ELECTRICAL AND ROBOTICS ENGINEERING
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Autonomous Navigation Systems for Multi-Terrain Outdoor Mobile Robots
ELECTRICAL AND ROBOTICS ENGINEERING
Dr Mohammed Ayoub Juman Lecturer PhD ,
Research expertise : Machine vision , Machine learning . Mobile robot control , Neural Networks , Smart Agriculture
E : mohammedayoub . juman @ monash . edu T : + 603 5514 4441
With the advancement of mobile robots over the recent years , the usage of them in various industries and uses has become more widespread . The usage of robots has extended out of indoor environments , and has moved forward with using them in outdoor tasks such as farming , food delivery and disaster support . Though they can be directly or remotely operated , enabling autonomous navigation would assist to increase their usage in various scenarios that would reduce the need for human intervention and monitoring . However , the problem lies in that most of the autonomous navigation systems are catered towards singular terrains , whereas outdoor robots might have to navigate through varied ones , necessitating different levels of control to maintain certain velocities or payload vibrations . This involves the creation of a motion controller system that could accurately detect the terrains encountered by the mobile robot , and then adapt the motion to ensure a smooth journey to the set destination . The created controller would be able to incrementally learn while undergoing motion , enabling the system to increase its performance accuracy while under operation . Other projects that I am currently involved in include : Industrial Automation to facilitate increased productivity , efficiency and quality control ; and Smart agriculture for the growth and monitoring of indoor plant with yield prediction .
Development of Noninvasive Brain-Computer Interface : Towards Clinical Use
Dr Lim Lam Ghai Lecturer PhD
Research expertise : Near-infrared spectroscopy , neural signal processing , brain-computer interface , artificial intelligence , health analytics
E : lim . lamghai @ monash . edu T : + 603 5514 5628
Functional near-infrared spectroscopy ( fNIRS ) is a noninvasive neuroimaging technique that measures neuronal activity indirectly through hemodynamic response in naturalistic environments . Recent advances in fNIRS have highlighted its potential as a powerful tool for assessing and monitoring functional brain activity across various applications . This progress opens up opportunities to utilize fNIRS in brain-computer interfaces ( BCIs ) and neurofeedback settings . However , several challenges must be addressed before fNIRS-based BCIs can be widely adopted in clinical settings . These challenges include the removal of physiological influences and motion artifacts , resolving baseline ambiguities , and accounting for scalp hemodynamics . Current research focuses on integrating artificial intelligence techniques with neuroimaging data to enhance the efficacy and accuracy of various fNIRS applications .
Modified mobile robot for autonomous navigation tasks
Automated Microgreen Farming Machine with Growth Monitoring
fNIRS-based BCIs .
Assessing working memory capacity using fNIRS .
RESEARCHER PROFILE 2025 / 2026