The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to-end policy designed to address the challenge of high-speed autonomous UAV navigation under partially observable environmental conditions. Our approach combines deep reinforcement learning with privileged learning to overcome the impact of observation data corruption caused by partial observability. We leverage an asymmetric Actor–Critic architecture to provide the agent with privileged information during training, which enhances the model’s perceptual capabilities. Additionally, we present a multi-agent exploration strategy across diverse environments to accelerate experience collection, which in turn expedites model convergence. We conducted extensive simulations across various scenarios, benchmarking our DPRL algorithm against state-of-the-art navigation algorithms. The results consistently demonstrate the superior performance of our algorithm in terms of flight efficiency, robustness and overall success rate.

Due to the adverse illumination in space, non-cooperative object perception based on multi-source image fusion is crucial for on-orbit maintenance and orbital debris removal. In this paper, we first propose a dual-branch multi-scale feature extraction encoder combining transformer block and Inception block to extract global and local features of visible and infrared images and establish high-dimensional semantic connections. Secondly, different from the traditional artificial design Fusion strategy, we propose a Feature Fusion Module called Cross-Convolution Feature Fusion (CCFF) Module, which can achieve image feature level fusion. Based on the above, we propose a Dual-branch Fusion Network based on Transformer and Inception (DFTI) for space non-cooperative object, which is an image fusion framework based on auto-encoder architecture and unsupervised learning. The fusion image can simultaneously retain the color texture details and contour energy information of space non-cooperative objects. Finally, we construct a fusion dataset of infrared and visible images for space non-cooperative objects (FIV-SNO) and compare DFTI with seven state-of-the-art methods. In addition, object tracking as a follow-up high-level visual task proves the effectiveness of our method. The experimental results demonstrate that compared with other advanced methods, the Entropy (EN) and average gradient (AG) of the fusing images using DFTI network are increased by 0.11 and 0.06 respectively. Our method exhibits excellent performance in both quantitative measures and qualitative evaluation.

The motion estimation of non-cooperative space objects is an important prerequisite for successfully completing on-orbit servicing, debris removing and asteroid exploration missions. The motion estimation of non-cooperative space objects is a challenging task because of the lack of prior information about the object in unknown scenarios. In this paper, a motion estimation method of non-cooperative space objects for autonomous navigation based on a monocular sequence of images is proposed. The rotational attitude estimation of the non-cooperative space object is realized by matching the feature points of adjacent frame images, and the conditions required for translational estimation are discussed. This method has small calculation costs and runs stably, which meets the requirements of real-time computation on-board. As a result, this method implements the estimation of the complete unknown information of the object, including the attitude quaternion and angular velocity. Numerical simulations and results verify the effectiveness of the proposed method.

Spacecraft approaching maneuver control normally uses traditional control methods such as Proportional–Integral–Derivative (PID) or Model Predictive Control (MPC), which require meticulous system design and lack robustness against unknown disturbances. To address these limitations, we propose an end-to-end asymmetric Deep Reinforcement Learning-based (DRL) spacecraft approaching maneuver (ADSAM) algorithm, which significantly enhances the robutsness of the space-approaching maneuver under large-scale unknown disturbance and Partial Observation Markov Decision Processes (POMDPs). We present a numerical simulation environment with the linear Clohessy–Wiltshire (CW) model, incorporating the Runge–Kutta 4th order method (RK4) to ensure a more accurate and efficient state transition. Experimental results also demonstrate that the effectiveness of the proposed algorithm outperforms the-state-of-the-art methods.

The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to-end policy designed to address the challenge of high-speed autonomous UAV navigation under partially observable environmental conditions. Our approach combines deep reinforcement learning with privileged learning to overcome the impact of observation data corruption caused by partial observability. We leverage an asymmetric Actor-Critic architecture to provide the agent with privileged information during training, which enhances the model's perceptual capabilities. Additionally, we present a multi-agent exploration strategy across diverse environments to accelerate experience collection, which in turn expedites model convergence. We conducted extensive simulations across various scenarios, benchmarking our DPRL algorithm against the state-of-the-art navigation algorithms. The results consistently demonstrate the superior performance of our algorithm in terms of flight efficiency, robustness and overall success rate.