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CoNav-UAV is proposed, which explicitly models the target-oriented vision-and-language navigation task as a Stackelberg game between a high-altitude leader and a low-altitude follower, with the system operating on onboard visual and linguistic inputs alone.
Experimental results validate the effectiveness of the proposed reinforcement learning approach based on the Deep Q-Network algorithm, demonstrating its superior obstacle avoidance capabilities and exceptional energy optimization performance in complex urban settings.
By constraining strategic exploration through a pretrained motion decoder, RoboStriker substantially reduces the catastrophic balance failures observed in raw action-space methods and achieves superior tactical performance in both competitive win rates and striking efficiency.
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