Cooperative energy harvesting for long-endurance autonomous vehicle teams

This paper considers the exploitation of energy harvesting technologies for teams of Autonomous Vehicles (AVs). Traditionally, the optimisation of information gathering tasks such as searching for and tracking new objects, and platform level power management, are only integrated at a mission-management level. In order to truly exploit new energy harvesting technologies which are emerging in both the commercial and military domains (for example the 'EATR' robot and next-generation solar panels), the sensor management and power management processes must be directly coupled. This paper presents a novel non-myopic sensor management framework which addresses this issue through the use of a predictive platform energy model. Energy harvesting opportunities are modelled using a dynamic spatial-temporal energy map and sensor and platform actions are optimised according to global team utility. The framework allows the assessment of a variety of different energy harvesting technologies and perceptive tasks. In this paper, two representative scenarios are used to parameterise the model with specific efficiency and energy abundance figures. Simulation results indicate that the integration of intelligent power management with traditional sensor management processes can significantly increase operational endurance and, in some cases, simultaneously improve surveillance or tracking performance. Furthermore, the framework is used to assess the potential impact of energy harvesting technologies at various efficiency levels. This provides important insight into the potential benefits that intelligent power management can offer in relation to improving system performance and reducing the dependency on fossil fuels and logistical support.