Neural network and fuzzy logic techniques based collision avoidance for a mobile robot

Zhang, Minglu; Peng, Shangxian; Meng, Qinghao

doi:10.1017/s0263574797000751

Cited by 15 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…= min for rules of Mamdani type. The membership function of the obstacle's position and distance from the robot is computed by the max-min composition [43] between the sensor readings, which represent the distance from the obstacles and the fuzzy relation described by (2). The second layer fuzzy controller, shown in Fig.…”

Section: B Fuzzy Logic Inference Enginementioning

confidence: 99%

Autonomous vehicle navigation utilizing electrostatic potential fields and fuzzy logic

Tsourveloudis

Valavanis

Hebert

2001

IEEE Trans. Robot. Automat.

129

View full text Add to dashboard Cite

An electrostatic potential field (EPF) path planner is combined with a two-layered fuzzy logic inference engine and implemented for real-time mobile robot navigation in a 2-D dynamic environment. The environment is first mapped into a resistor network; an electrostatic potential field is then created through current injection into the network. The path of maximum current through the network corresponds to the approximately optimum path in the environment. The first layer of the fuzzy logic inference engine performs sensor fusion from sensor readings into a fuzzy variable, collision, providing information about possible collisions in four directions, front, back, left, and right. The second layer guarantees collision avoidance with dynamic obstacles while following the trajectory generated by the electrostatic potential field. The proposed approach is experimentally tested using the Nomad 200 mobile robot.

show abstract

Section: B Fuzzy Logic Inference Enginementioning

confidence: 99%

Autonomous vehicle navigation utilizing electrostatic potential fields and fuzzy logic

Tsourveloudis

Valavanis

Hebert

2001

IEEE Trans. Robot. Automat.

129

View full text Add to dashboard Cite

show abstract

“…Autonomous navigation of robots has a large number of application areas such as automatic driving, transporting objects in factory or office environments, and unmanned exploration of dangerous regions, see e.g. [Antonelo et al, 2006;Zhang et al, 1997;Guivant et al, 2000]. Roughly speaking, autonomous navigation of mobile robots can be based on prior path planning or as a direct mapping of sensory input to actions [Antonelo et al, 2008].…”

Section: Introductionmentioning

confidence: 99%

Training a Network of Electronic Neurons for Control of a Mobile Robot

Vromen

Steur

Nijmeijer

2016

Int. J. Bifurcation Chaos

View full text Add to dashboard Cite

An adaptive training procedure is developed for a network of electronic neurons, which controls a mobile robot driving around in an unknown environment while avoiding obstacles. The neuronal network controls the angular velocity of the wheels of the robot based on the sensor readings. The nodes in the neuronal network controller are clusters of neurons rather than single neurons. The adaptive training procedure ensures that the input–output behavior of the clusters is identical, even though the constituting neurons are nonidentical and have, in isolation, nonidentical responses to the same input. In particular, we let the neurons interact via a diffusive coupling, and the proposed training procedure modifies the diffusion interaction weights such that the neurons behave synchronously with a predefined response. The working principle of the training procedure is experimentally validated and results of an experiment with a mobile robot that is completely autonomously driving in an unknown environment with obstacles are presented.

show abstract

“…Autonomy for mobile robots implies the ability of the robot to react to static obstacles and unpredictable dynamic events [1]. Autonomous navigation is involved in applications such as automatic driving, surveillance, exploration, guidance for the blind and disabled, exploration of dangerous or hostile regions, transportation, and collecting geographical information in unknown terrains like unmanned exploration of a new planetary surface [2,3].…”

Section: Introductionmentioning

confidence: 99%

Virtual force field algorithm for a behaviour-based autonomous robot in unknown environments

Nia

Tang

Karasfi

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

The present paper describes a real-time motion-planning approach which lies in the integration of three techniques: fuzzy logic (FL), virtual force field (VFF), and boundary following (BF). The FL algorithm is used for velocity control based on sonar readings. The path-planning algorithm is based on the VFF and BF methods. The proposed navigation system differs from previous works in terms of using different algorithms for planning robot motion. Other improvements concern functional and computational aspects of the design and integration of the modules. The robot shows robust performance in complex situations and local minimum scenarios. Simulation results show the effectiveness of the developed system in various environments with long walls, U-shaped, maze-like, and other types of clutter.

show abstract

Neural network and fuzzy logic techniques based collision avoidance for a mobile robot

Cited by 15 publications

References 11 publications

Autonomous vehicle navigation utilizing electrostatic potential fields and fuzzy logic

Autonomous vehicle navigation utilizing electrostatic potential fields and fuzzy logic

Training a Network of Electronic Neurons for Control of a Mobile Robot

Virtual force field algorithm for a behaviour-based autonomous robot in unknown environments

Contact Info

Product

Resources

About