Slip prediction using visual information

This paper considers prediction of slip from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering a particular terrain can be very useful for better planning and avoiding terrains with large slip. The proposed method is based on learning from experience and consists of terrain type recognition and nonlinear regression modeling. After learning, slip prediction is done remotely using only the visual information as input. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The slip prediction error is about 20% of the step size

Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain

In this paper we tackle the problem of visually predicting surface friction for environments with diverse surfaces, and integrating this knowledge into biped robot locomotion planning. The problem is essential for autonomous robot locomotion since diverse surfaces with varying friction abound in the real world, from wood to ceramic tiles, grass or ice, which may cause difficulties or huge energy costs for robot locomotion if not considered. We propose to estimate friction and its uncertainty from visual estimation of material classes using convolutional neural networks, together with probability distribution functions of friction associated with each material. We then robustly integrate the friction predictions into a hierarchical (footstep and full-body) planning method using chance constraints, and optimize the same trajectory costs at both levels of the planning method for consistency. Our solution achieves fully autonomous perception and locomotion on slippery terrain, which considers not only friction and its uncertainty, but also collision, stability and trajectory cost. We show promising friction prediction results in real pictures of outdoor scenarios, and planning experiments on a real robot facing surfaces with different friction

Evaluation of 3D CNN Semantic Mapping for Rover Navigation

Terrain assessment is a key aspect for autonomous exploration rovers, surrounding environment recognition is required for multiple purposes, such as optimal trajectory planning and autonomous target identification. In this work we present a technique to generate accurate three-dimensional semantic maps for Martian environment. The algorithm uses as input a stereo image acquired by a camera mounted on a rover. Firstly, images are labeled with DeepLabv3+, which is an encoder-decoder Convolutional Neural Networl (CNN). Then, the labels obtained by the semantic segmentation are combined to stereo depth-maps in a Voxel representation. We evaluate our approach on the ESA Katwijk Beach Planetary Rover Dataset.Comment: To be presented at the 7th IEEE International Workshop on Metrology for Aerospace (MetroAerospace

System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

Autonomous model building using vision and manipulation

It is often the case that robotic systems require models, in order to successfully control themselves, and to interact with the world. Models take many forms and include kinematic models to plan motions, dynamics models to understand the interaction of forces, and models of 3D geometry to check for collisions, to name but a few. Traditionally, models are provided to the robotic system by the designers that build the system. However, for long-term autonomy it becomes important for the robot to be able to build and maintain models of itself, and of objects it might encounter. In this thesis, the argument for enabling robotic systems to autonomously build models is advanced and explored. The main contribution of this research is to show how a layered approach can be taken to building models. Thus a robot, starting with a limited amount of information, can autonomously build a number of models, including a kinematic model, which describes the robot’s body, and allows it to plan and perform future movements. Key to the incremental, autonomous approach is the use of exploratory actions. These are actions that the robot can perform in order to gain some more information, either about itself, or about an object with which it is interacting. A method is then presented whereby a robot, after being powered on, can home its joints using just vision, i.e. traditional methods such as absolute encoders, or limit switches are not required. The ability to interact with objects in order to extract information is one of the main advantages that a robotic system has over a purely passive system, when attempting to learn about or build models of objects. In light of this, the next contribution of this research is to look beyond the robot’s body and to present methods with which a robot can autonomously build models of objects in the world around it. The first class of objects examined are flat pack cardboard boxes, a class of articulated objects with a number of interesting properties. It is shown how exploratory actions can be used to build a model of a flat pack cardboard box and to locate any hinges the box may have. Specifically, it is shown how when interacting with an object, a robot can combine haptic feedback from force sensors, with visual feedback from a camera to get more information from an object than would be possible using just a single sensor modality. The final contribution of this research is to present a series of exploratory actions for a robotic text reading system that allow text to be found and read from an object. The text reading system highlights how models of objects can take many forms, from a representation of their physical extents, to the text that is written on them

Calage robuste et accéléré de nuages de points en environnements naturels via l'apprentissage automatique

En robotique mobile, un élément crucial dans la réalisation de la navigation autonome est la localisation du robot. En utilisant des scanners laser, ceci peut être réalisé en calant les nuages de points consécutifs. Pour ce faire, l’utilisation de points de repères appelés descripteurs sont généralement efficaces, car ils permettent d’établir des correspondances entre les nuages de points. Cependant, nous démontrons que dans certains environnements naturels, une proportion importante d’entre eux peut ne pas être fiable, dégradant ainsi les performances de l’alignement. Par conséquent, nous proposons de filtrer les descripteurs au préalable afin d’éliminer les nuisibles. Notre approche consiste à utiliser un algorithme d’apprentissage rapide, entraîné à la volée sous le paradigme positive and unlabeled learning sans aucune intervention humaine nécessaire. Les résultats obtenus montrent que notre approche permet de réduire significativement le nombre de descripteurs utilisés tout en augmentant la proportion de descripteurs fiables, accélérant et augmentant ainsi la robustesse de l’alignement.Localization of a mobile robot is crucial for autonomous navigation. Using laser scanners, this can be facilitated by the pairwise alignment of consecutive scans. For this purpose, landmarks called descriptors are generally effective as they facilitate point matching. However, we show that in some natural environments, many of them are likely to be unreliable. The presence of these unreliable descriptors adversely affects the performances of the alignment process. Therefore, we propose to filter unreliable descriptors as a prior step to alignment. Our approach uses a fast machine learning algorithm, trained on-the-fly under the positive and unlabeled learning paradigm without the need for human intervention. Our results show that the number of descriptors can be significantly reduced, while increasing the proportion of reliable ones, thus speeding up and improving the robustness of the scan alignment process

Slip Modelling, Estimation and Control of Omnidirectional Wheeled Mobile Robots with Powered Caster Wheels

Ph.DDOCTOR OF PHILOSOPH