The integration of satellite communication into mobile devices represents a paradigm shift in connectivity, yet the performance characteristics under motion and environmental occlusion remain poorly understood. We present the Starlink Robot, the first mobile robotic platform equipped with Starlink satellite internet, comprehensive sensor suite including upward-facing camera, LiDAR, and IMU, designed to systematically study satellite communication performance during movement. Our multi-modal dataset captures synchronized communication metrics, motion dynamics, sky visibility, and 3D environmental context across diverse scenarios including steady-state motion, variable speeds, and different occlusion conditions. This platform and dataset enable researchers to develop motion-aware communication protocols, predict connectivity disruptions, and optimize satellite communication for emerging mobile applications from smartphones to autonomous vehicles. In this work, we use LEOViz for real-time data collection and visualization. The project is available at https://starlinkrobot.github.io.

The increasing deployment of mobile robots in dynamic outdoor environments necessitates robotic systems capable of maintaining reliability amidst fluctuating wireless connectivity. While the Robot Operating System (ROS) has established itself as the de facto standard for such networked robotics, its abstraction of communication as an opaque, besteffort utility creates a critical bottleneck: it fails to leverage physical layer (PHY) information, resulting in degraded performance and unreliable execution in fluctuating networks. To address this, this paper presents MobileROS, a wireless-native robot operating system that transforms wireless communication from an external service into a core system resource. Grounded in the Symbiotic Paradigm, MobileROS establishes a bidirectional exchange where network conditions inform robotic decisions and mission requirements guide network resource allocation. Based on service mesh principles and domain-driven design, our architecture implements a Hub-Engines-Cells (HEC) model. It features a central Hub for global optimization, three specialized engines (the Radio Information Engine, the Cross Domain Engine, and the Physical Adaptive Engine) for crosslayer intelligence, and distributed Cells as functional units. A key mechanism, Application-Driven Bidirectional Dynamic Slicing, allows robots to actively reconfigure network resources based on semantic urgency, transforming the robot from a passive observer into an active network controller. We systematically evaluate MobileROS across three cities (London, Hong Kong, and Shenzhen) in five scenarios: distributed visual SLAM, cross-domain LiDAR perception, V2X autonomous driving, hybrid multi-robot collaboration against WebRTC baselines, and partition recovery validating CAP-theorem-aware failsafe mechanisms. Results demonstrate that MobileROS maintains significantly more stable performance than standard ROS in mobile wireless deployments.We provide implementation details athttps://github.com/MobileROS.

Underwater acoustic communication, characterized by limited bandwidth, high latency, and low reliability, poses significant challenges for data exchange in bathymetric collaborative simultaneous localization and mapping (CSLAM). In this paper, we introduce a novel vector quantization (VQ) method called ID(O) for mapping data compression in bathymetric CSLAM. ID(O) encodes the map into an index map ($\mathbb {I}$), a central depth map ($\mathbb {D}$), and an orientation map ($\mathbb {O}$). To accommodate strict communication constraints, orientations can be partially or fully excluded from transmission, and we propose a method to estimate these orientations during map restoration. Moreover, we integrate ID(O) within a feature-based bathymetric CSLAM framework named TTT CSLAM. Extensive experiments on two large-scale sea trial datasets demonstrate that ID(O) achieves about 40$\%$higher restoration accuracy than the baseline method using principal component analysis. TTT CSLAM with ID(O) can match that with lossless compression regarding mapping accuracy and efficiency, and it is robust against 40$\%$packet loss and large dead reckoning drift errors across diverse environments. To the best of our knowledge, ID(O) is the first VQ method for bathymetric data compression, and TTT CSLAM with ID(O) is the first bathymetric CSLAM tested within an underwater communication network employed by acoustic modems.

In crowd navigation, the local goal plays a crucial role in trajectory initialization, optimization, and evaluation. Recognizing that when the global goal is distant, the robot's primary objective is avoiding collisions, making it less critical to pass through the exact local goal point, this work introduces the concept of goal lines, which extend the traditional local goal from a single point to multiple candidate lines. Coupled with a topological map construction strategy that groups obstacles to be as convex as possible, a goal-adaptive navigation framework is proposed to efficiently plan multiple candidate trajectories. Simulations and experiments demonstrate that the proposed GA-TEB framework effectively prevents deadlock situations, where the robot becomes frozen due to a lack of feasible trajectories in crowded environments. Additionally, the framework greatly increases planning frequency in scenarios with numerous non-convex obstacles, enhancing both robustness and safety.

LiDAR-based 3D pedestrian detection has recently been extensively applied in autonomous driving and intelligent mobile robots. However, it remains a highly challenging perceptual task due to the sparsity of pedestrian point cloud data and the significant deformation of pedestrian body postures. To address these challenges, we propose a Dense Cross Connections network with Linear Attention (DCCLA), which mitigates the semantic discrepancy between the encoder and decoder of the network by integrating multiple 3D sparse convolutional layers within the skip connections. Furthermore, we enhance these connections by introducing cross-connections, thereby effectively promoting information interaction among various channels. To effectively retain crucial information while summarizing diverse pedestrian representations, we propose the Linear Self-Attention module for 3D point clouds (LSA3D), which significantly reduces model complexity. The experimental results demonstrate that our DCCLA achieves state-of-the-art Average Precision (AP) for the 3D pedestrian detection task on the JRDB large-scale dataset, outperforming the second-ranked method by 2.7% AP. Furthermore, our DCCLA enhances 1.6% mIoU over the benchmark method on the SemanticKITTI dataset. Therefore, our method achieves excellent performance through a cross-scale feature fusion strategy and linear attention that fully combines the advantages of convolution and transformer architectures. The project is publicly available athttps://github.com/jinzhengguang/DCCLA.

6D object pose estimation holds essential roles in various fields, particularly in the grasping of industrial workpieces. Given challenges like rust, high reflectivity, and absent textures, this paper introduces a point cloud based pose estimation framework (PS6D). PS6D centers on slender and multi-symmetric objects. It extracts multi-scale features through an attention-guided feature extraction module, designs a symmetry-aware rotation loss and a center distance sensitive translation loss to regress the pose of each point to the centroid of the instance, and then uses a two-stage clustering method to complete instance segmentation and pose estimation. Objects from the Siléane and IPA datasets and typical workpieces from industrial practice are used to generate data and evaluate the algorithm. In comparison to the state-of-the-art approach, PS6D demonstrates an 11.5% improvement in ${{\text{F}}_{{1_{inst}}}}$ and a 14.8% improvement in Recall. The main part of PS6D has been deployed to the software of Mech-Mind, and achieves a 91.7% success rate in bin-picking experiments, marking its application in industrial pose estimation tasks.

Exploring topologically distinctive trajectories provides more options for robot motion planning. Since computing time grows greatly with environment complexity, improving exploration efficiency and picking the optimal trajectory in complex environments are critical issues. To this end, this paper proposes a Graphic-and Timed-Elastic-Band-based approach (GraphicTEB) with spatial completeness and high computing efficiency. The environment is analyzed utilizing computer graphics, where obstacles are extracted as nodes and their relationships are built as edges. Three contributions are presented. 1) By assembling directed detours formed by nodes and segmented paths formed by edges, a generalized path consisting of nodes and edges derives various normal paths efficiently. 2) By multiplying two vectors starting from the obstacle point closest to the waypoint and the boundary point farthest from the waypoint, an novel obstacle gradient is introduced to guide safer optimization. 3) By assigning edges with asymmetric Gaussian model, a trajectory evaluation strategy is designed to reflect the motion tendency and motion uncertainty of dynamic obstacles. Qualitative and quantitative simulations demonstrate that the proposed GraphicTEB achieves spatial completeness, higher scene pass rate, and fastest computing efficiency. Experiments are implemented in long corridor and broad room scenarios, where the robot goes through gaps safely, finds trajectories quickly, and passes pedestrians politelyNote to Practitioners—The motivation stems from the fact that our daily cruising robot occasionally gets trapped in a corridor with piled obstacles or in a complex dynamic crowd due to the lack of a reliable trajectory. The solution is to search for more topologically distinctive trajectories and pick the optimal one. Considering that existing open-source approaches are either incomplete or highly time-consuming, a method for clustering and searching trajectories in the obstacle-occupied regions is proposed to achieve spatial completeness and high computing efficiency. In addition, an optimization technique and a trajectory selection strategy are proposed to improve motion safety. However, at present, the search is incomplete in the temporal-spatial dimension when dynamic obstacle are moving fast. How to perform a complete and fast search in temporal-spatial space will be developed in the future.

Multi-signature scheme is a unique type of digital signature where a group of participants are capable of producing a signature interactively on a shared message, thus significantly reducing the signature size. This is especially important for Internet of Vehicles (IoV) systems where higher efficiency and lower costs are required during the communication. Most approaches so far, however, are developed by traditional methods such as the integer factoring assumption, which result in potential vulnerability to quantum computing attacks. Although a few lattice-based multi-signature candidates have been proposed, they either rely on hash-and-sign process with higher costs or may be compromised by larger size of public key and signature. Motivated by the Bimodal Lattice Signature Scheme (BLISS) model [1], we propose a new lattice-based multi-signature scheme (Multi-BLISS, MB) in this paper. Our scheme can also be transformed into an aggregate signature scheme (Aggregate MB, AMB) with similar level of performance. We evaluate both schemes by setting security levels of 128, 160 and 192 bits in the experiments, and the results demonstrate significant improvement on security and efficiency comparing to existing lattice-based multi-signature schemes.

In human-aware navigation, the robot tacitly games with humans, balancing safety and efficiency according to human intentions. Poor balance or bad intent recognition causes the robot to stop conservatively or advance rashly, resulting in a deadlock or even a collision respectively. To address the issue, this paper proposes an improved limit cycle for collaboratively parameterizing human intentions and planning robot motions. The human-robot interaction is modeled as a dynamic chicken game with incomplete information, where the human gaze is introduced to depict the unique characteristics of each person, allowing the robot to approach with different safety margins. Our method is tested in challenging indoor scenarios and outperforms traditional methods in both safety and efficiency. We enable robots to utilize human wisdom to solve problems that cannot be solved on their own. The robot bravely goes through oncoming crowds by getting closer to people with higher attention on it and has the foresight to stably cross in front or behind people.

Abstract Background The performance of nearest‐neighbour classification is highly sensitive to the quality of data. In order to reduce the impact of the inevitable existence of irrelevant features in data, this paper employs the information learned by neural networks to implement a feature weighting technique and associated weighted multi‐functional nearest‐neighbour classification method. Method The non‐iterative neural networks are easy to implement while enjoying remarkable computational efficiency. In this paper, four non‐iterative neural networks (ELM, E‐ELM, RAWN and RVFL) are employed to evaluate the significance of features to decisions learned and stored in the parameters of neural networks. Moreover, the bias of the significance of features is comforted by using cross‐validation. The resulting feature significance is converted into feature weights to further implement weighted multi‐functional nearest‐neighbour classification. Result The experimental results demonstrate that the proposed neuro‐weighted feature weighting strategy can effectively reduce the impact of irrelevant features and enhance the performance of multi‐functional nearest‐neighbour classification. Contribution The proposed algorithm explores an avenue to efficiently utilize the learned knowledge of neural networks to reduce the role of irrelevant feature in nearest‐neighbour classification.

Existing methods on knowledge base question generation (KBQG) learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraphs.In this work, we show that making use of the past experience on semantically similar subgraphs can reduce the learning difficulty and promote the performance of KBQG models.To achieve this, we propose a novel approach to model diverse subgraphs with metalearner (DSM).Specifically, we devise a graph contrastive learning-based retriever to identify semantically similar subgraphs, so that we can construct the semantics-aware learning tasks for the meta-learner to learn semanticsspecific and semantics-agnostic knowledge on and across these tasks.Extensive experiments on two widely-adopted benchmarks for KBQG show that DSM derives new state-of-the-art performance and benefits the question answering tasks as a means of data augmentation.Codes and datasets are available online 1 .

In the narrow lane scene of autonomous driving, it is critical for the ego car to recognize the intentions of social vehicles and cooperate with them. However, cooperating with social vehicles is challenging due to insufficient information. This paper proposes an Explorative Game that adopts Participant Game and Perfect Bayesian Equilibrium to exploratively perform some aggressive actions to obtain additional information, thus the autonomous vehicle can cooperate robustly and efficiently. Explorative Game assumes each vehicle maintains a unique belief about the current situation and attributes insecurity and instability to the conflict of various Perfect Bayesian Equilibriums formed by various beliefs. Aggressive actions enable the ego car to proactively guide social vehicles to cooperate as it expects and encourage them to express their intentions as quickly and clearly as possible so that the equilibriums can converge and the conflict can be eliminated. Additional information reduces the error between the actual intentions of social vehicles and the estimated intentions from the ego car, helping rationally prune potential interactions and update parameters of the reward function. We demonstrate our algorithm on recorded data as well as virtual environments with manually controlled social vehicles to prove the efficiency of cooperation and the robustness of decision-making. And it has been running for more than 20 kilometers in the real world.

Despite the surges of deep learning-based method in dynamic scene deblurring achieves good performance, the challenges still remain a lot: (a) the running speed is far from the requirement of processing; (b) there will be inevitable information loss along with the deepening of network layers, which will further lead to the deterioration of the quality of the restored pictures. To deal with these challenges, we propose a novel learning-based model. In our method, we integrate two mechanisms for the generator based on the Generative Adversarial Nets (GAN). First, we develop the Enhanced Feature Fusion (EFF) mechanism which aims at providing multi-layer feature information to assist the image restoration. We further design Feature Multi-distillation (FMD) mechanism to filter and well fuse the multi-scale feature maps. By integrating the two mechanisms, the high-level feature maps can be progressively refined and the detailed semantic information can be properly utilized. In addition, we use the double-scale discriminator architecture which could enables the network to observe the image from the perspective of local and global respectively and obtain overall information for the whole image restoration. Extensive experimental results on the GOPRO and Kohler datasets show that our method can approach comparably to the state-of-the-arts in terms of accuracy while consuming much less inference time, which demonstrates that our method acquiring a better trade off between image restoration quality and running speed.

Mammogram mass detection is of great clinical significance due to its high proportion in breast cancers. The information from cross views (i.e., mediolateral oblique and cranio-caudal) is highly related and complementary, and is helpful to make comprehensive decisions. However, unlike radiologists who are able to recognize masses with reasoning ability in cross-view images, most existing methods lack the ability to reason under the guidance of domain knowledge, thus it limits the performance. In this paper, we introduce bipartite graph convolutional network to endow existing methods with cross-view reasoning ability of radiologists in mammogram mass detection. The bipartite node sets are constructed by cross-view images respectively to represent relatively consistent regions in breasts, while the bipartite edge learns to model both inherent cross-view geometric constraints and appearance similarities between correspondences. Based on the bipartite graph, the information propagates methodically through correspondences and enables spatial visual features equipped with customized cross-view reasoning ability. Experimental results on DDSM dataset demonstrate that the proposed algorithm achieves state-of-the-art performance. Besides, visual analysis shows the model has a clear physical meaning, which is helpful for radiologists in clinical interpretation.

Unmanned aerial vehicles (UAVs) have shown enormous potential in both public and civil domains. Although multi-UAV systems can collaboratively accomplish missions efficiently, UAV network(UAVNET) design faces many challenging issues, such as high mobility, dynamic topology, power constraints, and varying quality of communication links. Topology control plays a key role for providing high network connectivity while conserving power in UAVNETs. In this paper, we propose a distributed topology control algorithm based on discrete particle swarm optimization with articulation points(AP-DPSO). To reduce signaling overhead and facilitate distributed control, we first identify a set of articulation points (APs) to partition the network into multiple segments. The local topology control problem for individual segments is formulated as a degree-constrained minimum spanning tree problem. Each node collects local topology information and adjusts its transmit power to minimize power consumption. We conduct simulation experiments to evaluate the performance of the proposed AP-DPSO algorithm. Numerical results show that AP-DPSO outperforms some known algorithms including LMST and LSP, in terms of network connectivity, average link length and network robustness for a dynamic UAVNET.