Information management in an integrated space telerobot

The in-orbit operations, like space structures inspection, servicing and repairing, is expected to be one of the most significant technological area for application and development of Robotics and Automation in Space Station environment. The Italian National Space Plan (PSN) has started up its strategic programme SPIDER (Space Inspection Device for Extravehicular Repairs), which is scheduled in three phases, with the final goal of performing docking and precision repairing in the Space Station environment. SPIDER system is an autonomous integrated space robot, using mature Artificial Intelligence tools and technics for its operational control. The preliminary results of a study on the information architecture of the spacecraft are described

Behavioral networks as a model for intelligent agents

On-going work at NASA Langley Research Center in the development and demonstration of a paradigm called behavioral networks as an architecture for intelligent agents is described. This work focuses on the need to identify a methodology for smoothly integrating the characteristics of low-level robotic behavior, including actuation and sensing, with intelligent activities such as planning, scheduling, and learning. This work assumes that all these needs can be met within a single methodology, and attempts to formalize this methodology in a connectionist architecture called behavioral networks. Behavioral networks are networks of task processes arranged in a task decomposition hierarchy. These processes are connected by both command/feedback data flow, and by the forward and reverse propagation of weights which measure the dynamic utility of actions and beliefs

A reactive system for open terrain navigation: Performance and limitations

We describe a core system for autonomous navigation in outdoor natural terrain. The system consists of three parts: a perception module which processes range images to identify untraversable regions of the terrain, a local map management module which maintains a representation of the environment in the vicinity of the vehicle, and a planning module which issues commands to the vehicle controller. Our approach is to use the concept of 'early traversability evaluation', and on the use of reactive planning for generating commands to drive the vehicle. We argue that our approach leads to a robust and efficient navigation system. We illustrate our approach by an experiment in which a vehicle travelled autonomously for one kilometer through unmapped cross-country terrain

Automated Visual Database Creation For A Ground Vehicle Simulator

This research focuses on extracting road models from stereo video sequences taken from a moving vehicle. The proposed method combines color histogram based segmentation, active contours (snakes) and morphological processing to extract road boundary coordinates for conversion into Matlab or Multigen OpenFlight compatible polygonal representations. Color segmentation uses an initial truth frame to develop a color probability density function (PDF) of the road versus the terrain. Subsequent frames are segmented using a Maximum Apostiori Probability (MAP) criteria and the resulting templates are used to update the PDFs. Color segmentation worked well where there was minimal shadowing and occlusion by other cars. A snake algorithm was used to find the road edges which were converted to 3D coordinates using stereo disparity and vehicle position information. The resulting 3D road models were accurate to within 1 meter

Perception system and functions for autonomous navigation in a natural environment

This paper presents the approach, algorithms, and processes we developed for the perception system of a cross-country autonomous robot. After a presentation of the tele-programming context we favor for intervention robots, we introduce an adaptive navigation approach, well suited for the characteristics of complex natural environments. This approach lead us to develop a heterogeneous perception system that manages several different terrain representatives. The perception functionalities required during navigation are listed, along with the corresponding representations we consider. The main perception processes we developed are presented. They are integrated within an on-board control architecture we developed. First results of an ambitious experiment currently underway at LAAS are then presented

Stereo-vision-based navigation of a six-legged walking robot in unknown rough terrain

In this paper we presents a visual navigation algorithm for the six-legged walking robot DLR Crawler in rough terrain. The algorithm is based on stereo images from which depth images are computed using the semi- global matching (SGM) method. Further, a visual odometry is calculated along with an error measure. Pose estimates are obtained by fusing iner- tial data with relative leg odometry and visual odometry measurements using an indirect information filter. The visual odometry error measure is used in the filtering process to put lower weights on erroneous visual odometry data, hence, improving the robustness of pose estimation. From the estimated poses and the depth images, a dense digital terrain map is created by applying the locus method. The traversability of the terrain is estimated by a plane fitting approach and paths are planned using a D* Lite planner taking the traversability of the terrain and the current motion capabilities of the robot into account. Motion commands and the traversability measures of the upcoming terrain are sent to the walking layer of the robot so that it can choose an appropriate gait for the terrain. Experimental results show the accuracy of the navigation algorithm and its robustness against visual disturbances

Correspondence A Curvature-Based Approach to Terrain Recognition

A6stract-This paper describes an algorithm which uses a Gaussian and mean curvature profile for extracting special points on the terrain, and then uses these points for recognition of particular regions of the terrain. The Gaussian and mean curvatures are chosen because they are invariant under rotation and translation. In the Gaussian and mean curvature image, the points of maximum and minimum curvature are extracted and used for matching. The stability of the position of these points in the presence of noise and with resampling is investigated. The input for this algorithm is 3-D digital terrain data. Curvature values are calculated from the data by fitting a quadratic surface over a square window and calculating directional derivatives of this surface. A method of surface fitting which is invariant to coordinate system transformation is suggested and implemented. The real terrain data used in our experiments are compiled by the U.S. Army Engineer Topographic Laboratories, Fort Belvoir, VA. The algorithm is tested with and without the presence of noise and its performance is described. Index Terms-Computer vision, range imaging, shape and 3-D description, 3-D representation and recognition, visual navigation