Health Aware Planning under uncertainty for UAV missions with heterogeneous teams

In large-scale persistent missions, the vehicle capabilities and health often degrade over time. This paper presents a Health Aware Planning (HAP) Framework for long-duration complex UAV missions by establishing close feedback between the high-level planning based on Markov Decision Processes (MDP) and the execution level learning-focused adaptive controllers. This feedback enables the HAP framework to plan by anticipating the failures and reassessing vehicle capabilities after the failures. This proactive behavior allows for efficient replanning to account for changing capabilities. Simulations for a 4 UAV target tracking scenario is presented to demonstrate the effectiveness of the proactive replanning capability of the presented HAP framework.