Joint Computation Offloading and Trajectory Planning for Multi-UAV Cooperative Target Search

Unmanned aerial vehicles (UAVs)-based cooperative target search plays an important role in the scenario of complex and urgent search missions. However, the limited battery life and onboard computational capacity make it challenging for UAVs to accomplish the search mission effectively. This paper investigates the problem of joint computation offloading and trajectory planning for a multi-UAV system integrated with edge computing. Our goal is to minimize the total uncertainty of the target search area while considering the limited energy and search time of UAVs. To adapt to highly dynamic environments, a deep reinforcement learning (DRL) framework is applied in this paper. We propose a novel approach of Action Masks and Branch-network based-Proximal Policy Optimization (AMB-PPO), by which a multi-branch network structure and action masks are introduced into PPO to address the challenges of large joint action spaces and low learning efficiency. The numerical results demonstrate that, when compared to other existing DRL methods, the proposed AMB-PPO algorithm performs better in reducing the total uncertainty of the target search area with higher learning efficiency and faster algorithm convergence.