Sistema de navegación de cobertura para vehículos no holonómicos en ambientes de exterior
- URL: http://arxiv.org/abs/2512.22734v1
- Date: Sun, 28 Dec 2025 00:36:16 GMT
- Title: Sistema de navegación de cobertura para vehículos no holonómicos en ambientes de exterior
- Abstract summary: This work is intended to be a proof of concept for the potential automation of various unit processes that require coverage navigation.<n>The developed system includes the calculation of routes that allow a mobile platform to cover a specific area.<n>The system was tested in different simulated and real outdoor environments, obtaining results near 90% of coverage.
- Score: 4.2566707664597
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In mobile robotics, coverage navigation refers to the deliberate movement of a robot with the purpose of covering a certain area or volume. Performing this task properly is fundamental for the execution of several activities, for instance, cleaning a facility with a robotic vacuum cleaner. In the mining industry, it is required to perform coverage in several unit processes related with material movement using industrial machinery, for example, in cleaning tasks, in dumps, and in the construction of tailings dam walls. The automation of these processes is fundamental to enhance the security associated with their execution. In this work, a coverage navigation system for a non-holonomic robot is presented. This work is intended to be a proof of concept for the potential automation of various unit processes that require coverage navigation like the ones mentioned before. The developed system includes the calculation of routes that allow a mobile platform to cover a specific area, and incorporates recovery behaviors in case that an unforeseen event occurs, such as the arising of dynamic or previously unmapped obstacles in the terrain to be covered, e.g., other machines or pedestrians passing through the area, being able to perform evasive maneuvers and post-recovery to ensure a complete coverage of the terrain. The system was tested in different simulated and real outdoor environments, obtaining results near 90% of coverage in the majority of experiments. The next step of development is to scale up the utilized robot to a mining machine/vehicle whose operation will be validated in a real environment. The result of one of the tests performed in the real world can be seen in the video available in https://youtu.be/gK7_3bK1P5g.
Related papers
- Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments [96.23886784364997]
Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation.<n>We present Qwen-VLA, a unified embodied foundation model that extends Qwen's vision-language modeling stack to continuous action and trajectory generation.
arXiv Detail & Related papers (2026-05-28T17:36:31Z) - MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation [56.30931340537373]
MolmoSpaces is a fully open ecosystem to support benchmarking of robot policies.<n>MolmoSpaces consists of over 230k diverse indoor environments.<n>MolmoSpaces-Bench is a benchmark suite of 8 tasks in which robots interact with our diverse scenes and richly annotated objects.
arXiv Detail & Related papers (2026-02-11T20:16:31Z) - CHORAL: Traversal-Aware Planning for Safe and Efficient Heterogeneous Multi-Robot Routing [4.3992789649203194]
Monitoring large, unknown, and complex environments with autonomous robots poses significant navigation challenges.<n>We propose an integrated semantic-aware framework for coordinating heterogeneous robots.<n> Experiments in simulation and in a real inspection mission with three robotic platforms demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2026-01-15T12:34:22Z) - Bench-Push: Benchmarking Pushing-based Navigation and Manipulation Tasks for Mobile Robots [6.551328007366227]
Bench-Push is the first unified benchmark for pushing-based mobile robot navigation and manipulation tasks.<n>It includes a range of simulated environments that capture the fundamental challenges in pushing-based tasks.<n>It also includes novel evaluation metrics to capture efficiency, interaction effort, and partial task completion.
arXiv Detail & Related papers (2025-12-12T17:25:32Z) - Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence [54.91177026001217]
Large language model (LLM)-enabled teaming methods typically assume well-structured and known environments.<n>We present SPINE-HT, a framework that addresses these limitations by grounding the reasoning abilities of LLMs in the context of a heterogeneous robot team.<n>Our framework achieves nearly twice the success rate compared to prior LLM-enabled heterogeneous teaming approaches.
arXiv Detail & Related papers (2025-10-30T18:24:38Z) - AnyTraverse: An off-road traversability framework with VLM and human operator in the loop [4.870043547158868]
Off-road traversability segmentation enables autonomous navigation with applications in search-and-rescue, military operations, wildlife exploration, and agriculture.<n>We present AnyTraverse, a framework combining natural language-based prompts with human-operator assistance to determine navigable regions for diverse robotic vehicles.
arXiv Detail & Related papers (2025-06-20T08:31:13Z) - Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities [65.98704516122228]
The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments.<n>This paper addresses the deployment of FM-enabled robots in the field, where missions often require a robot to operate in large-scale and unstructured environments.<n>We present the first demonstration of large-scale LLM-enabled robot planning in unstructured environments with several kilometers of missions.
arXiv Detail & Related papers (2025-05-14T15:28:43Z) - Harmonic Mobile Manipulation [35.82197562695662]
HarmonicMM is an end-to-end learning method that optimize both navigation and manipulation.<n>Our contributions include a new benchmark for mobile manipulation and the successful deployment with only RGB visual observation.
arXiv Detail & Related papers (2023-12-11T18:54:42Z) - Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
A fully-autonomous aerial robot for high-speed object grasping has been proposed.
As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface.
Our approach has been validated in a challenging international competition and has shown outstanding results.
arXiv Detail & Related papers (2021-12-10T11:49:51Z) - Rapid Exploration for Open-World Navigation with Latent Goal Models [78.45339342966196]
We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments.
At the core of our method is a learned latent variable model of distances and actions, along with a non-parametric topological memory of images.
We use an information bottleneck to regularize the learned policy, giving us (i) a compact visual representation of goals, (ii) improved generalization capabilities, and (iii) a mechanism for sampling feasible goals for exploration.
arXiv Detail & Related papers (2021-04-12T23:14:41Z) - Towards Multi-Robot Task-Motion Planning for Navigation in Belief Space [1.4824891788575418]
We present an integrated multi-robot task-motion planning framework for navigation in knowledge-intensive domains.
In particular, we consider a distributed multi-robot setting incorporating mutual observations between the robots.
The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning.
arXiv Detail & Related papers (2020-10-01T06:45:17Z) - SAPIEN: A SimulAted Part-based Interactive ENvironment [77.4739790629284]
SAPIEN is a realistic and physics-rich simulated environment that hosts a large-scale set for articulated objects.
We evaluate state-of-the-art vision algorithms for part detection and motion attribute recognition as well as demonstrate robotic interaction tasks.
arXiv Detail & Related papers (2020-03-19T00:11:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.