  One of the most critical applications undertaken by coalitions of Unmanned
Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) is reaching
predefined targets by following the most time-efficient routes while avoiding
collisions. Unfortunately, UAVs are hampered by limited battery life, and UGVs
face challenges in reachability due to obstacles and elevation variations.
Existing literature primarily focuses on one-to-one coalitions, which
constrains the efficiency of reaching targets. In this work, we introduce a
novel approach for a UAV-UGV coalition with a variable number of vehicles,
employing a modified mean-shift clustering algorithm to segment targets into
multiple zones. Each vehicle utilizes Multi-agent Deep Deterministic Policy
Gradient (MADDPG) and Multi-agent Proximal Policy Optimization (MAPPO), two
advanced reinforcement learning algorithms, to form an effective coalition for
navigating obstructed environments without collisions. This approach of
assigning targets to various circular zones, based on density and range,
significantly reduces the time required to reach these targets. Moreover,
introducing variability in the number of UAVs and UGVs in a coalition enhances
task efficiency by enabling simultaneous multi-target engagement. The results
of our experimental evaluation demonstrate that our proposed method
substantially surpasses current state-of-the-art techniques, nearly doubling
efficiency in terms of target navigation time and task completion rate.
