(2-1) is called "white descriptor" for modeling a white system that we can find its parameters (a, b) directly from the observed system outputs. However, to estimate the parameters of the partial-known system or called "grey system", it is approximated by the following grey-differential equation [8]:…”

Section: The Grey-fuzzy Motion Decision-making Algorithmmentioning

confidence: 99%

“…To derive the motion velocities we consider two inputs, one is the error value of the difference between predicted distance and the desired distance D from target to robot: (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) and the other input is the error derivative:…”

Section: X+(t) : the Predicted Position Of Mobile Robot At T+l Calcumentioning

confidence: 99%

“…The original grey-fuzzy motion decision-making (GFMD) algorithm [8] combines an adaptive algorithm for target position prediction based on the Grey theory [9, 101 and a look-ahead fuzzy logic controller for the control of the tracking robot. The predictor assumes the intemal structure, parameters, and characteristics of an observed system are unknown in a priori or called "black system".…”

Section: Introductionmentioning

confidence: 99%

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Mobile target tracking using hierarchical grey-fuzzy motion decision-making method

Chen

Luo²

Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (C

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This paper presents a hierarchical grey-fuzzy motion decision-making (HGFMD) algorithm, which is capable of integrating multiple sequential data for decision making and for the design of the control kernel of the target tracking system. The algorithm combines multiple grey prediction modules, each of them can on-line estimate the suitable model from sequential sensory information to approximate the observed dynamic system model for future-trend prediction, and for decision making through a multilayered fuzzy logic inference engine. We have designed the HGFMD controller for target tracking system and implemented in our autonomous mobile robot "Chung Cheng-1". The HGFMD is compared with the conventional fuzzy logic controller, multilayered fuzzy controller, and the original grey-fuzzy controller we have developed previously in various target-tracking experiments. We have demonstrated the high reliability of the HGFMD controller and tracking system even encountering the uncertain status of slow sensory response time and the nonlinear motion behaviors of the target.

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Target tracking by grey prediction theory and look-ahead fuzzy logic control

Cited by 25 publications

References 6 publications

Intelligent Control of Robotic Systems

Intelligent Control of Robotic Systems

Grey Prediction Based Particle Filter for Maneuvering Target Tracking

Mobile target tracking using hierarchical grey-fuzzy motion decision-making method

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