Search Results (5)

A camera-guided warehouse robot keeps its bearings by repeatedly estimating its pose against known visual references, and it must relocalize whenever that estimate is lost. What limits this process is often not identification but the availability of a usable reference along the route. The references used in practice are usually single-sided planar fiducial markers such as QR-like codes, ArUco markers, and AprilTags, which stay readable only within a limited cone about their surface normal; a spatial reference, by contrast, can in principle be recognized from any azimuth. We quantify what that difference is worth at the navigation level. The framework is built in Unity with NavMesh navigation and a purely geometric-availability model, rather than an image-based recognizer, whose single switchable property is the availability rule. In the idealized all-azimuth spatial-reference regime (the spatial regime), a reference is available from any direction; in the single-sided, angularly constrained planar-reference regime (the planar regime) it is available only within $\pm 20°$ of the surface normal. A full-factorial experiment with 54 configurations ($3\times 3\times 2\times 3$) and $n=100$ paired replications, 10,800 runs in all, was run in both regimes over four deployment factors: deployment scheme, camera field of view, recovery step, and detection range. Under this geometric model, the spatial regime reached $5.7\times$ higher reference coverage ($41.6\%$ vs. $7.3\%$) and a mission-completion rate 30 percentage points higher (86.2% vs. 55.9%). A paired Wilcoxon signed-rank test confirms the coverage difference ($p<0.001$, matched-pairs $d_z=1.84$), and McNemar’s test together with a logistic regression confirms the completion difference. In a factorial analysis of variance, the detection range dominates (partial ${\eta }^2=0.903$), and a strong deployment × range interaction concentrates the advantage in the rack aisles, where a planar reference is seen edge-on. Three further analyses point the same way: an angular-threshold sweep from $10°$ to $60°$, an equal-count deployment control, and route- and time-normalized visibility and relocalization metrics. The advantage also held across square, L-shaped, and U-shaped aisle layouts (32,400 runs in total), with a negligible regime × layout interaction. All these numbers are model-based estimates under an explicitly stated availability model: they measure the navigation-level value of azimuthal reference availability and do not validate any particular physical object, decoding algorithm, or AR device. Full article

Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function $g\left(n\right)$, and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function $h\left(n\right)$. Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration. Full article

Indoor navigation remains a challenge due to the limitations of GPS-based systems in enclosed environments. Current approaches, such as marker-based ones, have been developed for indoor navigation. However, it requires extensive manual mapping and makes indoor navigation time-consuming and difficult to scale. To enhance current approaches to indoor navigation, this study proposes a node-based mapping for indoor navigation, allowing users to dynamically construct navigation paths using a mobile device. The system leverages NavMesh, the A* algorithm for pathfinding, and is integrated into the ARCore for real-time AR guidance. Nodes are placed within the environment to define walkable paths, which can be stored and reused without requiring a full system to rebuild. Once the prototype has been developed, usability testing is conducted using the Handheld Augmented Reality Usability Scale (HARUS) to evaluate manipulability, comprehensibility, and overall usability. This study finds that using node-based mapping for indoor navigation can help enhance flexibility in mapping new indoor spaces and offers an effective AR-guided navigation experience. However, there are some areas of improvement, including interface clarity and system scalability, that can be considered for future research. This study contributes practically to improving current practices in adaptive indoor navigation systems using AR-based dynamic mapping techniques. Full article

This paper presents a 3D vision-based autonomous navigation system for wheeled mobile robots equipped with an RGB-D camera. The system integrates SLAM (simultaneous localization and mapping), motion planning, and obstacle avoidance to operate in both static and dynamic environments. A real-time pipeline is developed to construct sparse and dense maps for precise localization and path planning. Navigation meshes (NavMeshes) derived from 3D reconstructions facilitate efficient A* path planning. Additionally, a dynamic “U-map” generated from depth data identifies obstacles, enabling rapid NavMesh updates for obstacle avoidance. The proposed system achieves real-time performance and robust navigation across diverse terrains, including uneven surfaces and ramps, offering a comprehensive solution for 3D vision-guided robotic navigation. Full article

Perfect information about an environment allows a robot to plan its actions optimally, but often requires significant investments into sensors and possibly infrastructure. In applications relevant to human–robot interaction, the environment is by definition dynamic and events close to the robot may be more relevant than distal ones. This suggests a non-trivial relationship between sensory sophistication on one hand, and task performance on the other. In this paper, we investigate this relationship in a simulated crowd navigation task. We use three different environments with unique characteristics that a crowd navigating robot might encounter and explore how the robot’s sensor range correlates with performance in the navigation task. We find diminishing returns of increased range in our particular case, suggesting that task performance and sensory sophistication might follow non-trivial relationships and that increased sophistication on the sensor side does not necessarily equal a corresponding increase in performance. Although this result is a simple proof of concept, it illustrates the benefit of exploring the consequences of different hardware designs—rather than merely algorithmic choices—in simulation first. We also find surprisingly good performance in the navigation task, including a low number of collisions with simulated human agents, using a relatively simple A*/NavMesh-based navigation strategy, which suggests that navigation strategies for robots in crowds need not always be sophisticated. Full article