Nils Axel Andersen

Tracing Carbon Sources through Aquatic and Terrestrial Food Webs Using Amino Acid Stable Isotope Fingerprinting

Tracing the origin of nutrients is a fundamental goal of food web research but methodological issues associated with current research techniques such as using stable isotope ratios of bulk tissue can lead to confounding results. We investigated whether naturally occurring δ13C patterns among amino acids (δ13CAA) could distinguish between multiple aquatic and terrestrial primary production sources. We found that δ13CAA patterns in contrast to bulk δ13C values distinguished between carbon derived from algae, seagrass, terrestrial plants, bacteria and fungi. Furthermore, we showed for two aquatic producers that their δ13CAA patterns were largely unaffected by different environmental conditions despite substantial shifts in bulk δ13C values. The potential of assessing the major carbon sources at the base of the food web was demonstrated for freshwater, pelagic, and estuarine consumers; consumer δ13C patterns of essential amino acids largely matched those of the dominant primary producers in each system. Since amino acids make up about half of organismal carbon, source diagnostic isotope fingerprints can be used as a new complementary approach to overcome some of the limitations of variable source bulk isotope values commonly encountered in estuarine areas and other complex environments with mixed aquatic and terrestrial inputs.

Auto- Cabibration in Automation Systems Using Vision

This paper discusses aspects of automatic camera calibration in automation systems. Three different scenarios are analysed: an AGV-system, a warehouse automation system, and a robot system. The properties of two methods for automatic camera calibration using multi-view 2-D calibration planes are investigated. It is found that the achievable calibration accuracy is comparable to methods using 3-D calibration objects.

Hand-Eye Calibration and Inverse Kinematics of Robot Arm Using Neural Network

Traditional technologies for solving hand-eye calibration and inverse kinematics are cumbersome and time consuming due to the high nonlinearity in the models. An alternative to the traditional approaches is the artificial neural network inspired by the remarkable abilities of the animals in different tasks. This paper describes the theory and implementation of neural networks for hand-eye calibration and inverse kinematics of a six degrees of freedom robot arm equipped with a stereo vision system. The feedforward neural network and the network training with error propagation algorithm are applied. The proposed approaches are validated in experiments. The results indicate that the hand-eye calibration with simple neural network outperforms the conventional method. Meanwhile, the neural network exhibits a promising performance in solving inverse kinematics.

A Test Bed for Experiments with Intelligent Vehicles

Abstract This paper describes the design considerations of a laboratory test bed for experiments with intelligent vehicles. A detailed description of the hard-and software elements is provided. Furthermore a specific operational mode is developed.

Real-Time Vision Based Control of Servomechanical Systems

This paper presents properties of vision used in real-time control systems. Two laboratory experiments have been carried out demonstrating the various aspects of vision used on servomechanical systems.

Orchard navigation using derivative free Kalman filtering

This paper describes the use of derivative free filters for mobile robot localization and navigation in an orchard. The localization algorithm fuses odometry and gyro measurements with line features representing the surrounding fruit trees of the orchard. The line features are created on basis of 2D laser scanner data by a least square algorithm. The three derivative free filters are compared to an EKF based localization method on a typical run covering four rows in the orchard. The Matlab® toolbox Kalmtool is used for easy switching between different filter implementations without the need for changing the base structure of the system.

MobotWare – A Plug-in Based Framework for Mobile Robots

Abstract This paper describes a plug-in based software framework developed at Automation and Control, DTU Electrical Engineering. The software has been used for education and research in mobile robotics for the last decade. Important design criteria have been real-time performance of the control level, easy integration of sensors, fast porting to new robots and core system stability and maintainability in an undisciplined programming environment. Real-time performance is assured by using RTAI-Linux; core stability is obtained by using plug-ins for user developed modules. The plug-in based module structure combined with inter-module communication based on TCP/IP sockets and human readable XML-protocol makes it easy to use the system on a wide range of hardware platforms, configurations and computer platform distributions. The framework has until now been interfaced to 7 different hardware platforms and has enabled many application i.e. robust navigation in an orchard with an autonomous tractor (Andersen,2010). Furthermore by providing a simple scripting robot control language the system also supports use by non-technicians.

Combining a Novel Computer Vision Sensor with a Cleaning Robot to Achieve Autonomous Pig House Cleaning

Cleaning of livestock buildings is the single most health-threatening task in the agricultural industry and a transition to robot-based cleaning would be instrumental to improving working conditions for employees. Present cleaning robots fall short on cleanness quality, as they cannot perform condition based cleaning. This paper describes how a novel sensor, developed for the purpose, and algorithms for classification and learning are combined with a commercial robot to obtain an autonomous system which meets the necessary quality attributes. These include features to make selective cleaning where dirty areas are detected, that operator assistance is called only when cleanness hypothesis cannot be made with confidence. The paper describes the design of the system where learning from experience maps and operator instructions are combined to obtain a smart and autonomous cleaning robot.

Visual Positioning and Docking of Non-holonomic Vehicles

The paper describes the benefits and drawbacks of different strategies for docking and positioning of autonomous vehicles based on visual feedback. Three algorithms are described in detail, and extensive experiments with an implementation of the algorithms on our test-bed Autonomous Guided Vehicle are documented.

Vision Assisted Laser Scanner Navigation for Autonomous Robots

This paper describes a navigation method based on road detection using both a laser scanner and a vision sensor. The method is to classify the surface in front of the robot into traversable segments (road) and obstacles using the laser scanner, this classifies the area just in front of the robot (2.5 m). The front looking camera is used to classify the road from this distance and forward, taking a seed area from the laser scanner data and from this estimate the outline of the visible part of the road. The method has been tested successfully on gravelled and asphalt roads in a national park environment.