Smart Auto-reforestation Robot Perception System for Complex Tropical Terrain

Smart Auto-reforestation Robot Perception System for Complex Tropical Terrain

Advisor/s

A/Prof Ulf Schmitz, Dr Daniel Xing, Prof Andreas Lopata

College or Research Centre

College of Medicine & Dentistry; College of Public Health, Medical & Veterinary Science

Summary of Project

Join our dynamic research team at JCU and the Townsville Cancer Centre (TCC) for an exciting HDR (Higher Degree by Research) project that merges cutting-edge technology with critical clinical applications. Our umbrella project encompasses two groundbreaking research initiatives aimed at improving cancer treatment outcomes and patient care. Project 1: Enhancing Glioblastoma Treatment with Magnetic Resonance and Radiotherapy Glioblastoma (GBM) remains one of the most aggressive brain cancers, with limited survival rates despite recent advances in treatment. Our research focuses on the novel application of tumour treating fields and the integration of a magnetic resonance linear accelerator (MRL) to explore the biological effects of combining magnetic fields with ionizing radiation. We aim to uncover how these combined modalities impact tumour and immune cell interactions, ultimately seeking to improve GBM treatment efficacy. Project 2: Developing Blood-Based Surveillance for HPV-Associated Oropharyngeal Cancer Human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (HPV-OPSCC) presents unique challenges in post-treatment surveillance, particularly for patients in remote regions. Our research aims to develop and validate a blood-based circulating HPV-DNA (cHPV-DNA) test using next-generation sequencing (NGS). This innovative approach will enable earlier detection of recurrent disease, reduce patient burden, and improve survival outcomes for HPV-OPSCC patients.

Planting trees in tropical regions is essential for mitigating climate change, preserving biodiversity, regulating water systems, protecting soil, and providing economic benefits. However, the complex terrain in tropical areas, characterised by an uneven surface, slippery nature, dense vegetation, and deep slopes, poses significant challenges for conventional tree-planting robots.

This project aims to develop a next-generation robot perception system for auto-reforestation robots. The project will leverage cutting-edge artificial intelligence and sensor technologies, considering the robustness, adaptability, and flexibility required for tropical terrain. The developed perception system will enable the auto-reforestation Robot to understand the surrounding environment, helping it navigate challenging terrain, accurately identify suitable planting locations, and efficiently plant trees.

Expected outcomes of this project include multi-modal deep learning algorithms with enhanced perception capabilities for tropical reforestation robots, which will enable more effective and sustainable reforestation efforts. This project has significant environmental, economic, and social benefits by contributing to global climate change mitigation efforts, protecting biodiversity, and supporting local economies. The project's impact lies in its potential to revolutionise reforestation efforts, create a scalable, cost-effective, and sustainable solution to the challenges of tropical terrain, and inform future developments in smart robots for environmental applications.

Key Words

brain cancer; head and neck cancer; radiation oncology; biomarker; bioinformatics next generation sequencing

Would suit an applicant who

We invite motivated students with a passion for oncology, molecular biology, and innovative technology to apply. This is a unique opportunity to contribute to transformative research with the potential to significantly impact patient care and treatment outcomes.

This project suits a candidate with a Bachelor's or Master's degree in Robotics, Artificial Intelligence, Computer Science, Electrical Engineering, or a related field. They should have strong technical skills in robotics, artificial intelligence, and computer vision and proficiency in programming languages like Python, C++, and ROS. Excellent communication and interpersonal skills are necessary for collaborating with other researchers, stakeholders, and industry partners.

Desirable skills and qualifications for the candidate include familiarity with transformers, multi-modal data fusion, and reinforcement learning. Experience with sensor technologies such as LIDAR, Radar, cameras, and data processing methods for sensor data would be beneficial. Prior publication and fieldwork experiences are also desirable.

Updated: 14 Jul 2023