In-tyre strain sensing of small non-pressurised tyres for mobile robotic applications

Lutfi, Ali Farooq Lutfi (2019) In-tyre strain sensing of small non-pressurised tyres for mobile robotic applications. [Thesis (PhD/Research)]

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Abstract

Small non-pressurised tyres are widely utilised for wheeled mobile robots. They possess several merits that justify their feasibility compared to pressurised tyres for small mobile vehicles. These tyres are relatively low cost, require less maintenance, and provide better traction. In addition, they can operate in harsh roads since they are invulnerable to punctures.

This research investigates the possibility of building a low-cost tyre deformation sensing system inside these tyres. In-tyre sensing provides direct and accurate measurements of the tyre-ground contact. The in-tyre measurements are analysed in this research to conclude features that are used to estimate the condition of the tyre and its surrounding. These features are employed to detect slip/spin incidents, identify obstacles, discriminate surfaces, and estimate the tyre’s properties, i.e., the forces, contact patch dimensions, angular velocity, and camber angle.

The novelty in this thesis is the aspect of addressing the in-tyre sensing for non-pressurised tyres. An in-tyre strain sensing system is designed and installed on the inner surface of the tyre. The strain profiles acquired from this system through static and dynamic tests are described and analysed.

Static tests were conducted on a custom-designed bench-top rig to evaluate the performance of the sensing system during slip incidents. These tests showed that the slip caused oscillations between two values in the strain reading. Dynamic tests were conducted on a second specially built bench-top rig to examine the in-tyre strain measurements for spin detection, obstacle identification, surface discrimination, and mechanical properties estimation. Spin incidents were indirectly detected utilising a cue in the strain waveforms. The waveforms contain a distinguishing segment that indicates reaching the track’s end on the rig. The spin was detected by calculating the number of the tyre’s revolutions relative to the beginning of this segment. In terms of obstacle identification, the shape of the strain waveform and the values of the strain were employed to detect hitting or passing over an obstacle. For surface identification, the results showed that the sensing system can indicate traversing rough surfaces. When the tyre moved from the control surface to a rough surface on the rig, the strain values increased considerably. The percentages of increase in strain values for three of the rough surfaces were 24.98 %, 51.20 %, and 52.13 %. The change in strain was much smaller when the tyre moved from the control surface to a smooth or liquid surface on the rig. The change in strain was between 1.73 % - 3.22 % for smooth surfaces and less than 1 % for liquid surfaces. Features were extracted from the strain profiles to estimate the tyre’s forces in three directions, the contact patch centre and length, the angular velocity, and the camber angle. Since the strain profiles of the non-pressurised tyres have not been previously modelled, the estimation models for pneumatic car tyres were utilised in this thesis. The estimations of the tyre’s mechanical properties by these models showed reasonable results. These results also showed that the estimation accuracy could be improved in the future by addressing the dynamic deformations of the non-pressurised tyres.

The results were verified qualitatively utilising the readings of the motor’s current sensor in the dynamic tests. Similar variations were evidently observed in the measurements of the strain sensors and the current sensor. In addition, the angular velocity estimations from the strain profiles were validated using the encoder’s measurements. The strain sensors provide acceptable estimations where the error was 3.98 %.

The proposed in-tyre strain sensing system can be utilised to complement the sensing modules in small mobile platforms. The features extracted from the measurements of this system can be employed to develop efficient control, localisation, and navigation systems.


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Item Type: Thesis (PhD/Research)
Item Status: Live Archive
Additional Information: Doctor of Philosophy (PhD) thesis.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Supervisors: Low, Tobias; Maxwell, Andrew
Date Deposited: 16 Oct 2020 04:28
Last Modified: 20 Apr 2021 23:58
Uncontrolled Keywords: in-tyre strain sensing, non-pressurised tyres, mobile robotic applications, slip/Spin detection, obstacle identification, surface discrimination
Fields of Research (2008): 11 Medical and Health Sciences > 1109 Neurosciences > 110906 Sensory Systems
09 Engineering > 0910 Manufacturing Engineering > 091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)
Fields of Research (2020): 32 BIOMEDICAL AND CLINICAL SCIENCES > 3209 Neurosciences > 320907 Sensory systems
40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400707 Manufacturing robotics
Identification Number or DOI: doi:10.26192/bjk2-5t81
URI: http://eprints.usq.edu.au/id/eprint/39917

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