Ryan Farish

Robotic and autonomous systems are increasingly becoming more prevalent: with autonomous vehicles, warehouse logistics, and drones to name a few. However, one key aspect lacking is the ability for such systems to navigate in newly unmapped complex environments. Existing technologies primarily rely on previous geometric data in conjunction with human intervention or do not extend to consider changing environmental objects and events, resulting in restricted capabilities for deployment in other use cases: such as disaster zones, deep sea exploration, or areas of dynamic activity. The proposed aim of this research is to develop an internal ASLAM multi-agent mapping system that is capable of unmapped autonomy through sustained learning, planning, and adapting to changing environments with the ability to recognise anomalies. In particular, extending to how such a system’s operation utilising ASLAM will impact their human counterparts through engagement in real-world applications targeting sustainable and trustworthy outcomes; identifying aspects of ethical and safety elements that can be used towards the development of responsible robotics.