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One of the most critical applications undertaken by coalitions of UnmannedAerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) is reachingpredefined targets by following the most time-efficient routes while avoidingcollisions. Unfortunately, UAVs are hampered by limited battery life, and UGVsface challenges in reachability due to obstacles and elevation variations.Existing literature primarily focuses on one-to-one coalitions, whichconstrains the efficiency of reaching targets. In this work, we introduce anovel approach for a UAV-UGV coalition with a variable number of vehicles,employing a modified mean-shift clustering algorithm to segment targets intomultiple zones. Each vehicle utilizes Multi-agent Deep Deterministic PolicyGradient (MADDPG) and Multi-agent Proximal Policy Optimization (MAPPO), twoadvanced reinforcement learning algorithms, to form an effective coalition fornavigating obstructed environments without collisions. This approach ofassigning 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 enhancestask efficiency by enabling simultaneous multi-target engagement. The resultsof our experimental evaluation demonstrate that our proposed methodsubstantially surpasses current state-of-the-art techniques, nearly doublingefficiency in terms of target navigation time and task completion rate.

Optimizing UAV-UGV Coalition Operations: A Hybrid Clustering and  Multi-Agent Reinforcement Learning Approach for Path Planning in Obstructed  Environment