Soft modelling and fuzzy logic control of wheeled skid-steer electric vehicles with steering prioritisation

Colyer, R.E.; Economou, J.T.

doi:10.1016/s0888-613x(99)00019-5

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…For a rover, such speed modulation allows management of traction by mitigating the risk of wheel slippage. The approach is similar in spirit to other fuzzy logic and dynamic feedback control methods [16,17] proposed for appropriately distributing wheel motor torques to improve traction, albeit, after the onset of wheel slip.…”

Section: Sensing and Perception Issuesmentioning

confidence: 96%

Approximate reasoning for safety and survivability of planetary rovers

Tunstel

Howard

2003

Fuzzy Sets and Systems

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Section: Sensing and Perception Issuesmentioning

confidence: 96%

Approximate reasoning for safety and survivability of planetary rovers

Tunstel

Howard

2003

Fuzzy Sets and Systems

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“…For a rover, such speed modulation allows management of traction by mitigating the risk of wheel slippage. The approach is similar in spirit to other fuzzy logic and dynamic feedback control methods (Sarkar & Yun, 1998;Colyer & Economou, 1999) proposed for appropriately distributing wheel motor torques to improve traction, albeit after the onset of wheel slippage.…”

Section: Sensing and Perception Issuesmentioning

confidence: 96%

Rule–based reasoning and neural network perception for safe off–road robot mobility

Tunstel¹,

Howard

Seraji

2002

Expert Systems

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Operational safety and health monitoring are critical matters for autonomous field mobile robots such as planetary rovers operating on challenging terrain. This paper describes relevant rover safety and health issues and presents an approach to maintaining vehicle safety in a mobility and navigation context. The proposed rover safety module is composed of two distinct components: safe attitude (pitch and roll) management and safe traction management. Fuzzy logic approaches to reasoning about safe attitude and traction management are presented, wherein inertial sensing of safety status and vision-based neural network perception of terrain quality are used to infer safe speeds of traversal. Results of initial field tests and laboratory experiments are also described. The approach provides an intrinsic safety cognizance and a capacity for reactive mitigation of robot mobility and navigation risks.

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“…The package contains a user-guide describing this, and in fact every process, the plotting tool is very simple but also very powerful and allows the user to easily interpret the results. Each individual component was partially designed from the equations described by Gillespie [4] and Economou et al in [5] and was built up into a module, each module was then test simulated with test values to ensure it was running accurately. Once a module in a set was completed it was tested with other relevant modules to ensure compatibility between modules.…”

Section: All-electric Vehicle Dynamicsmentioning

confidence: 99%

Multi-input all-electric vehicle design using a forward simulation software approach

Richardson

Economou

Luk

2003

2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484)

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This paper describes the modular based system for the design and testing of electric vehicles with user defined physical inputs, which also include steering. The software tool used in the design of the package is the MATLAB/Simulink environment. The focus on this package is the ability of the user to use rapid prototyping techniques to quickly and easily produce accurate results, and to have the option of quickly running further simulations with the minimum of effort. The package can be used across a range of depths in terms of using simplified equations to more technical models which also allow the definition of user defined incline slopes and steering commands. Thus allowing the detailed simulation under a variety of scenarios. The existing software package could also be used as a teaching aid in Universities and its capabilities can be extended by simply incorporating more modules.

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Soft modelling and fuzzy logic control of wheeled skid-steer electric vehicles with steering prioritisation

Cited by 11 publications

References 11 publications

Approximate reasoning for safety and survivability of planetary rovers

Approximate reasoning for safety and survivability of planetary rovers

Rule–based reasoning and neural network perception for safe off–road robot mobility

Multi-input all-electric vehicle design using a forward simulation software approach

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