Aug 2026· International Journal of Aeronautical and Space Sciences· 0 citations· 10 references
TL;DR
To enable real-time computation, a heuristic-based solver framework is developed: an extended sequential greedy algorithm provides a quick baseline, while a reinforcement learning approach using an attention model and long short-term memory aims to overcome the local optimality of the greedy method.
Abstract
This paper presents a rolling-horizon replanning framework for multi-UAV missions considering fuel constraints in dynamic environments. At each decision epoch, a re-optimization subproblem is solved to minimize the total travel distance of all agents (UAVs) while visiting remaining task locations, considering en-route refueling depots. A mixed-integer linear programming formulation of the subproblem is proposed to find the optimal solution. In addition, to enable real-time computation, we develop a heuristic-based solver framework: an extended sequential greedy algorithm provides a quick baseline, while a reinforcement learning (RL) approach using an attention model and long short-term memory aims to overcome the local optimality of the greedy method. A case study and numerical experiments demonstrate the effectiveness of the proposed framework and the performance of the solution methods.
Efficient allocation of strips and flight paths for multiple unmanned aerial vehicles (UAVs) carrying out cooperative area coverage is a basic issue in aerial robotics, especially when fixed-wing kinematic constraints and multi-pass reentry coordination need to be considered together. This paper introduces a reinforcem...
Ning Li, Yi-Jie Zhang· International Conference on...· 0 citations
Coordinated multi-unmanned aerial vehicles (UAVs) planning requires the joint consideration of spatial paths, flight speeds, arrival times, and separation constraints. This study develops an improved whale optimization algorithm (IWOA) for offline path–speed planning in known static environments. The method combines co...
Dynamic target search using multiple unmanned aerial vehicles (UAVs) is challenging due to stochastic target emergence, partial observability, and strict onboard energy limitations. This paper proposes an energy-aware decentralized search framework for multi-UAV dynamic target search in uncertain environments. A dual-i...
The proposed hierarchical multi-agent proximal policy optimization framework can reduce total airlines' operational costs—including direct operating cost and capital cost and achieves a computation speedup in comparison with a conventional optimization baseline.
Li-Jing Liu, James M. Shihua, Qi-Yu Yan et al.· MATEC Web of Conferences· 0 citations
Unmanned Aerial Vehicles (UAVs) are becoming vital in many fields like robotics and aerospace. UAVs have many applications, ranging from delivering goods in disaster areas to agriculture. To make sure UAVs complete their mission efficiently, path planning becomes an important problem. The traditional models of path pla...
The growing deployment of autonomous unmanned aerial vehicles (UAVs) in applications such as surveillance, logistics, and disaster response necessitates robust multi-agent navigation in dynamic environments. A critical challenge in such systems is ensuring collision-free path planning while adapting to unforeseen obsta...
Smriti Tiwari, Nilotpal Chakraborty, M. Khatua· 2026 International Conferenc...· 0 citations
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