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Collision Avoidance for Multiple UAVs in Unknown Scenarios with Causal Representation Disentanglement

Autor: Zhuang, Jiafan, Xia, Zihao, Han, Gaofei, Wang, Boxi, Li, Wenji, Wang, Dongliang, Hao, Zhifeng, Cai, Ruichu, Fan, Zhun

Deep reinforcement learning (DRL) has achieved remarkable progress in online path planning tasks for multi-UAV systems. However, existing DRL-based methods often suffer from performance degradation when tackling unseen scenarios, since the non-causal

Externí odkaz: http://arxiv.org/abs/2407.04064

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Robust Policy Learning for Multi-UAV Collision Avoidance with Causal Feature Selection

Autor: Zhuang, Jiafan, Han, Gaofei, Xia, Zihao, Wang, Boxi, Li, Wenji, Wang, Dongliang, Hao, Zhifeng, Cai, Ruichu, Fan, Zhun

In unseen and complex outdoor environments, collision avoidance navigation for unmanned aerial vehicle (UAV) swarms presents a challenging problem. It requires UAVs to navigate through various obstacles and complex backgrounds. Existing collision avo

Externí odkaz: http://arxiv.org/abs/2407.04056

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Revisiting Stereo Triangulation in UAV Distance Estimation

Autor: Zhuang, Jiafan, Yuan, Duan, Yan, Rihong, Huang, Weixin, Li, Wenji, Fan, Zhun

Distance estimation plays an important role for path planning and collision avoidance of swarm UAVs. However, the lack of annotated data seriously hinders the related studies. In this work, we build and present a UAVDE dataset for UAV distance estima

Externí odkaz: http://arxiv.org/abs/2306.08939

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