Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems

Mbede, Jean Bosco; Ele, Pierre; Mveh-Abia, Chantal-Marguerite; Touré, Youssoufi; Graefe, Volker; Ma, Shugen

doi:10.1016/j.ins.2004.09.014

Cited by 46 publications

(28 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simulated results showed that the mobile manipulator could be controlled to retain any end-effector pose, regardless of the external, applied-force direction. In 2005, Mbede et al [73] applied a robust, adaptive, fuzzy, reactive motion-planning algorithm for mobile manipulator navigation. The system included an incomplete mathematical robot system model and sensor data uncertainties.…”

Section: Miscellaneousmentioning

confidence: 99%

Survey of Research for Performance Measurement of Mobile Manipulators

Bostelman¹,

Hong²,

Marvel³

2016

J. RES. NATL. INST. STAN.

View full text Add to dashboard Cite

This survey provides the basis for developing research in the area of mobile manipulator performance measurement, an area that has relatively few research articles when compared to other mobile manipulator research areas. The survey provides a literature review of mobile manipulator research with examples of experimental applications. The survey also provides an extensive list of planning and control references as this has been the major research focus for mobile manipulators which factors into performance measurement of the system. The survey then reviews performance metrics considered for mobile robots, robot arms, and mobile manipulators and the systems that measure their performance, including machine tool measurement systems through dynamic motion tracking systems. Lastly, the survey includes a section on research that has occurred for performance measurement of robots, mobile robots, and mobile manipulators beginning with calibration, standards, and mobile manipulator artifacts that are being considered for evaluation of mobile manipulator performance.

show abstract

Section: Miscellaneousmentioning

confidence: 99%

Survey of Research for Performance Measurement of Mobile Manipulators

Bostelman¹,

Hong²,

Marvel³

2016

J. RES. NATL. INST. STAN.

View full text Add to dashboard Cite

show abstract

“…Fujimori and Tani presented the collision avoidance in cooperative multi-robot system for avoiding moving obstacles and for navigating to the goal [19]. Mbede et al combined a probabilistic map as a global planner and fuzzy reactive (local) planner for autonomous navigation and avoiding obstacles [20]. Sabourin and Madani presented obstacle avoidance strategy for biped robot using fuzzy Q-learning method [21].…”

Section: Related Workmentioning

confidence: 99%

Adaptive behaviors of reactive mobile robot with Bayesian inference in nonstationary environments

Min

Cho

2009

Appl Intell

View full text Add to dashboard Cite

This paper presents a technique for a reactive mobile robot to adaptively behave in unforeseen and dynamic circumstances. A robot in nonstationary environments needs to infer how to adaptively behave to the changing environment. Behavior-based approach manages the interactions between the robot and its environment for generating behaviors, but in spite of its strengths of fast response, it has not been applied much to more complex problems for highlevel behaviors. For that reason many researchers employ a behavior-based deliberative architecture. This paper proposes a 2-layer control architecture for generating adaptive behaviors to perceive and avoid moving obstacles as well as stationary obstacles. The first layer is to generate reflexive and autonomous behaviors with behavior network, and the second layer is to infer dynamic situations of the mobile robot with Bayesian network. These two levels facilitate a tight integration between high-level inference and low-level behaviors. Experimental results with various simulations and a real robot have shown that the robot reaches the goal points while avoiding stationary or moving obstacles with the proposed architecture.

show abstract

“…Godjevac and Steele (1999) discussed a radial basis function based fuzzy controller for a mobile robot that learns faster than conventional error back-propagation style networks, and also has the advantage of being able to express the input-output transformation in the form of fuzzy inference rules. Mbede, Huang and Wang (2003) and Mbede et al (2005) presented a robust neuro-fuzzy controller for robot manipulators working in environments with moving obstacles. They showed how any perturbations in the system dynamics could automatically be compensated for by the controller.…”

Section: Fuzzy Neural Network Controlmentioning

confidence: 99%