4,112 research outputs found
Recommended from our members
Autonomous mobility scooters as assistive tools for the elderly
The aim of this research is to investigate the development of an autonomous navigation system that could be used as an assistive tool for elderly and disabled people in their activities of daily living. The navigation environment is an urban environment and the platform is a Mobility Scooter (MoS). To achieve this aim, a differentially steered MoS was modifed to receive motion commands from a computer and outfitted with onboard sensors that included a Global Positioning System (GPS) receiver and two 2D planar laser range sensors. Perception methods were developed to detect the presence of an outdoor pedestrian walkway. These methods achieved this by processing the range data produced by the laser sensors to identify features that are typically found around walkways like curbs, low vegetation, walls and barriers. A method that utilises GPS localisation information to plan and navigate a route in an outdoor urban environment was also developed. Extensive experimental work was conducted to test the accuracy, repeatability and usefulness of the sensory devices. The developed perception methodologies were evaluated in real world environments while the navigation algorithms were predominantly tested in virtual environments. A navigation system that plans a route in an urban environment and follows it using behaviours arranged in a hierarchy is presented and shown to have the ability to safely navigate an MoS along an outdoor pedestrian path
Challenges and solutions for autonomous ground robot scene understanding and navigation in unstructured outdoor environments: A review
The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges
Road terrain type classification based on laser measurement system data
For road vehicles, knowledge of terrain types is useful in improving passenger safety and comfort. The conventional methods are susceptible to vehicle speed variations and in this paper we present a method of using Laser Measurement System (LMS) data for speed independent road type classification. Experiments were carried out with an instrumented road vehicle (CRUISE), by manually driving on a variety of road terrain types namely Asphalt, Concrete, Grass, and Gravel roads at different speeds. A looking down LMS is used for capturing the terrain data. The range data is capable of capturing the structural differences while the remission values are used to observe anomalies in surface reflectance properties. Both measurements are combined and used in a Support Vector Machines Classifier to achieve an average accuracy of 95% on different road types
Influence of complex environments on LiDAR-Based robot navigation
La navigation sĂ©curitaire et efficace des robots mobiles repose grandement sur lâutilisation des capteurs embarquĂ©s. Lâun des capteurs qui est de plus en plus utilisĂ© pour cette tĂąche est le Light Detection And Ranging (LiDAR). Bien que les recherches rĂ©centes montrent une amĂ©lioration des performances de navigation basĂ©e sur les LiDARs, faire face Ă des environnements non structurĂ©s complexes ou des conditions mĂ©tĂ©orologiques difficiles reste problĂ©matique. Dans ce mĂ©moire, nous prĂ©sentons une analyse de lâinfluence de telles conditions sur la navigation basĂ©e sur les LiDARs. Notre premiĂšre contribution est dâĂ©valuer comment les LiDARs sont affectĂ©s par les flocons de neige durant les tempĂȘtes de neige. Pour ce faire, nous crĂ©ons un nouvel ensemble de donnĂ©es en faisant lâacquisition de donnĂ©es durant six prĂ©cipitations de neige. Une analyse statistique de ces ensembles de donnĂ©es, nous caractĂ©risons la sensibilitĂ© de chaque capteur et montrons que les mesures de capteurs peuvent ĂȘtre modĂ©lisĂ©es de maniĂšre probabilistique. Nous montrons aussi que les prĂ©cipitations de neige ont peu dâinfluence au-delĂ de 10 m. Notre seconde contribution est dâĂ©valuer lâimpact de structures tridimensionnelles complexes prĂ©sentes en forĂȘt sur les performances dâun algorithme de reconnaissance dâendroits. Nous avons acquis des donnĂ©es dans un environnement extĂ©rieur structurĂ© et en forĂȘt, ce qui permet dâĂ©valuer lâinfluence de ces derniers sur les performances de reconnaissance dâendroits. Notre hypothĂšse est que, plus deux balayages laser sont proches lâun de lâautre, plus la croyance que ceux-ci proviennent du mĂȘme endroit sera Ă©levĂ©e, mais modulĂ© par le niveau de complexitĂ© de lâenvironnement. Nos expĂ©riences confirment que la forĂȘt, avec ses rĂ©seaux de branches compliquĂ©s et son feuillage, produit plus de donnĂ©es aberrantes et induit une chute plus rapide des performances de reconnaissance en fonction de la distance. Notre conclusion finale est que, les environnements complexes Ă©tudiĂ©s influencent nĂ©gativement les performances de navigation basĂ©e sur les LiDARs, ce qui devrait ĂȘtre considĂ©rĂ© pour dĂ©velopper des algorithmes de navigation robustes.To ensure safe and efficient navigation, mobile robots heavily rely on their ability to use on-board sensors. One such sensor, increasingly used for robot navigation, is the Light Detection And Ranging (LiDAR). Although recent research showed improvement in LiDAR-based navigation, dealing with complex unstructured environments or difficult weather conditions remains problematic. In this thesis, we present an analysis of the influence of such challenging conditions on LiDAR-based navigation. Our first contribution is to evaluate how LiDARs are affected by snowflakes during snowstorms. To this end, we create a novel dataset by acquiring data during six snowfalls using four sensors simultaneously. Based on statistical analysis of this dataset, we characterized the sensitivity of each device and showed that sensor measurements can be modelled in a probabilistic manner. We also showed that falling snow has little impact beyond a range of 10 m. Our second contribution is to evaluate the impact of complex of three-dimensional structures, present in forests, on the performance of a LiDAR-based place recognition algorithm. We acquired data in structured outdoor environment and in forest, which allowed evaluating the impact of the environment on the place recognition performance. Our hypothesis was that the closer two scans are acquired from each other, the higher the belief that the scans originate from the same place will be, but modulated by the level of complexity of the environments. Our experiments confirmed that forests, with their intricate network of branches and foliage, produce more outliers and induce recognition performance to decrease more quickly with distance when compared with structured outdoor environment. Our conclusion is that falling snow conditions and forest environments negatively impact LiDAR-based navigation performance, which should be considered to develop robust navigation algorithms
Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor
In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements
aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at
growth stage 12â14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and
reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R 2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate
mostly between soil and vegetation and, to a far lesser extent, between crop and weeds.
The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying
- âŠ