Simultaneous Location and Mapping (SLAM) is a common algorithm for position estimation in GNSS-denied environments. However, the high structural consistency and low lighting conditions in tunnel environments pose challenges for traditional visual SLAM and LiDAR SLAM. To this end, this paper presents LiDAR and optical flow fusion odometry (LOFF), which uses a direction-separated data fusion method to fuse optical flow odometry into the degenerate direction of the LiDAR SLAM without sacrificing the accuracy. Moreover, LOFF incorporates detectors and a compensator, allowing for a smooth transition between general environments and degeneracy environments. This capability facilitates the stable flight of unmanned aerial vehicles (UAVs) in GNSS-denied tunnel environments, including corners and long-distance consistency. Through real-world experiments conducted in a GNSS-denied pedestrian tunnel, we demonstrate the superior position accuracy and trajectory smoothness of LOFF compared to state-of-the-art visual SLAM and LiDAR SLAM.

Unmanned aerial vehicle (UAV) navigation systems face significant challenges in complex environments, such as sensor degradation and signal loss. This study proposes the NSDDI-AFF (Normalized Single-Dimensional Degradation Isolation with Adaptive Federated Filtering) method to address these issues. By integrating adaptive thresholding, degradation isolation, and multi-sensor fusion, the method dynamically identifies degraded channels and ensures robust state estimation. Evaluated in a semi-enclosed coal yard and in a hydropower pipeline, NSDDI-AFF reduced positional errors by 97.5% and 95.7% compared to LIO-SAM and Fast-LIO2, achieving a position RMSE of 0.05 m and orientation RMSE of 0.5°. The method detected degradation early (120 s) and maintained mapping accuracy with geometric errors below 0.5%. These results demonstrate that NSDDI-AFF significantly enhances UAV positioning accuracy, fault tolerance, and mapping reliability, making it a robust solution for challenging industrial applications.

Hydropower tunnel inspection is critical for infrastructure integrity yet remains inefficient and hazardous using manual methods. We propose FLISP (Fast LiDAR-IMU Synchronized Path Planner), a mapless planning framework for cooperative UGV-UAV inspection. Unlike traditional map-based paradigms, FLISP features three core contributions: (1) a unified architecture where a single UGV-mounted LiDAR-IMU suite drives synchronized path generation for both platforms; (2) platform-specific solvers utilizing an enhanced Firefly Algorithm for UGV obstacle avoidance and a dynamic iterative optimizer for UAV flight; and (3) a hierarchical refinement strategy ensuring kinematic feasibility without state estimation drift. Benchmarks in a 1.2 km operational tunnel demonstrate that FLISP circumvents structural bottlenecks of map-based methods, eliminating map rasterization overhead (Fast-LIO2 + A*) and sampling instability (LIO-SAM + RRT*). FLISP achieves a 100% success rate with ∼7 ms latency, representing a 7-fold speedup over grid-based and three-order-of-magnitude improvement over sampling-based baselines. Validated in operational hydropower tunnels, this approach offers a scalable solution for robotic inspection in feature-degraded linear infrastructure. A demonstration video is available at https://youtu.be/Y ezs1PfLJ4, and the code at https://github.com/ArchibaldGuo/FLISP.git.

In this paper, we present the design and control of a novel aerial vehicle inspired by the biomechanics of a rabbit named “Flybbit”. Flybbit consists of two main components, namely a movable “Ears” part and a rigid “Body” part, forming a composite flying system with five controllable degrees of freedom (DOFs). The “Ears” part is equipped with two tiltable motors paired with optional-sized propellers, enabling additional thrust generation and flight stability maintenance, and the “Body” part incorporates four fixed motors, analogous to a rabbit’s limbs, to provide the primary propulsion. To fully exploit the actuation capability, we derive the system dynamics and introduce a dynamic control allocation method with an adaptive strategy to mitigate actuator saturation during complex combined maneuvers. Furthermore, we analyze the differential flatness property and develop a nonlinear inverse dynamics controller enhanced with hybrid external wrench estimation, enabling accurate trajectory tracking in five DOFs. Flybbit supports both manual operation via RC and autonomous flight via onboard computation. Comprehensive simulations and real-world experiments validate the proposed design and control framework.

Recent autonomous exploration systems for unmanned platforms have improved motion efficiency, trajectory smoothness, and replanning continuity, but thorough coverage remains difficult in confined environments with short branches and partially resolved connectors. We present TOVEX, a completion-oriented framework that couples unresolved Euclidean signed distance field (ESDF) voxels with a sparse topological voxel-sphere (TVS) graph. Evaluation includes a bridge simulation, five-run maze comparisons with the baseline systems EDEN, GBPlanner, and TARE, component ablation, runtime and memory profiling, a cross-structure cave-scene stress test, and real-world experiments with an unmanned aerial vehicle (UAV) in an underground garage and an unmanned ground vehicle (UGV) in a warehouse. The bridge test shows coherent mapping and continuous velocity profiles. In the maze, TOVEX achieves the highest median final coverage of 96.2%, exceeding TARE by 5.2 percentage points, while TARE remains stronger in early coverage and motion efficiency. The geometry-derived terminal-tail analysis confirms that TOVEX achieves the most complete residual-passage resolution, while removing short-branch priority produces the largest loss. At approximately 1100 TVS vertices, median target-selection latency is 0.89 ms and planner proportional set size (PSS) is 52.3 MiB. Field trials demonstrate the same gain-bearing decision layer on aerial and ground platforms. TOVEX is well suited to unmanned-platform missions that prioritize resolving accessible residual passages.

The orchestration of heterogeneous swarm systems, such as Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) teams, in unknown, GPS-denied environments requires coordination among active perception, collision avoidance, and topological flexibility. Traditional Artificial Potential Field (APF) methods can suffer from local-minimum deadlocks, while optimization-based Control Barrier Function Quadratic Programs (CBF-QPs) may produce high-frequency control variation in narrow corridors. To address these limitations, this paper proposes the Riemannian Information-geometric Swarm Motion Orchestration (RISMO) framework. RISMO integrates objective-driven navigation, perception-aware motion generation, and obstacle avoidance within a common non-Euclidean metric representation. We construct a Riemannian metric tensor that incorporates the Fisher Information Matrix (FIM) to encourage the UGVs to preserve line-of-sight tracking of the UAVs. To accommodate formation deformation in narrow passages, the swarm topology is modeled as a Semi-Markov Jump System (S-MJS). Under the enforced positive per-agent inter-event bound and the stated Average Dwell Time (ADT) condition, the modeled switching sequence has a bounded transition frequency and cannot exhibit Zeno accumulation. Furthermore, a goal-aligned tangential flux supplies a deterministic local directional bias near saddle-like stagnation. The numerical and ablation results show that this mechanism facilitates escape from the tested non-convex traps. In the planar simulations, RISMO reduces the observed gradient stagnation of APF and the high-frequency control variation of CBF-QP while preserving positive obstacle clearance and cooperative perception.