Learning Ground Traversability from Simulations

Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation

Watch Your Step! Terrain Traversability for Robot Control

Watch your step! Or perhaps, watch your wheels. Whatever the robot is, if it puts its feet, tracks, or wheels in the wrong place, it might get hurt; and as robots are quickly going from structured and completely known environments towards uncertain and unknown terrain, the surface assessment becomes an essential requirement. As a result, future mobile robots cannot neglect the evaluation of terrain’s structure, according to their driving capabilities. With the objective of filling this gap, the focus of this study was laid on terrain analysis methods, which can be used for robot control with particular reference to autonomous vehicles and mobile robots. Giving an overview of theory related to this topic, the investigation not only covers hardware, such as visual sensors or laser scanners, but also space descriptions, such as digital elevation models and point descriptors, introducing new aspects and characterization of terrain assessment. During the discussion, a wide number of examples and methodologies are exposed according to different tools and sensors, including the description of a recent method of terrain assessment using normal vectors analysis. Indeed, normal vectors has demonstrated great potentialities in the field of terrain irregularity assessment in both on‐road and off‐road environments

Adaptive and intelligent navigation of autonomous planetary rovers - A survey

The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors

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

Compact Modeling Technique for Outdoor Navigation

16 pages, 46 figures.In this paper, a new methodology to build compact local maps in real time for outdoor robot navigation is presented. The environment information is obtained from a 3-D scanner laser. The navigation model, which is called traversable region model, is based on a Voronoi diagram technique, but adapted to large outdoor environments. The model obtained with this methodology allows a definition of safe trajectories that depend on the robot's capabilities and the terrain properties, and it will represent, in a topogeometric way, the environment as local and global maps. The application presented is validated in real outdoor environments with the robot called GOLIAT.This work was supported by the Spanish Government through the MICYT project DPI2003-01170.Publicad

Improving the mobility performance of autonomous unmanned ground vehicles by adding the ability to 'Sense/Feel' their local environment.

This paper follows on from earlier work detailed in output one and critically reviews the sensor technologies used in autonomous vehicles, including robots, to ascertain the physical properties of the environment including terrain sensing. The paper reports on a comprehensive study done in terrain types and how these could be determined and the appropriate sensor technologies that can be used. It also reports on work currently in progress in applying these sensor technologies and gives details of a prototype system built at Middlesex University on a reconfigurable mobility system, demonstrating the success of the proposed strategies. This full paper was subject to a blind refereed review process and presented at the 12th HCI International 2007, Beijing, China, incorporating 8 other international thematic conferences. The conference involved over 250 parallel sessions and was attended by 2000 delegates. The conference proceedings are published by Springer in a 17 volume paperback book edition in the Lecture Notes in Computer Science series (LNCS). These are available on-line through the LNCS Digital Library, readily accessible by all subscribing libraries around the world, published in the proceedings of the Second International Conference on Virtual Reality, ICVR 2007, held as Part of HCI International 2007, Beijing, China, July 22-27, 2007. It is also published as a collection of 81 papers in Lecture Notes in Computer Science Series by Springer

A Near-to-Far Learning Framework for Terrain Characterization Using an Aerial/Ground-Vehicle Team

In this thesis, a novel framework for adaptive terrain characterization of untraversed far terrain in a natural outdoor setting is presented. The system learns the association between visual appearance of different terrain and the proprioceptive characteristics of that terrain in a self-supervised framework. The proprioceptive characteristics of the terrain are acquired by inertial sensors recording measurements of one second traversals that are mapped into the frequency domain and later through a clustering technique classified into discrete proprioceptive classes. Later, these labels are used as training inputs to the adaptive visual classifier. The visual classifier uses images captured by an aerial vehicle scouting ahead of the ground vehicle and extracts local and global descriptors from image patches. An incremental SVM is utilized on the set of images and training sets as they are grabbed sequentially. The framework proposed in this thesis has been experimentally validated in an outdoor environment. We compare the results of the adaptive approach with the offline a priori classification approach and yield an average 12% increase in accuracy results on outdoor settings. The adaptive classifier gradually learns the association between characteristics and visual features of new terrain interactions and modifies the decision boundaries

Haptic robot-environment interaction for self-supervised learning in ground mobility

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de ComputadoresThis dissertation presents a system for haptic interaction and self-supervised learning mechanisms to ascertain navigation affordances from depth cues. A simple pan-tilt telescopic arm and a structured light sensor, both fitted to the robot’s body frame, provide the required haptic and depth sensory feedback. The system aims at incrementally develop the ability to assess the cost of navigating in natural environments. For this purpose the robot learns a mapping between the appearance of objects, given sensory data provided by the sensor, and their bendability, perceived by the pan-tilt telescopic arm. The object descriptor, representing the object in memory and used for comparisons with other objects, is rich for a robust comparison and simple enough to allow for fast computations. The output of the memory learning mechanism allied with the haptic interaction point evaluation prioritize interaction points to increase the confidence on the interaction and correctly identifying obstacles, reducing the risk of the robot getting stuck or damaged. If the system concludes that the object is traversable, the environment change detection system allows the robot to overcome it. A set of field trials show the ability of the robot to progressively learn which elements of environment are traversable

Unevenness Point Descriptor for Terrain Analysis in Mobile Robot Applications

In recent years, the use of imaging sensors that produce a three-dimensional representation of the environment has become an efficient solution to increase the degree of perception of autonomous mobile robots. Accurate and dense 3D point clouds can be generated from traditional stereo systems and laser scanners or from the new generation of RGB-D cameras, representing a versatile, reliable and cost-effective solution that is rapidly gaining interest within the robotics community. For autonomous mobile robots, it is critical to assess the traversability of the surrounding environment, especially when driving across natural terrain. In this paper, a novel approach to detect traversable and non-traversable regions of the environment from a depth image is presented that could enhance mobility and safety through integration with localization, control and planning methods. The proposed algorithm is based on the analysis of the normal vector of a surface obtained through Principal Component Analysis and it leads to the definition of a novel, so defined, Unevenness Point Descriptor. Experimental results, obtained with vehicles operating in indoor and outdoor environments, are presented to validate this approach