The optimization control of multi-robot systems based on Reinforcement Learning is the
frontier field of Robotics and distributed Artificial Intelligence in recent years. Multi-robot …

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 …

Heterogeneous unmanned vehicles (UVs) are used in various defense and civil
applications. Some of the civil applications of UVs for gathering data and monitoring include …

The increased use of autonomous robotic agents, such as unmanned aerial vehicles (UAVs)
and ground rovers, for complex missions has motivated the development of autonomous …

The main issue in multi-agent planning (MAP) is the agents' coordination process that is a
computationally hard problem. Thus, many works focus on the planning strategy considering …

We consider the problem of high-level learning and decision making to enable multi-agent
teams to autonomously tackle complex, large-scale missions, over long time periods in the …

Temporal logics serve as a framework for expressing complex, temporally-extended
specifications in a mathematical precise manner. Using formal synthesis techniques, these …

This paper presents algorithms and flight test results for multi-agent cooperative planning
problems in presence of state-correlated uncertainty. An online learning and planning …

The multi-sensors autonomous cooperative mission planning is the senior situation of the
mission planning, and it plays an important role in information sharing, sources integration …

The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is
designed to characterise scenarios at the intersection between task allocation and coalition …