39 research outputs found

    Robotic Monitoring of Habitats: the Natural Intelligence Approach

    Get PDF
    In this paper, we first discuss the challenges related to habitat monitoring and review possible robotic solutions. Then, we propose a framework to perform terrestrial habitat monitoring exploiting the mobility of legged robotic systems. The idea is to provide the robot with the Natural Intelligence introduced as the combination of the environment in which it moves, the intelligence embedded in the design of its body, and the algorithms composing its mind. This approach aims to solve the challenges of deploying robots in real natural environments, such as irregular and rough terrains, long-lasting operations, and unexpected collisions, with the final objective of assisting humans in assessing the habitat conservation status. Finally, we present examples of robotic monitoring of habitats in four different environments: forests, grasslands, dunes, and screes

    Procedural Constraint-based Generation for Game Development

    Get PDF

    Robotic Monitoring of Habitats: The Natural Intelligence Approach

    Get PDF
    In this paper, we first discuss the challenges related to habitat monitoring and review possible robotic solutions. Then, we propose a framework to perform terrestrial habitat monitoring exploiting the mobility of legged robotic systems. The idea is to provide the robot with the Natural Intelligence introduced as the combination of the environment in which it moves, the intelligence embedded in the design of its body, and the algorithms composing its mind. This approach aims to solve the challenges of deploying robots in real natural environments, such as irregular and rough terrains, long-lasting operations, and unexpected collisions, with the final objective of assisting humans in assessing the habitat conservation status. Finally, we present examples of robotic monitoring of habitats in four different environments: forests, grasslands, dunes, and screes

    Adaptive Locomotion: The Cylindabot Robot

    Get PDF
    Adaptive locomotion is an emerging field of robotics due to the complex interaction between the robot and its environment. Hybrid locomotion is where a robot has more than one mode of locomotion and potentially delivers the benefits of both, however, these advantages are often not quantified or applied to new scenarios. The classic approach is to design robots with a high number of degrees of freedom and a complex control system, whereas an intelligent morphology can simplify the problem and maintain capabilities. Cylindabot is designed to be a minimally actuated hybrid robot with strong terrain crossing capabilities. By limiting the number of motors, this reduces the robot's weight and means less reinforcement is needed for the physical frame or drive system. Cylindabot uses different drive directions to transform between using wheels or legs. Cylindabot is able to climb a slope of 32 degrees and a step ratio of 1.43 while only being driven by two motors. A physical prototype and simulation models show that adaptation is optimal for a range of terrain (slopes, steps, ridges and gaps). Cylindabot successfully adapts to a map environment where there are several routes to the target location. These results show that a hybrid robot can increase its terrain capabilities when changing how it moves and that this adaptation can be applied to wider environments. This is an important step to have hybrid robots being deployed to real situations

    Snake and Snake Robot Locomotion in Complex, 3-D Terrain

    Get PDF
    Snakes are able to traverse almost all types of environments by bending their elongate bodies in three dimensions to interact with the terrain. Similarly, a snake robot is a promising platform to perform critical tasks in various environments. Understanding how 3-D body bending effectively interacts with the terrain for propulsion and stability can not only inform how snakes move through natural environments, but also inspire snake robots to achieve similar performance to facilitate humans. How snakes and snake robots move on flat surfaces has been understood relatively well in previous studies. However, such ideal terrain is rare in natural environments and little was understood about how to generate propulsion and maintain stability when large height variations occur, except for some qualitative descriptions of arboreal snake locomotion and a few robots using geometric planning. To bridge this knowledge gap, in this dissertation research we integrated animal experiments and robotic studies in three representative environments: a large smooth step, an uneven arena of blocks of large height variation, and large bumps. We discovered that vertical body bending induces stability challenges but can generate large propulsion. When traversing a large smooth step, a snake robot is challenged by roll instability that increases with larger vertical body bending because of a higher center of mass. The instability can be reduced by body compliance that statistically increases surface contact. Despite the stability challenge, vertical body bending can potentially allow snakes to push against terrain for propulsion similar to lateral body bending, as demonstrated by corn snakes traversing an uneven arena. This ability to generate large propulsion was confirmed on a robot if body-terrain contact is well maintained. Contact feedback control can help the strategy accommodate perturbations such as novel terrain geometry or excessive external forces by helping the body regain lost contact. Our findings provide insights into how snakes and snake robots can use vertical body bending for efficient and versatile traversal of the three-dimensional world while maintaining stability

    Simplifying robotic locomotion by escaping traps via an active tail

    Get PDF
    Legged systems offer the ability to negotiate and climb heterogeneous terrains, more so than their wheeled counterparts \cite{freedberg_2012}. However, in certain complex environments, these systems are susceptible to failure conditions. These scenarios are caused by the interplay between the locomotor's kinematic state and the local terrain configuration, thus making them challenging to predict and overcome. These failures can cause catastrophic damage to the system and thus, methods to avoid such scenarios have been developed. These strategies typically take the form of environmental sensing or passive mechanical elements that adapt to the terrain. Such methods come at an increased control and mechanical design complexity for the system, often still being susceptible to imperceptible hazards. In this study, we investigated whether a tail could serve to offload this complexity by acting as a mechanism to generate new terradynamic interactions and mitigate failure via substrate contact. To do so, we developed a quadrupedal C-leg robophysical model (length and width = 27 cm, limb radius = 8 cm) capable of walking over rough terrain with an attachable actuated tail (length = 17 cm). We investigated three distinct tail strategies: static pose, periodic tapping, and load-triggered (power) tapping, while varying the angle of the tail relative to the body. We challenged the system to traverse a terrain (length = 160 cm, width = 80 cm) of randomized blocks (length and width = 10 cm, height = 0 to 12 cm) whose dimensions were scaled to the robot. Over this terrain, the robot exhibited trapping failures independent of gait pattern. Using the tail, the robot could free itself from trapping with a probability of 0 to 0.5, with the load-driven behaviors having comparable performance to low frequency periodic tapping across all tested tail angles. Along with increasing this likelihood of freeing, the robot displayed a longer survival distance over the rough terrain with these tail behaviors. In summary, we present the beginning of a framework that leverages mechanics via tail-ground interactions to offload limb control and design complexity to mitigate failure and improve legged system performance in heterogeneous environments.M.S

    Terrain segmentation with neural networks for off-road applications

    Get PDF
    To successfully move in off-road terrain such as at construction sites or in forests, autonomous mobile machines must be able to identify what they are moving on – or at least how challenging it is to move through – and decide if the short way is worth it. Traversability cost maps help in making that decision, and semantic segmentation of images of the terrain help in making the cost maps. It also brings machines more in-depth knowledge about the environments they are in. Semantic image segmentation means segmenting areas of an image into different classes, with the result being a mask of the differently labeled regions. It has benefited immensely from the emergence of computationally efficient neural networks, and the field has advanced quickly in recent years. In this thesis, the availability and performance of semantic segmentation deep learning models is explored, and their use in off-road applications is reviewed. Terrain traversability, which is a measure of how easy it is to move on or through a terrain, is classified into appearance-based and geometry-based methods. Image segmentation falls into the former. An application of semantic segmentation was made with an existing state-of-the-art neural network model called Deeplab v3+ on a self-gathered dataset. A heavy mobile vehicle was maneuvered at a test site to gather two sets of stereo images with a stereo camera and accompanying position and heading data from a Global Navigation Satellite System (GNSS) receiver. 15 images in the dataset were annotated with pixel-wise semantic masks and used to train the model. Different amounts of pretraining were experimented with to apply fine-tuning for transfer learning to the model. To demonstrate the use of segmentation as a tool for mapping, images segmented with the model were turned into a point cloud map by applying depth calculation for stereo camera images and merging together individual point clouds by utilizing the position and heading data. The trained semantic segmentation model was evaluated to have a mean intersection over union (IoU) score of 79.7, and a class frequency –weighted IoU score of 87.6. Pretraining the model with a larger dataset before fine-tuning it with a smaller dataset similar to our own images, and then fine-tuning it for the last time with our dataset, brought an increase of 7.3% in mean IoU and an increase of 4.6% in weighted IoU. Especially smaller classes were more accurately segmented when applying transfer learning. Based on the performance of the model and the merged point cloud, the use of semantic segmentation models for terrain traversability analysis shows promise in use cases with small datasets when utilizing transfer learning from larger datasets. More research could be done to find alternative methods to manually labeling detailed semantic segmentation masks, which remains an issue in all segmentation tasks, and to use more robust ways to generate the point cloud map and fuse it with the segmentation output.Onnistuakseen liikkumaan erilaisissa maastoissa teiden ulkopuolella kuten työmailla tai metsissä, on autonomisten liikkuvien koneiden tiedettävä minkä päällä ne liikkuvat – tai ainakin kuinka vaikeaa liikkuminen tulee olemaan – ja päättää onko lyhyin reitti helpoin vai ei. Ajokelpoisuuskustannuskartat auttavat tekemään tämän päätöksen, ja kuvien semanttinen segmentointi auttaa tekemään kustannuskartat. Se tuo koneille myös syvällisempää lisätietoa ympäristöistään. Kuvan semanttinen segmentointi tarkoittaa kuvan jakamista alueisiin sen perusteella, mihin luokkaan kuvan osat kuuluvat. Tuloksena on kuvan maski, johon on merkitty alueita niiden luokkien mukaan. Semanttinen segmentointi on hyötynyt valtavasti laskennallisesti tehokkaiden neuroverkkojen noususta, ja ala on kehittynyt nopeasti viime vuosina. Tässä diplomityössä tutkitaan erilaisten semanttisen segmentoinnin syväoppimismallien saatavuutta ja suorituskykyä etenkin maaston segmentointiin sovellettuna. Maaston ajokelpoisuus on mittari erilaisten maastojen vaikea- tai helppokulkuisuudelle. Se on luokiteltu kahteen eri menetelmään: ulkonäköperusteiseen ja muotoperusteiseen. Kuvien segmentointi kuuluu näistä ensimmäiseen. Semanttista segmentointia sovellettiin tässä työssä itse kerättyyn aineistoon käyttämällä olemassa olevaa tämänhetkistä huipputasoa edustavaa neuroverkkomallia Deeplab v3+:aa. Kaksi aineistoa kuvia ja paikka- sekä suuntaustietoa kerättiin ajamalla stereokameralla ja satelliittinavigointijärjestelmäpaikantimella varustettua raskasta työmaa-ajoneuvoa mittauspaikalla. 15 kuvaa aineistossa merkattiin pikselitason segmentaatiomaskilla ja käytettiin kouluttamaan neuroverkkomalli. Mallille tehtiin eri määriä esikoulutusta hienosäätösiirto-oppimisen arviointia varten. Demonstraationa segmentoinnin käytöstä kartoituksessa, segmentointimallilla segmentoiduista kuvista rakennettiin 3D-pistepilvi käyttämällä stereokameran mahdollistamaa syvyyden laskentaa ja yhdistämällä yksittäiset pistepilvet käyttäen apuna kerättyä paikka- ja suuntaustietoa. Koulutettu malli sai aineistolla keskimääräisen leikkaus-yli-liiton (IoU) –tuloksen 79,7 ja luokkien yleisyyden mukaan painotetun IoU-tuloksen 87,6. Esikoulutus ensin suuremmalla aineistolla, sitten hienosäätö meidän aineistomme kaltaisella pienemmällä aineistolla, ja lopuksi hienosäätö meidän kuvillamme toi mallille 7,3% parannuksen keskimääräiseen IoU:hun ja 4,6% parannuksen painotettuun IoU:hun. Etenkin pienempien luokkien segmentointi parani soveltamalla siirto-oppimista. Mallin tuloksien sekä yhdistetyn pistepilven perusteella semanttisen segmentoinnin neuroverkkomallien käyttämiseen maaston ajokelpoisuuden arviointiin on lupaavaa näyttöä pienilläkin lähtöaineistoilla, kun käytetään siirto-oppimista suuremmista aineistoista. Tutkimusta voitaisiin jatkaa etsimällä vaihtoehtoisia tapoja kuvien merkkaamiseen käsin tarkoilla segmentointimaskeilla, mikä on raskas työvaihe missä tahansa segmentointitehtävässä, sekä käyttämällä tarkempia tapoja pistepilvikartan generointiin ja segmentointimallin tuloksen yhdistämiseen karttaan

    GPGM-SLAM: a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps

    Get PDF
    Simultaneous Localization and Mapping (SLAM) techniques play a key role towards long-term autonomy of mobile robots due to the ability to correct localization errors and produce consistent maps of an environment over time. Contrarily to urban or man-made environments, where the presence of unique objects and structures offer unique cues for localization, the apperance of unstructured natural environments is often ambiguous and self-similar, hindering the performances of loop closure detection. In this paper, we present an approach to improve the robustness of place recognition in the context of a submap-based stereo SLAM based on Gaussian Process Gradient Maps (GPGMaps). GPGMaps embed a continuous representation of the gradients of the local terrain elevation by means of Gaussian Process regression and Structured Kernel Interpolation, given solely noisy elevation measurements. We leverage the imagelike structure of GPGMaps to detect loop closures using traditional visual features and Bag of Words. GPGMap matching is performed as an SE(2) alignment to establish loop closure constraints within a pose graph. We evaluate the proposed pipeline on a variety of datasets recorded on Mt. Etna, Sicily and in the Morocco desert, respectively Moon- and Mars-like environments, and we compare the localization performances with state-of-the-art approaches for visual SLAM and visual loop closure detection

    NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge

    Get PDF
    This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA)
    corecore