Position control of Robotino mobile robot using fuzzy logic

Oltean, Stelian-Emilian; Dulău, Mircea; Puskas, R.

doi:10.1109/aqtr.2010.5520855

Cited by 26 publications

(26 citation statements)

References 5 publications

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“…From 10 th rule to 15 th rule, we have the local minima situations, The T2FP controller uses fuzzy logic approach. Comparing with [30] and [31], where the authors have used the fuzzy logic approach to control the same mobile robot, our rule base size is significantly reduced (15 rules instead of 64 and 182 rules). IF-THEN rule for IT2FP controller can be expressed as following (the first rule of the table).…”

Section: Mobile Robot Controlmentioning

confidence: 94%

Adaptive navigation of an omni-drive autonomous mobile robot in unstructured dynamic environments

Melingui

Chettibi²,

Mbede

2013

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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One of the challenges of Autonomous Systems navigating in real world is to deal with the large amounts of uncertainties which are inherent in such environment while maintaining stability. Higher order Fuzzy Logic Systems (FLS), such as Interval Type-2 Fuzzy Logic Systems (IT2FLS), that use type-2 fuzzy sets, can model and handle such uncertainties, and give good performances that outperform their Type-1 counterparts. However, the complexity and computational time of type-reduction process which is strongly related to Membership Functions (MFs) structure and the number of fuzzy rules limit their applications to simple cases in real-time. Artificial Potential Field (APF) approach due to its elegant mathematical analysis, simplicity and its possibility to take into account the dynamic of the system is widely used for autonomous mobile robots navigation. However, the potential field introduced exhibits local minima other than at the goal position of the robot. In this paper, a new real-time navigation approach where we combine the APF and IT2FL approaches is developed and implemented for an omnidrive mobile robot navigating in dynamic unstructured environments. The novelty of the approach is the association of IT2FL to APF and the way in which the two approaches are hybridized (rule base size reduction). The experiments carried out on an omnidrive mobile robot named Robotino show the effectiveness of our approach.

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Section: Mobile Robot Controlmentioning

confidence: 94%

Adaptive navigation of an omni-drive autonomous mobile robot in unstructured dynamic environments

Melingui

Chettibi²,

Mbede

2013

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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“…Robotino is an omni-drive mobile robot platform which is produced by Festo [15][16]. It is shown in Fig.…”

Section: Festo Robotino Mobile Robotmentioning

confidence: 99%

“…Technical properties of the Robotino are listed in Table 1. The embedded micro-controller of Robotino has ROS (Robotic Operating System) and controls wireless communication, sensors, speed mode [15][16]. By using the encoder which is inside of 1.72AP version embedded software, x-y position and z-orientation can be easily obtained.…”

Section: Festo Robotino Mobile Robotmentioning

confidence: 99%

“…By using the encoder which is inside of 1.72AP version embedded software, x-y position and z-orientation can be easily obtained. Using the IEEE 802.11g standard, x-y coordinates and angular velocity information can be easily transmitted to Robotino [15][16]. RobotinoSim and RobotinoView software which are developed by Festo offer simulation environment for Robotino.…”

Section: Festo Robotino Mobile Robotmentioning

confidence: 99%

“…"robotinoAPI" and "robotinoIP" parameters can be various from Robotino versions [17]. Detailed information about Robotino can be found in [15][16][17]. Object detection algorithm which is consist of six steps was developed by using Matlab.…”

Section: Festo Robotino Mobile Robotmentioning

confidence: 99%

See 2 more Smart Citations

An Object Detection and Identification System for a Mobile Robot Control

Aydin¹,

Erdemir²

2017

Balkan Journal of Electrical and Computer Engineering

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The one of the features of mobile robot control is to detect and to identify objects in workspace. Especially, autonomous systems must detect obstacles and then revise actual trajectories according to new conditions. Hence, many solutions and approaches can be found in literature. Different sensors and cameras are used to solve problem by many researchers. Different type sensors usage can affect not only system performance but also operational cost. In this study, single camera based obstacle detection and identification algorithm was developed to control omni-drive mobile robot systems. Objects and obstacles, which are in robot view, are detected and identified their coordinates by using developed algorithms dynamically. Developed algorithm was tested on Festo Robotino mobile robot. Proposed approach offers not only cost efficiency but also short process time.

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