Particle filter localization and real time obstacle avoidance control based on 3D perception for wheelchair mobile robot

Baklouti, Emna; Amor, Nader Ben; Jallouli, Mohamed

doi:10.1109/is.2016.7737394

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Both simulation and real world data have been used to demonstrate the proposed decoupled Bayesian-based controller (DBC) with comparisons of the state-ofthe-art line of sight (LOS) controller [36], Bayes filter (BF) [21], particle filter based controller (PFC) [38], bio-inspired-learning of sensorimotor control (BSC) [39] and centralized Bayesian-based controller (CBC) [24].…”

Section: Resultsmentioning

confidence: 99%

“…To the best of our knowledge, little research has been done using MC algorithms directly for snake robot control due to the high DOFs and the challenging obstacle interaction problem. In order to compare the performance of the proposed DBC to the state-of-the-art, two more related approaches PFC [38] and BSC [39] were chosen. Although PFC was not originally designed for snake robot control, it can be implemented for the head link of the snake robot similar as BF.…”

Section: Simulation Analysismentioning

confidence: 99%

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A decoupled Bayesian method for snake robot control in unstructured environment

Jia,

2023

Bioinspir. Biomim.

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This paper presents a method which avoids the common practice of using a complex coupled snake robot model and performing kinematic analysis for control in cluttered environments. Instead, we introduce a completely decoupled dynamical Bayesian formulation with respect to interacted snake robot links and environmental objects, which requires much lower complexity for efficient and robust control. When a snake robot does not interact with obstacles, it runs by a simple serpenoid controller. However, when it exhibits interaction with environments, defined as close proximity or collision with targets and/or obstacles, we extend the conventional Bayesian framework by modeling such interactions in terms of stimuli. The proposed ‘multi-neural-stimulus function’ represents the cumulative effect of both external environmental influences and internal constraints of the snake robot. It implicitly handles the ‘unexpected collision’ problem and thus solve the difficult data association and shape adjustment problems for snake robot control in an innovative way. Preliminary experimental results have demonstrated promising performance of the proposed method comparing with the state-of-the-art.

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