Jeg har spesialisert meg på utvikling av robotiserte løsninger, fra hardware arkitektur til etablering av kontrollrutiner og systemintegrering på fysiske plattformer. Spesialområdene omfatter navigasjon av UGV, menneske-maskin-grensesnittet, helintegrerte systemer og heterogent multi-robot-samarbeid. Jeg jobber med utvikling av nye robotiserte tilnærminger for presisjonsjordbruket.
In this paper, we present a novel method for obstacle avoidance designed for a nonholonomic mobile robot. The method relies on light detection and ranging (LiDAR) readings, which are mapped into a polar coordinate system. Obstacles are taken into consideration when they are within a predefined radius from the robot. A central part of the approach is a new Heading Weight Function (HWF), in which the beams within the aperture angle of the LiDAR are virtually weighted in order to generate the best trajectory candidate for the robot. The HWF is designed to find a solution also in the case of a local-minima situation. The function is coupled with the robot’s controller in order to provide both linear and angular velocities. We tested the method both by simulations in a digital environment with a range of different static obstacles, and in a real, experimental environment including static and dynamic obstacles. The results showed that when utilizing the novel HWF, the robot was able to navigate safely toward the target while avoiding all obstacles included in the tests. Our findings thus show that it is possible for a robot to navigate safely in a populated environment using this method, and that sufficient efficiency in navigation may be obtained without basing the method on a global planner. This is particularly promising for navigation challenges occurring in unknown environments where models of the world cannot be obtained.
The key factor for autonomous navigation is efficient perception of the surroundings,while being able to move safely from an initial to a final point. We deal in this paper with a wheeled mobile robot working in a GPS-denied environment typical for a greenhouse. The Hector Simultaneous Localization and Mapping (SLAM) approach is used in order to estimate the robots’ pose using a LIght Detection And Ranging (LIDAR) sensor. Waypoint following and obstacle avoidance are ensured by means of a new artificial potential field (APF) controller presented in this paper. The combination of the Hector SLAMand the APF controller allows themobile robot to performperiodic tasks that require autonomous navigation between predefined waypoints. It also provides themobile robot with a robustness to changing conditions thatmay occur inside the greenhouse, caused by the dynamic of plant development through the season. In this study, we show that the robot is safe to operate autonomously with a human presence, and that in contrast to classical odometrymethods, no calibration is needed for repositioning the robot over repetitive runs. We include here both hardware and software descriptions, as well as simulation and experimental results.
SolarFarm - Exploring solar on-farm energy production combined with a fleet of electrical vehicles and precision agriculture for reduced GHG-emissions
SolarFarm - En systemstudie av hvordan solstrøm produsert på gårdsnivå kan drive elektriske og delvis selvstyrte farkoster i et presisjonsjordbruk med reduserte utslipp av klimagasser