Space Situational Awareness (SSA) relies on Low Earth Orbit (LEO) satellites to capture continuous, highresolution imagery critical for identifying space threats. The vast volume of SSA images overwhelms satellite network throughput, hindering timely transmission and processing. This paper presents a reinforcement learning (RL)-driven distributed sparse coding framework that integrates novel compression algorithms with orbital RL to address these challenges. First, we introduce an Aggregated Dictionary Learning (ADL) algorithm and a Context-aware Adaptive Binary Arithmetic Coding (CABAC) algorithm, achieving a 93.78% compression ratio by exploiting the high sparsity and spatiotemporal redundancy of SSA images. Second, the proposed compression workflow is deployed across LEO satellites in a distributed manner, where both overlapping and non-overlapping regions of images are dynamically partitioned and processed in parallel to optimize resource utilization and reduce latency. An Orbital Double Deep Q-Network (DQN) framework is proposed to optimize task offloading decisions by (1) integrating orbital dynamics into the state space, and (2) adaptively partitioning images based on visible LEO resources. Evaluations demonstrate that our framework achieves 100% task completion under visibility constraints and a 51.61% reduction on CPU and RAM occupation time compared to centralized processing.

Robotic swarm coordination holds transformative potential for applications such as warehouse automation, search & rescue, and entertainment. However, approaches relying on wearable devices or vision-based systems are often constrained by hardware-intensive, high computational requirements, reliance on line-of-sight, and privacy concerns. Wireless sensing, particularly using Channel State Information (CSI), offers a promising alternative by translating environmental perturbations into CSI variation data. Nevertheless, existing CSI-based systems face significant challenges in domain adaptation, resource limitation, and scalability issues. This paper introduces MaestroBot, a hierarchical motion coordination system that combines distributed CSI-based wireless sensing with domain-adaptive learning to address these limitations. For leader robots, the system features a lightweight hand gesture recognition model, built on a “Hybrid-Single” knowledge distillation framework, achieving up to 95.87% accuracy while maintaining adaptability across diverse domains. For follower robots, the hierarchical motion propagation model leverages localized CSI analysis and dual-layer error correction mechanisms to deliver 97.2% accuracy with a low latency of 0.085 seconds, even in multi-row formations. Additionally, its cost-effective hardware design ensures practical scalability and real-world deployability. These results position MaestroBot as an efficient, robust, and privacy-preserving solution for large-scale robotic swarm coordination in dynamic environments.

The Low-Earth-Orbit (LEO) mega-constellation networks, by providing low-latency and high-speed communications, are becoming indispensable infrastructures for the future six-generation (6G) architecture. Consequently, the topology, with thousands of satellites equipped with batteries of limited life, has to be adaptively controlled with high energy efficiency. However, existing work lacks the joint consideration of energy efficiency and load adaptation. In this paper, we first propose the line-of-sight condition to determine the candidate ISL set. Next, we model the energy consumption of the LEO mega-constellation networks. Along this direction, we formulate the Load-Adaptive and Energy-Efficient (LAEE) topology control problem in LEO mega-constellation networks and prove its NP-hardness. Finally, we propose the Amortized Energy based Topology Control (AETC) algorithm to solve the LAEE problem, with good adaptation to the fluctuating load and guarantees connectivities between any two satellites. Extensive simulation results demonstrate that the AETC algorithm outperforms related schemes in terms of energy consumption and results in good topology stability.

With the proliferation of laser communication and space networks, the increasing communication requests, tasks, and traffic loads introduce new challenges to data routing to laser space networks (LSNs). Existing routing strategies perform in a distributed manner on single-layer networks. But they suffer from the delay of information update in highly dynamic LSNs, which encounter unstable laser links and uneven traffic distribution problems. In this paper, we first propose a multi-layer LSN architecture. It is composed of a routing layer with high-orbit satellites for routing planning and a forwarding layer with low-orbit satellites for data transmitting. A centralized multi-layer optical routing strategy is further designed, namely CMOR. It is expected to provide the data forwarding plan depending on the global view of the routing layer on real-time laser link status and traffic loads of the forwarding layer. Compared with single-layer distributed routing, CMOR is proven to have a lower packet loss rate and transmission latency with large-scale satellite simulations.