An autonomous satellite architecture integrating deliberative reasoning and behavioural intelligence

This paper describes a method for the design of autonomous spacecraft, based upon behavioral approaches to intelligent robotics. First, a number of previous spacecraft automation projects are reviewed. A methodology for the design of autonomous spacecraft is then presented, drawing upon both the European Space Agency technological center (ESTEC) automation and robotics methodology and the subsumption architecture for autonomous robots. A layered competency model for autonomous orbital spacecraft is proposed. A simple example of low level competencies and their interaction is presented in order to illustrate the methodology. Finally, the general principles adopted for the control hardware design of the AUSTRALIS-1 spacecraft are described. This system will provide an orbital experimental platform for spacecraft autonomy studies, supporting the exploration of different logical control models, different computational metaphors within the behavioral control framework, and different mappings from the logical control model to its physical implementation

NASA space station automation: AI-based technology review. Executive summary

Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics

SAFDetection:Sensor Analysis based Fault Detection in Tightly-CoupledMulti-Robot Team Tasks

This dissertation addresses the problem of detecting faults based on sensor analysis for tightly-coupled multi-robot team tasks. The approach I developed is called SAFDetection, which stands for Sensor Analysis based Fault Detection, pronounced “Safe Detection”. When dealing with robot teams, it is challenging to detect all types of faults because of the complicated environment they operate in and the large spectrum of components used in the robot system. The SAFDetection approach provides a novel methodology for detecting robot faults in situations when motion models and models of multi-robot dynamic interactions are unavailable. The fundamental idea of SAFDetection is to build the robots’ normal behavior model based on the robots’ sensor data. This normal behavior model not only describes the motion pattern for the single robot, but also indicates the interaction among the robots in the same team. Inspired by data mining theory, it combines data clustering techniques with the generation of a probabilistic state transition diagram to model the normal operation of the multi-robot system. The contributions of the SAFDetection approach include: (1) providing a way for a robot system to automatically generate a normal behavior model with little prior knowledge; (2) enabling a robot system to detect physical, logic and interactive faults online; (3) providing a way to build a fault detection capability that is independent of the particular type of fault that occurs; and (4) providing a way for a robot team to generate a normal behavior model for the team based the individual robot’s normal behavior models. SAFDetection has two different versions of implementation on multi-robot teams: the centralized approach and the distributed approach; the preferred approach depends on the size of the robot team, the robot computational capability and the network environment. The SAFDetection approach has been successfully implemented and tested in three robot task scenarios: box pushing (with two robots) and follow-the-leader (implemented with two- and five-robot teams). These experiments have validated the SAFDetection approach and demonstrated its robustness, scalability, and applicability to a wide range of tightly-coupled multi-robot applications

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

The progress made by levels 1, 2, and 3 of the Office of Space Station in developing and applying advanced automation and robotics technology is described. Emphasis is placed upon the Space Station Freedom Program responses to specific recommendations made in the Advanced Technology Advisory Committee (ATAC) progress report 10, the flight telerobotic servicer, and the Advanced Development Program. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for the Space Station Freedom