We present a solution to the problem of long-range obstacle/path recognition in autonomous robots. The system uses sparse traversability information from a stereo module to train a classifier online. The trained classifier can then predict the traversability of the entire scene. A distance-normalized image pyramid makes it possible to efficiently train on each frame seen by the robot, using lar...

In humanoid robot locomotion, using palm contacts can help the robot locomote more robustly under disturbance or when traversing unstructured environments, such as disaster sites. However, adding more contacts complicates the problem by increasing the branching factor of search-based planning significantly. Furthermore, balance checking with multiple noncoplanar contacts is also expensive to co...

For an autonomous mobile robot, an important task to accomplish while maneuvering in outdoor rugged environments is terrain traversability analyzing. Due to the large variety of terrain, a general representation cannot be obtained a priori. Thus, the ability to determine the traversability based on the vehicle motion information and its environments is necessary, and more likely to enable acces...

The concept of affordances, introduced by J.J. Gibson in Psychology, has recently attracted interest in autonomous robotics towards the development of cognitive systems. In earlier work (Şahin et al., Adaptive Behavior, vol.15(4), pp. 447-472, 2007), we reviewed the uses of this concept in different fields and proposed a formalism to use affordances at different levels of robot control. In this...

We present semi-supervised deep learning approaches for traversability estimation from fisheye images. Our method, GONet, and the proposed extensions leverage Generative Adversarial Networks (GANs) to effectively predict whether the area seen in the input image(s) is safe for a robot to traverse. These methods are trained with many positive images of traversable places, but just a small set of ...

This paper describes a method for classifying the traversability of terrain by combining unsupervised learning of color models that predict scene geometry with supervised learning of the relationship between geometric features and traversability. A neural network is trained offline on hand-labeled geometric features computed from stereo data. An online process learns the association between col...

We present a method that uses high-resolution topography data of rough terrain, and ground vehicle simulation, to predict traversability. Traversability is expressed as three independent measures: the ability traverse terrain at target speed, energy consumption, acceleration. The measures are continuous reflect different objectives for planning go beyond binary classification. A deep neural net...

The purpose of this study was to evaluate the prognostic impact of endoscopic traversability in patients with locally advanced esophageal squamous cell carcinoma.This retrospective study was based on medical records from a single tertiary medical center. The records of 317 patients with esophageal squamous cell carcinoma treated with surgery or definitive chemoradiotherapy (CRT) between January...