Mission Planning for Long-Term Deployment using Probabilistic Environment Models

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1 Brief description of the content of the research including potential impactAs mobile robots become more advanced and affordable, they are being deployed in increasingly complex monitoring tasks in a variety of environments, from agricultural croplands to oceans and rainforests. In many cases, robots offer enhanced opportunities for environment modelling compared with fixed sensors, as they can cover more ground and remove the need for permanent installation of sensors in the environment. However, this leads to the question of how the robot should decide where to go to acquire the most useful information. My research will aim to develop algorithms that improve the capabilities of robotic monitoring systems, with potential applications in agriculture, ecology, renewable energy and many other areas. 2 Aims and objectivesThe aim of the proposed work is broadly to improve the efficiency of robotic monitoring systems, enabling them to provide more useful information within their operating constraints. Particular objectives to be addressed will include: 1. Designing a framework for spatiotemporal modelling that can be used by robots to capture a rich representation of their environments. A robot deployed in a monitoring context must be able to model its environment in a way that can usefully inform its planning system. 2. Demonstrating efficient informative planning with a rich underlying environment model. Existing work on persistent modelling and informative planning has typically used fairly simple environment models. We look to extend these capabilities to the richer models described above. 3. Incorporating practical constraints such as battery life in an integrated manner, to allow the system to proactively plan around its need to charge. 3 Novelty of the research methodologyRobotic technologies have developed dramatically in recent years and continue to do so, and as suchresearch into robotic monitoring still presents many avenues for further exploration. Much work has been done on the algorithmic foundations of this work, but we hope to both extend these foundations and - importantly - validate them through deployment on real robots, as well as through simulation testing. 4 Alignment to EPSRC strategies and research areasThe proposed work aligns strongly with the Robotics research area described by the EPSRC. Thisresearch area acknowledges the potential for robotics research to contribute in the near future to disruptive technologies in many major industrial sectors, including automotive, aerospace, nuclear,oil and gas, agriculture, space, manufacturing, defence and construction. This research area, and theproposed work, are also strongly connected with the Artificial Intelligence Technologies area,within which one of the focuses is toward development of autonomous and intelligent technologies.5 Companies or collaborators involvedThe proposed work is supported by Amazon Web Services (AWS)