Long-horizon composite task planning for multi-robot systems in cross-regional complex scenarios faces dual challenges: spatial-semantic comprehension of natural language described tasks and collaborative optimization of subtask al-location. To address these challenges, this paper proposes a progressive three-stage task planning framework. First, an augmented scene graph is constructed to enable large language models (LLMs) to comprehend environmental structures, thereby generating simplified Linear Temporal Logic (LTL) task sequences. Subsequently, a novel heuristic function is employed to select optimal task allocation plans. Finally, LLMs are used to generate low-level executable robot instructions based on robotic system instruction templates. We establish a long-horizon composite task dataset for experimental validation on real-world quadrupedal multi-robot systems. Experimental results demonstrate the effectiveness of our approach in resolving cross-regional composite tasks.

It is a challenging task for ground robots to autonomously navigate in harsh environments due to the presence of non-trivial obstacles and uneven terrain. This requires trajectory planning that balances safety and efficiency. The primary challenge is to generate a feasible trajectory that prevents robot from tip-over while ensuring effective navigation. In this paper, we propose a capsizing-aware trajectory planner (CAP) to achieve trajectory planning on the uneven terrain. The tip-over stability of the robot on rough terrain is analyzed. Based on the tip-over stability, we define the traversable orientation, which indicates the safe range of robot orientations. This orientation is then incorporated into a capsizing-safety constraint for trajectory optimization. We employ a graph-based solver to compute a robust and feasible trajectory while adhering to the capsizing-safety constraint. Extensive simulation and real-world experiments validate the effectiveness and robustness of the proposed method. The results demonstrate that CAP outperforms existing state-of-the-art approaches, providing enhanced navigation performance on uneven terrains.

It is challenging for the mobile robot to achieve autonomous and mapless navigation in the unknown environment with uneven terrain. In this study, we present a layered and systematic pipeline. At the local level, we maintain a tree structure that is dynamically extended with the navigation. This structure unifies the planning with the terrain identification. Besides, it contributes to explicitly identifying the hazardous areas on uneven terrain. In particular, certain nodes of the tree are consistently kept to form a sparse graph at the global level, which records the history of the exploration. A series of subgoals that can be obtained in the tree and the graph are utilized for leading the navigation. To determine a subgoal, we develop an evaluation method whose input elements can be efficiently obtained on the layered structure. We conduct both simulation and real-world experiments to evaluate the developed method and its key modules. The experimental results demonstrate the effectiveness and efficiency of our method. The robot can travel through the unknown uneven region safely and reach the target rapidly without a preconstructed map.

This study focuses on localization and mapping for UGVs when they are deployed in environments with non-level roads. In these scenarios, the vehicles need to travel through flat but not necessarily level grounds, i.e., ascent or descent, which may cause drifts of the robot pose and distortion of the map. We develop a low-drift LiDAR odometry and mapping approach for the UGV with LiDAR as the only exteroceptive sensor. A factor-graph based pose optimization method is developed with a specifically designed factor named slope factor. This factor includes the slope information that is estimated from a real-time LiDAR data stream. The slope information is also used to enhance the loop-closure detection procedure. Moreover, an incremental pitch estimation mechanism is designed to achieve further pose estimation refinement. We demonstrate the effectiveness of the developed framework in real-world environments. The odometry drift is lower and the map is more precise than experiments with the state-of-the-arts. Notably, on the Kitti dataset, our method also exhibits convincing performance, demonstrating its strength in more general application scenarios.

Insider attacks is a well-known problem acknowledged as a threat as early as 1980s. The threat is attributed to legitimate users who take advantage of familiarity with the computational environment and abuse their privileges, can easily cause significant damage or losses. In this paper, we present an active defense model and framework of insider threat detection and sense. Firstly, we describe the hierarchical framework which deal with insider threat from several aspects, and subsequently, show a hierarchy-mapping based insider threats model, the kernel of the threats detection, sense and prediction. The experiments show that the model and framework could sense the insider threat in real-time effectively.