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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...
This paper introduces an approximation algorithm for stochastic multi-agent planning based on Markov decision processes (MDPs). Specifically, we focus on a decentralized approach for planning the actions of a team of cooperating agents with uncertainties in fuel consumption and health-related models. The core idea behind the algorithm presented in this paper is to allow each agent to approximate the...
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