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Relevant bibliographies by topics / Robotic terrain perception

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Academic literature on the topic 'Robotic terrain perception'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Robotic terrain perception"

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Shi, Wenlei, Zerui Li, Wenjun Lv, Yuping Wu, Ji Chang, and Xiaochuan Li. "Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification." Electronics 9, no. 3 (2020): 513. http://dx.doi.org/10.3390/electronics9030513.

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The achievement of robot autonomy has environmental perception as a prerequisite. The hazards rendered from uneven, soft and slippery terrains, which are generally named non-geometric hazards, are another potential threat reducing the traversing efficient, and therefore receiving more and more attention from the robotics community. In the paper, the vibration-based terrain classification (VTC) is investigated by taking a very practical issue, i.e., lack of labels, into consideration. According to the intrinsic temporal correlation existing in the sampled terrain sequence, a modified Laplacian

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Kouame, Yann Olivier Akansie, C. Biradar Rajashekhar, Rajendra Karthik, and D. Devanagavi Geetha. "A terrain data collection sensor box towards a better analysis of terrains conditions." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4388–402. https://doi.org/10.11591/ijai.v13.i4.pp4388-4402.

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Autonomous mobile robots are increasingly used across various applications, relying on multiple sensors for environmental awareness and efficient task execution. Given the unpredictability of human environments, versatility is crucial for these robots. Their performance is largely determined by how they perceive their surroundings. This paper introduces a machine learning (ML) approach focusing on land conditions to enhance a robot’s locomotion. The authors propose a method to classify terrains for data collection, involving the design of an apparatus to gather field data. This design is

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Weisbin, C., and D. Perillard. "R & D Profile Jet Propulsion Laboratory Robotic Facilities and Associated Research." Robotica 9, no. 1 (1991): 7–21. http://dx.doi.org/10.1017/s0263574700015526.

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SUMMARYThis paper describes the robotics facilities and associated research program of the Jet Propulsion Laboratory, lead center in telerobotics for the United States National Aeronautics and Space Administration. Emphasis is placed on evolution from teleoperation to remote System automation. Research is described in manipulator modelling and control, real-time planning and monitoring, navigation in outdoor terrain, real-time sensing and perception, human-machine interface, and overall System architectures. Applications to NASA missions emphasize robotic spacecraft for solar System exploratio

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Moh, K. T., and Van Jiang. "A Miniature Robotic Gait Algorithm for Terrain-Adaptive Walking in Soft Ground Environments." International Academic Journal of Science and Engineering 12, no. 3 (2025): 43–49. https://doi.org/10.71086/iajse/v12i1/iajse1208.

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The ability of miniature robots to move efficiently across soft or deformable terrain remains a challenge, particularly when facing limits on power and control. To solve this problem, a robotic gait algorithm was developed to modify gait parameters in response to real-time feedback from the environment. The system combines a hierarchical command system with machine-learning terrain classification to move with precision and minimal power on unstable surfaces like sand, soil, and gravel. A visual perception component uses BoW representations and SVM to classify the terrain before contact, enabli

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Azcarate, Raul Fernando Garcia, Akhil Jayadeep, Aung Kyaw Zin, James Wei Shung Lee, M. A. Viraj J. Muthugala, and Mohan Rajesh Elara. "Adaptive Outdoor Cleaning Robot with Real-Time Terrain Perception and Fuzzy Control." Mathematics 13, no. 14 (2025): 2245. https://doi.org/10.3390/math13142245.

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Outdoor cleaning robots must operate reliably across diverse and unstructured surfaces, yet many existing systems lack the adaptability to handle terrain variability. This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. A 128-channel LiDAR sensor captures signal intensity images, which are processed by a ResNet-18 convolutional neural network to classify floor types as wood, smooth, or rough. Simultaneously, pitch angles from an onboard IMU detect terrain inclination. These inputs are tran

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Boboc, Răzvan Gabriel. "Robotic Applications in Skiing: A Systematic Review of Current Research and Challenges." Machines 13, no. 5 (2025): 397. https://doi.org/10.3390/machines13050397.

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This paper provides a comprehensive review of the current state of research on robotic technologies in sports, with a specific emphasis on skiing. Using a systematic review methodology, I conducted an extensive search for relevant academic articles and conference papers across several databases, including Scopus, Web of Science, and IEEE. A predefined set of keywords guided the search process, leading to an initial collection of 327 papers. After applying specific selection criteria, 24 studies were identified as most relevant to the topic. These selected works were analyzed in detail, coverin

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Park, Jae-Min, Sungchul Hong, and Hyu-Soung Shin. "Pilot Study of Low-Light Enhanced Terrain Mapping for Robotic Exploration in Lunar PSRs." Remote Sensing 15, no. 13 (2023): 3412. http://dx.doi.org/10.3390/rs15133412.

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The recent discovery of water ice in the lunar polar shadowed regions (PSRs) has driven interest in robotic exploration, due to its potential utilization to generate water, oxygen, and hydrogen that would enable sustainable human exploration in the future. However, the absence of direct sunlight in the PSRs poses a significant challenge for the robotic operation to obtain clear images, consequently impacting crucial tasks such as obstacle avoidance, pathfinding, and scientific investigation. In this regard, this study proposes a visual simultaneous localization and mapping (SLAM)-based robotic

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Edlinger, R., and A. Nüchter. "TERRAIN PREDICTION WITH A LOW-COST LIDAR SENSOR FOR MOBILE ROBOTS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W1-2022 (December 8, 2022): 81–86. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w1-2022-81-2022.

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Abstract. Terrain modelling influences various aspects of mobile robot navigation. The ability to explore in rough terrain and to recognise ground conditions are essential to perform different activities efficiently, safely and satisfactorily. For this reason, intelligent vehicles and robotic systems need cognitive capabilities to understand the terrain and derive information from it. The information is mostly acquired and processed by very high resolution 3D-cameras and LiDAR sensors which provide full 360-degree environmental view to deliver accurate 3D data. The aim of this paper is to find

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Raj, Anil K., Peter D. Neuhaus, Adrien M. Moucheboeuf, Jerryll H. Noorden, and David V. Lecoutre. "Mina: A Sensorimotor Robotic Orthosis for Mobility Assistance." Journal of Robotics 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/284352.

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While most mobility options for persons with paraplegia or paraparesis employ wheeled solutions, significant adverse health, psychological, and social consequences result from wheelchair confinement. Modern robotic exoskeleton devices for gait assistance and rehabilitation, however, can support legged locomotion systems for those with lower extremity weakness or paralysis. The Florida Institute for Human and Machine Cognition (IHMC) has developed the Mina, a prototype sensorimotor robotic orthosis for mobility assistance that provides mobility capability for paraplegic and paraparetic users. T

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Griffin, Brent, and Jessy Grizzle. "Nonholonomic virtual constraints and gait optimization for robust walking control." International Journal of Robotics Research 36, no. 8 (2017): 895–922. http://dx.doi.org/10.1177/0278364917708249.

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A key challenge in robotic bipedal locomotion is the design of feedback controllers that function well in the presence of uncertainty, in both the robot and its environment. This paper addresses the design of feedback controllers and periodic gaits that function well in the presence of modest terrain variation, without over-reliance on perception and a priori knowledge of the environment. Model-based design methods are introduced and subsequently validated in simulation and experiment on MARLO, an underactuated three-dimensional bipedal robot that is of roughly human size and is equipped with

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Dissertations / Theses on the topic "Robotic terrain perception"

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Pech, Thomas Joel. "A Deep-Learning Approach to Evaluating the Navigability of Off-Road Terrain from 3-D Imaging." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1496377449249936.

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Schalit, Emmanuel. "Navigation autonome d'un robot mobile en terrain accidenté." Toulouse 3, 1994. http://www.theses.fr/1994TOU30191.

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Ce memoire presente une approche originale dans le domaine de la navigation autonome en terrain accidente. Il a pour but d'aborder certaines des difficultes rencontrees par cette branche de la robotique. Nos travaux reposent sur des algorithmes simple et rapides, qui operent sur des cartes robocentriques construites a partir de perceptions des capteurs embarques. Les niveaux inferieurs de notre architecture ne modelisent que tres grossierement l'environnement, mais reposent sur une frequence elevee du cycle de perception-planification pour obtenir de reelles capacites de navigation. Ces niveau

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Defraiteur, Rémi. "Évaluation de systèmes d'aide à la conduite. Génération automatique de vérité terrain augmentée à partir d’un capteur haute résolution et d’une cartographie sémantique et 3D ; Evaluation de fonctions de perception tierces." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG040.

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Le véhicule autonome représente l'un des défis technologiques actuels majeurs dans le secteur automobile. Les véhicules actuels se complexifient et intègrent de nouveaux systèmes reposants sur des fonctionnalités clés telles que la perception. Permettant au véhicule d'appréhender l'environnement dans lequel il évolue, elle est exploitée sous différents aspects pour garantir une mobilité plus sûre. Étant donné le rôle essentiel de la perception dans le bon comportement d'un véhicule autonome, il est nécessaire de s'assurer que les solutions de perception utilisées soient suffisamment performant

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Fillatreau, Philippe. "Localisation et modélisation tridimensionnelles pour un robot mobile autonome tout terrain." Phd thesis, Université Paul Sabatier - Toulouse III, 1994. http://tel.archives-ouvertes.fr/tel-00261834.

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CETTE THESE TRAITE DU PROBLEME DE LA LOCALISATION DANS UN ENVIRONNEMENT TRIDIMENSIONNEL (3D) D'UN ROBOT MOBILE AUTONOME, AINSI QUE DE CELUI DE LA MODELISATION DE TERRAIN. DEUX DOMAINES TYPIQUES D'APPLICATION DE CE TRAVAIL SONT LA ROBOTIQUE MOBILE D'INTERVENTION (SECURITE CIVILE,) ET L'EXPLORATION PLANETAIRE. LA CONTRIBUTION DE LA THESE SE SITUE SURTOUT AU NIVEAU DE LA LOCALISATION D'UN ROBOT DANS UN ENVIRONNEMENT SEMI-STRUCTURE OU NON STRUCTURE. UN ETAT DE L'ART CONCERNANT D'UNE PART LES PRINCIPAUX CAPTEURS UTILISABLES, ET D'AUTRE PART LA MODELISATION, EST PRESENTE. L'ACCENT EST MIS SUR LES CA

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Lourenço, André Filipe Lopes. "A volumetric hybrid representation for obstacle detection in all-terrain robots." Master's thesis, 2014. http://hdl.handle.net/10362/13187.

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This dissertation presents an approach aimed at three-dimensional perception’s obstacle detection on all-terrain robots. Given the huge amount of acquired information, the adversities such environments present to an autonomous system and the swiftness, thus required, from each of its navigation decisions, it becomes imperative that the 3-D perceptional system to be able to map obstacles and passageways in the most swift and detailed manner. In this document, a hybrid approach is presented bringing the best of several methods together, combining the lightness of lesser meticulous analyses

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Book chapters on the topic "Robotic terrain perception"

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Tu, Jun, Chengliang Liu, Mingjun Wang, Liang Gong, and Yanming Li. "Far-Field Terrain Perception Using Max-Margin Markov Networks." In Intelligent Robotics and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33503-7_43.

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Belter, Dominik. "Perception-Based Motion Planning for a Walking Robot in Rugged Terrain." In Robot Motion and Control 2011. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2343-9_10.

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Focchi, Michele, Romeo Orsolino, Marco Camurri, et al. "Heuristic Planning for Rough Terrain Locomotion in Presence of External Disturbances and Variable Perception Quality." In Springer Tracts in Advanced Robotics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22327-4_9.

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Eldridge, Luci. "Sweeping Away the Dust: Mars as Reconstructed Image." In Robotic Vision and Virtual Interfacing. Edinburgh University Press, 2024. https://doi.org/10.3366/edinburgh/9781399523424.003.0009.

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Recent images from NASA’s rovers offer us virtual access to ostensibly familiar yet remote and hostile terrains. This chapter hones in upon one aspect of seeing and sensing this alien terrain; the use of colour balancing images to reveal the chemical compositions of rocks or to make stratigraphic comparisons between geology on Mars and Earth. Drawing on critical perspectives within the fields of photographic theory, science and technology studies, and Ludwig Wittgenstein’s philosophical investigations into changing aspects, the chapter interrogates the practice of working with images in this w

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Sammut, Claude, Reza Farid, Handy Wicaksono, and Timothy Wiley. "Logic-based Robotics." In Human-Like Machine Intelligence. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198862536.003.0023.

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This chapter explores methods for combining symbolic and sub-symbolic reasoning and learning systems to take advantage of the strengths of each approach in challenging tasks in robotics. In perception, Inductive Logic Programming (ILP) can be used to learn descriptions of classes of objects and to find relations between objects. Examples are given of perception for robots in urban search and rescue. We also describe systems for learning plans and behaviours for robots. Relational learning is used to acquire abstract model of robot actions that are then used to constrain sub-symbolic learning f

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ŁABĘCKI, Przemysław, and Piotr SKRZYPCZYŃSKI. "REAL-TIME VISUAL PERCEPTION FOR TERRAIN MAPPING IN A WALKING ROBOT." In Adaptive Mobile Robotics. WORLD SCIENTIFIC, 2012. http://dx.doi.org/10.1142/9789814415958_0096.

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Wang, Yong, Dexin Ran, Congcong Ming, Lihong Yao, and Yongjun Liu. "Analysis and Outlook on the Core Technology of All Terrain Intelligent Pathfinder Robot." In Advances in Transdisciplinary Engineering. IOS Press, 2024. https://doi.org/10.3233/atde241250.

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With the continuous development of technology, the application of all terrain intelligent pathfinder robots in various fields is becoming increasingly widespread. This article provides an in-depth analysis of the key technologies of all terrain intelligent pathfinder robots, including environmental perception technology, autonomous navigation technology, power system technology, mechanical structure design technology, etc. It explores the principles, application status, and challenges faced by these technologies, and looks forward to their future development trends, aiming to provide useful re

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Sindiramutty, Siva Raja, N. Z. Jhanjhi, and Wei Wei Goh. "Fundamentals of Drone Navigation and the Role of Computer Vision." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8497-8.ch006.

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Drone navigation is based on precise navigation for efficient and secure performance for delivery, surveillance, or rescue. Traditional navigation based on GPS, inertial measurement units, and magnetometers provides good guidance but is inefficient in conditions with weakened signals or unpredictable obstacles. Computer vision is changing this. By equipping drones to perceive and understand visual information about their surrounding space, it makes decision-making independent, allows for better navigation past obstacles, and builds real-time maps. Object detection, optical flow, and SLAM are s

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Conference papers on the topic "Robotic terrain perception"

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Jiang, Shuo, Adarsh Salagame, Alireza Ramezani, and Lawson L. S. Wong. "Snake Robot with Tactile Perception Navigates on Large-scale Challenging Terrain." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611384.

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Qian, Tangyu, Hao Zhang, Zhangli Zhou, Hao Wang, Mingyu Cai, and Zhen Kan. "LEEPS: Learning End-to-End Legged Perceptive Parkour Skills on Challenging Terrains." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801925.

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Allred, Christopher, Mason Russell, Mario Harper, and Jason Pusey. "Improving Methods for Multi-Terrain Classification Beyond Visual Perception." In 2021 Fifth IEEE International Conference on Robotic Computing (IRC). IEEE, 2021. http://dx.doi.org/10.1109/irc52146.2021.00022.

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Lawton, Teri B. "Dynamic object-oriented scene analysis based on computational neurobiology." In OSA Annual Meeting. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.wj5.

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The construction of robust object-oriented depth maps is fundamental to understanding the topography and motion of objects located within a given terrain. A computational vision system that is particularly novel in its approach has been developed; it creates object maps by using algorithms based on biological models. The success of this method is demonstrated by the speed and robustness of the results when the input consists of natural outdoor scenes, where the effects of terrain, shadows, scene illumination, reference landmarks, and scene complexity can be systematically explored. The perform

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Xing, Boyang, Bo Su, Lei Jiang, et al. "Perceptive Locomotion of Legged Robot Coupling Model Predictive Control and Terrain Mapping." In 11th Asia-Pacific Regional Conference of the ISTVS. International Society for Terrain-Vehicle Systems, 2022. http://dx.doi.org/10.56884/kpgl5403.

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Legged robots promise an advantage over traditional wheeled systems, however, most legged robots are still confined to structured and flat environments. In this paper, we present a motion planner for the perceptive rough-terrain locomotion with quadruped robots. One of the main reasons for this is the difficulty in planning complex whole-body motions while taking into account the terrain conditions. This problem is very high-dimensional as it considers the robots dynamics together with the terrain model in a suitable problem formulation. In this work, we propose a novel trajectory and foothold

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Reina, Giulio, Annalisa Milella, and Mario Foglia. "Vision-Based Methods for Mobile Robot Localization and Wheel Sinkage Estimation." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2188.

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External perception based on vision plays a critical role in developing improved and robust localization algorithms for mobile robots, as well as in gaining important information about the vehicle and the traversed terrain. This paper presents two novel methods to improve mobility on rough terrains by using visual input. The first method consists of a stereovision algorithm for 6-DoF ego-motion estimation, which integrates image intensity information and 3D stereo data using an Iterative Closest Point (ICP) approach. The second method aims at estimating the wheel sinkage of a mobile robot on d

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Niles, Kenneth, Steven Bunkley, W. Jacob Wagner, Isaac Blankenau, Anton Netchaev, and Ahmet Soylemezoglu. "SATELLITE IMAGE TEMPLATE MATCHING WITH COVARIANCE ESTIMATION FOR UNMANNED GROUND VEHICLE LOCALIZATION: ACTIVE TERRAIN LOCALIZATION IMAGING SYSTEM (ATLIS)." In 2024 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium. National Defense Industrial Association, 2024. http://dx.doi.org/10.4271/2024-01-3995.

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<title>ABSTRACT</title> <p>Robotic platforms require accurate geo-spatial localization for high-level mission planning, real-time site reconnaissance, and multi-machine collaboration. Global navigation satellite system (GNSS) receivers are most commonly used to provide UGVs with accurate geolocation. However, GNSS is not reliable in contested environments because it is vulnerable to jamming, spoofing and black-outs. To address these issues, the United States Army Corps of Engineers (USACE) -Engineer Research and Development Center (ERDC) has developed the Active Terrain Local

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Kweon, In S., Martial Hebert, and Takeo Kanade. "Perception For Rugged Terrain." In 1988 Robotics Conferences, edited by William J. Wolfe. SPIE, 1989. http://dx.doi.org/10.1117/12.949089.

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Kanoulas, Dimitrios, and Marsette Vona. "Bio-inspired rough terrain contact patch perception." In 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014. http://dx.doi.org/10.1109/icra.2014.6907083.

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Brooks, D., and A. M. Howard. "Mobility reconfiguration for terrain exploration using passive perception." In 2009 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2009. http://dx.doi.org/10.1109/robot.2009.5152310.

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Reports on the topic "Robotic terrain perception"

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Mekonnen, Bisrat, Benjamin Christie, Michael Paquette, and Garry Glaspell. 3D mapping and navigation using MOVEit. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47179.

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Until recently, our focus has been primarily on the development of a low SWAP-C payload for deployment on a UGV that leverages 2D mapping and navigation. Due to these efforts, we are able to autonomously map and navigate very well within flat indoor environments. This report will explore the implementation of 3D mapping and navigation to allow unmanned vehicles to operate on a variety of terrains, both indoor and outdoor. The method we followed uses MOVEit, a motion planning framework. The MOVEit application is typically used in the control of robotic arms or manipulators, but its handling of

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