Proceedings of the 4th field robot event 2006, Stuttgart/Hohenheim, Germany, 23-24th June 2006

Zeer uitgebreid verslag van het 4e Fieldrobotevent, dat gehouden werd op 23 en 24 juni 2006 in Stuttgart/Hohenhei

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach

The main aim of this work is the development of a vision-based road detection system fast enough to cope with the difficult real-time constraints imposed by moving vehicle applications. The hardware platform, a special-purpose massively parallel system, has been chosen to minimize system production and operational costs. This paper presents a novel approach to expectation-driven low-level image segmentation, which can be mapped naturally onto mesh-connected massively parallel SIMD architectures capable of handling hierarchical data structures. The input image is assumed to contain a distorted version of a given template; a multiresolution stretching process is used to reshape the original template in accordance with the acquired image content, minimizing a potential function. The distorted template is the process output.Comment: See http://www.jair.org/ for any accompanying file

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

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

An intelligent, free-flying robot

The ground based demonstration of the extensive extravehicular activity (EVA) Retriever, a voice-supervised, intelligent, free flying robot, is designed to evaluate the capability to retrieve objects (astronauts, equipment, and tools) which have accidentally separated from the Space Station. The major objective of the EVA Retriever Project is to design, develop, and evaluate an integrated robotic hardware and on-board software system which autonomously: (1) performs system activation and check-out; (2) searches for and acquires the target; (3) plans and executes a rendezvous while continuously tracking the target; (4) avoids stationary and moving obstacles; (5) reaches for and grapples the target; (6) returns to transfer the object; and (7) returns to base

Development of a Neural Network-Based Camera for Tomato Harvesting Robots

Automated tomato harvesting robots were rapidly developed recently. Most of the designs were more focused on positioning of the end of robotic arm by using various methods such as combination of the sensor and vision system. This project concentrated on the artificial intelligent via the Neural Network, in order to provide a better decision making system for tomato harvesting robot. The objective of this study was to develop 3 degree of freedom tomato harvesting robotic system complete with gripper and motion program. The development of software for tomato pattern identification, determination of the X and Y coordinates from web camera captured and the determination of the tomato and tomato ripeness using decision making from Neural Network also become the main objective. The approach is to detect the desired object using vision system attached to the cylindrical automation system and perform image analysis. These features will serve as inputs to a neural net, which will be trained with a set of predetermined ripe tomato. The output is a command for harvester arm to make the movement for harvesting. The position determination was done with a conversion of the distance in pixel into a distance in metric unit (mm) of the tomato image. Whereas the depth of the tomato distance (z direction) was done by moving the actuator system towards the calculated tomato position until the object sensor senses the present of the tomato. AWIsoft07 software was developed to view the harvester vision, display the captured image analysis on the harvester vision, and display the numerical analysis output and neural network output. The harvester system with 3 degree of freedoms (3DOF) equips with specially designed tomato gripper named as AWI2007 Tomato Harvesting Robot was developed in order to realize the data from the AWISoft07 developed software. Several calibrations were made to ensure the accuracy of the AWI2007 Tomato Harvesting Robot. The AWIsoft07 and AWI2007 Tomato Harvesting Robot were subjected to several harvesting tests under the laboratory environment. The harvesting result shows the ability of the software and the harvester. Consequently, AWI2007 Tomato Harvesting Robot with the camera vision was able to recognize the tomato ripeness intelligently via neural network analysis and moved to the harvesting position. These situations provided new improvements for tomato harvesting system compared to the previous findings. Therefore the application of the neural network based on camera vision was successful perform as artificial intelligent for tomato harvesting robotic system

Machine Vision for intelligent Semi-Autonomous Transport (MV-iSAT)

AbstractThe primary focus was to develop a vision-based system suitable for the navigation and mapping of an indoor, single-floor environment. Devices incorporating an iSAT system could be used as ‘self-propelled’ shopping carts in high-end retail stores or as automated luggage routing systems in airports. The primary design feature of this system is its Field Programmable Gate Array (FPGA) core, chosen for its strengths in parallelism and pipelining. Image processing has been successfully demonstrated in real-time using FPGA hardware. Remote feedback and monitoring was broadcasted to a host computer via a local area network. Deadlines as short as 40ns have been met by a custom built memory-based arbitration scheme. It is hoped that the iSAT platform will provide the basis for future work on advanced FPGA-based machine-vision algorithms for mobile robotics

Large-scale environment mapping and immersive human-robot interaction for agricultural mobile robot teleoperation

Remote operation is a crucial solution to problems encountered in agricultural machinery operations. However, traditional video streaming control methods fall short in overcoming the challenges of single perspective views and the inability to obtain 3D information. In light of these issues, our research proposes a large-scale digital map reconstruction and immersive human-machine remote control framework for agricultural scenarios. In our methodology, a DJI unmanned aerial vehicle(UAV) was utilized for data collection, and a novel video segmentation approach based on feature points was introduced. To tackle texture richness variability, an enhanced Structure from Motion (SfM) using superpixel segmentation was implemented. This method integrates the open Multiple View Geometry (openMVG) framework along with Local Features from Transformers (LoFTR). The enhanced SfM results in a point cloud map, which is further processed through Multi-View Stereo (MVS) to generate a complete map model. For control, a closed-loop system utilizing TCP for VR control and positioning of agricultural machinery was introduced. Our system offers a fully visual-based immersive control method, where upon connection to the local area network, operators can utilize VR for immersive remote control. The proposed method enhances both the robustness and convenience of the reconstruction process, thereby significantly facilitating operators in acquiring more comprehensive on-site information and engaging in immersive remote control operations. The code is available at: https://github.com/LiuTao1126/Enhance-SF