发表机构
Alma Mater Europaea University; AGH University of Krakow（欧洲阿尔马马特大学; 克拉科夫AGH科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于脉冲神经网络的近端策略优化算法，用于无人机在受限环境中自主导航，通过Actor-Critic与高斯策略结合，实现63.77%成功率并后期超90%。
AI 中文摘要
在受限三维环境中自主导航无人机一直是机器人领域的挑战。自主无人机在民用基础设施检查中的应用涉及桥梁检查、隧道检查和结构检查。深度强化学习在无人机自主导航中已在受限环境中取得成功。然而，算法的计算成本限制了其在无人机自主导航中的应用。本文提出在受限序列环境中使用基于脉冲神经网络的近端策略优化算法进行无人机自主导航。所提出的算法将基于脉冲的Actor-Critic强化学习与近端策略优化算法相结合。该算法在无人机自主导航中使用随机高斯策略。该算法在受限三维环境中的无人机自主导航中得以实现。该算法在超过3000个回合中成功完成了1913个回合。该算法平均每回合成功通过2.10个窗口。该算法成功实现了63.77%的成功率。在算法后期阶段，该算法成功实现了超过90%的成功率。
英文摘要
Autonomous navigation of unmanned aerial vehicles in constrained three-dimensional environments has been a challenge in the robotics domain. The application of autonomous unmanned aerial vehicles in civil infrastructure inspection involves the use of such vehicles in bridge inspection, tunnel inspection, and structural inspection. The use of deep reinforcement learning in the autonomous navigation of unmanned aerial vehicles has been successful in constrained environments. However, the computational cost of the algorithm limits the application of the algorithm in the autonomous navigation of unmanned aerial vehicles. This paper proposes the use of the spiking neural network-based Proximal Policy Optimization algorithm in the autonomous navigation of unmanned aerial vehicles in constrained sequential environments. The proposed algorithm integrates the use of spike-based actor-critic reinforcement learning with the Proximal Policy Optimization algorithm. The proposed algorithm uses the stochastic Gaussian policy in the autonomous navigation of unmanned aerial vehicles. The proposed algorithm was implemented in the autonomous navigation of unmanned aerial vehicles in constrained 3D environments. The proposed algorithm was successful in completing 1913 episodes out of more than 3000. The proposed algorithm was successful in passing an average of 2.10 windows per episode. The proposed algorithm was successful in achieving a success rate of 63.77%. The proposed algorithm was successful in achieving success rates of more than 90% in the later stages of the algorithm.
