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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Xibo Li Shruti Patel David Stronzek-Pfeifer Christof Büskens working group Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen Germany

Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated framework for indoor localization and experimental validation of an autonomous driving system based on an advanced driver-assistance system (ADAS) model car. The global pose of the model car is obtained by fusing information from fiducial markers, inertial sensors and wheel odometry. In order to achieve robust localization, we investigate and compare two extensions to the Extended Kalman Filter; first with adaptive noise tuning and second with $\chi^{2}-\mathbf{testing}$ for measurement outlier detection. An efficient and low-cost ground truth measurement method using a single LiDAR sensor is also proposed to validate the results. The performance of the localization algorithms is tested on a complete autonomous driving system with trajectory planning and model predictive control.

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Indoor Localization for an Autonomous Model Car: A Marker-Based Multi-Sensor Fusion Framework

arXiv

引用

arXiv 2023年

作者： Li, Xibo Patel, Shruti Stronzek-Pfeifer, David Büskens, Christof The Working Group Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen28359 Germany

Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated framework for indoor localization and experimental validation of an autonomous driving system based on an advanced driver-assistance system (ADAS) model car. The global pose of the model car is obtained by fusing information from fiducial markers, inertial sensors and wheel odometry. In order to achieve robust localization, we investigate and compare two extensions to the Extended Kalman Filter;first with adaptive noise tuning and second with χ2−testing for measurement outlier detection. An efficient and low-cost ground truth measurement method using a single LiDAR sensor is also proposed to validate the results. The performance of the localization algorithms is tested on a complete autonomous driving system with trajectory planning and model predictive control. Copyright © 2023, The Authors. All rights reserved.

关键词： Fault detection

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Let Hybrid A∗ Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework

arXiv

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arXiv 2024年

作者： Li, Xibo Patel, Shruti Büskens, Christof The working group Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen28359 Germany TOPAS Industriemathematik InnovationgGmbH Bremen28359 Germany

Deep reinforcement learning (DRL) allows a system to interact with its environment and take actions by training an efficient policy that maximizes self-defined rewards. In autonomous driving, it can be used as a strategy for high-level decision making, whereas low-level algorithms such as the hybrid A∗ path planning have proven their ability to solve the local trajectory planning problem. In this work, we combine these two methods where the DRL makes high-level decisions such as lane change commands. After obtaining the lane change command, the hybrid A∗ planner is able to generate a collision-free trajectory to be executed by a model predictive controller (MPC). In addition, the DRL algorithm is able to keep the lane change command consistent within a chosen time-period. Traffic rules are implemented using linear temporal logic (LTL), which is then utilized as a reward function in DRL. Furthermore, we validate the proposed method on a real system to demonstrate its feasibility from simulation to implementation on real hardware. © 2024, CC BY.

关键词： Motion planning

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***.15 Kanaria: Identifying the challenges for cognitive autonomous navigation and guidance for missions to small planetary bodies 66

***.15 Kanaria: Identifying the challenges for cognitive aut...

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66th International Astronautical Congress 2015: Space - The Gateway for Mankind's Future, IAC 2015

作者： Probst, Alena Pcytavf, Graciela Gonzalez Nakath, David Schattel, Anne Rachuy, Carsten Langef, Patrick Clemens, Joachim Echim, Mitja Schwarting, Verena Srinivas, Abhishek Gadzicki, Konrad Fdrstner, Roger Eissfeller, Bernd Schill, Kerstin Biiskens, Christof Zachmann, Gabriel Space Technology Dept. Institute of Space Technology and Applications Bundeswehr University Munich Germany Navigation Dept. Institute of Space Technology and Applications Bundeswehr University Munich Germany Sensor Fusion Group Cognitive Neuroinformatics University of Bremen Germany Working group Optimization and Optimal Control University of Bremen Germany Autonomy Group Cognitive Neuroinformatics University of Bremen Germany Computer Graphics and Virtual Reality University of Bremen Germany

ISBN: (纸本)9781510818934

Problems and the calculation of trajectory sensitivities for on-board stability analysis as well as real-time optimal control are explained. Bringing cognitive autonomy to a spacecraft requires an on-board computational module as a central spacecraft component. This module is responsible for state evaluation, mission planning and decision-making regarding selection of potential targets, trajectory selection and FD1R. A knowledge-base serves as a database for decision making processes. With the aim to validate and test our methods, we create a virtual environment in which humans can interact with the simulation of the mission. In order to achieve real-time performance, we propose a massively-parallel software system architecture, which enables very efficient and easily adaptable communication between concurrent software modules within KaNaRiA.,With the rapid evolution of space technologies and increasing thirst for knowledge about the origin of life and the universe, the need for deep space missions as well as for autonomous solutions for complex, time-critical mission operations becomes urgent. Within this context, the project KaNaRiA aims at technology development tailored to the ambitious task of space resource mining on small planetary bodies using increased autonomy for on-board mission planning, navigation and guidance. This paper focuses on the specific challenges as well as first solutions and results corresponding to the KaNaRiA mission phases (1) interplanetary cruise, (2) target identification and characterization and (3) proximity operations. Based on the KaNaRiA asteroid mining mission objectives, initially, a mission reference scenario as well as a reference mission architecture are described in this paper. KaNaRiA has been proposed as a multi-spacecraft mission to the asteroid main belt. Composed of a flock of prospective scout spacecraft, a mother ship carrying the mining payload and several service modules placed on a 2.8 AU parking orbit around the

关键词： Asteroids

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Ancora Pharmaceuticals: Counting Carbs Synthetically

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CHEMISTRY & BIOLOGY 2010年第2期17卷 99-100页

作者： McCarthy, Alice Main Text With many biotech companies reaching for commercial success it helps when the founding science comes from an unquestioned leader in the field. Such is the case with Ancora Pharmaceuticals (http://***) founded in 2003 to develop a synthetic carbohydrate platform based on work licensed from MIT. In 2002 the company's scientific cofounder Dr. Peter Seeberger a world-class carbohydrate chemist then based at MIT helped establish the company after winning an MIT-based entrepreneurial competition setting in motion the foundation for the now 20 person company based in Medford Massachusetts. Ancora's technology overcomes the labor-intensive nature of traditional carbohydrate synthesis. Why Carbohydrates? When seeking to target an infectious agent the first place to look is usually the invading organism's cell surface which is made up of proteins lipids and carbohydrates. For decades medicine has been perfecting protein-based approaches both in drugs and vaccines. Yet carbohydrates extend well into the cell's neighborhood and are often involved in initial interactions with other cells and in cell signaling. This makes them attractive antigens explaining why they are of interest for vaccine development. In the vaccine field perhaps the leading application for carbohydrate-based therapies to date many opportunities go unpursued or are pursued until they reach a technical hurdle that cannot be overcome. “This is mainly due to poor access to large amounts of homogenous carbohydrate material to work with” explains Stewart Campbell Ph.D. Ancora's vice president of R&D. However isolating or purifying attractive carbohydrate targets from a bacterium fungus or other organism in the quantities needed can be challenging. “There are a number of reasons why carbohydrate synthesis is more attractive than isolation from the pathogen” explains Geert-Jan Boons Ph.D. Franklin Professor of Chemistry Complex Carbohydrate Research Center University of Georgia. Dr. Boons's lab is focused on the

With many biotech companies reaching for commercial success, it helps when the founding science comes from an unquestioned leader in the field. Such is the case with Ancora Pharmaceuticals (http://***), founded in 2003 to develop a synthetic carbohydrate platform based on work licensed from MIT. In 2002, the company’s scientific cofounder, Dr. Peter Seeberger, a world-class carbohydrate chemist then based at MIT, helped establish the company after winning an MIT-based entrepreneurial competition, setting in motion the foundation for the now 20 person company based in Medford, Massachusetts.

关键词： success science companies

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