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Compliant navigation in crowded environments
The goal of this project is to develop a novel algorithm for compliant robot navigation in crowded environments.
Keywords: Navigation; planning; object detection, tracking and prediction; computer Vision
Reliable object detection and avoidance is crucial for real-world operations in crowded environments. When autonomous ground vehicles operate in complex places such as warehouses, supermarkets or hospitals, they not only need to reliably detect and avoid static objects but also incorporate the existence and behavior of dynamic objects into their planning, ideally in a socially compliant way.
The goal of this project is to develop novel algorithms to improve the detection of dynamic and static objects in the robot’s vicinity and to use these detection to safely navigate to a predefined goal. To this purpose, the student may investigate motion models of pedestrians, robustify object detection and tracking algorithms, and develop a path planner to incorporate dynamic objects. He/she will be invited to contribute to a common framework and will have the chance to work with close-to-market robot prototypes. Finally, by the end of the project, the student will have developed considerable knowledge in the very challenging topics of object detection, tracking, motion prediction and path planning. The hands-on experience with a mobile robotic platform will further augment this experience with a deep understanding of the software-hardware interplay.
**What We Offer**
- Possibility to contribute to ongoing research in the exciting and quickly developing field of robotic navigation in public spaces.
- Work with a team of enthusiastic roboticists and researchers in a Zurich based robotics startup in collaboration with the Autonomous Systems Lab, one of the largest robotic labs in the world.
- Possibility to deploy your algorithms to different robotic platforms and highly-valued hands-on experience.
Reliable object detection and avoidance is crucial for real-world operations in crowded environments. When autonomous ground vehicles operate in complex places such as warehouses, supermarkets or hospitals, they not only need to reliably detect and avoid static objects but also incorporate the existence and behavior of dynamic objects into their planning, ideally in a socially compliant way.
The goal of this project is to develop novel algorithms to improve the detection of dynamic and static objects in the robot’s vicinity and to use these detection to safely navigate to a predefined goal. To this purpose, the student may investigate motion models of pedestrians, robustify object detection and tracking algorithms, and develop a path planner to incorporate dynamic objects. He/she will be invited to contribute to a common framework and will have the chance to work with close-to-market robot prototypes. Finally, by the end of the project, the student will have developed considerable knowledge in the very challenging topics of object detection, tracking, motion prediction and path planning. The hands-on experience with a mobile robotic platform will further augment this experience with a deep understanding of the software-hardware interplay.
**What We Offer**
- Possibility to contribute to ongoing research in the exciting and quickly developing field of robotic navigation in public spaces. - Work with a team of enthusiastic roboticists and researchers in a Zurich based robotics startup in collaboration with the Autonomous Systems Lab, one of the largest robotic labs in the world. - Possibility to deploy your algorithms to different robotic platforms and highly-valued hands-on experience.
- Make yourself familiar with our robotic perception and planning framework as well as the current state-of-the-art.
- Build upon the state of the art by developing your own ideas and your supervisor's input.
- Design, test, and deploy a real-time capable solution for compliant navigation in crowds.
- Design and conduct experiments with a mobile robot to evaluate the selected approach.
- Make yourself familiar with our robotic perception and planning framework as well as the current state-of-the-art. - Build upon the state of the art by developing your own ideas and your supervisor's input. - Design, test, and deploy a real-time capable solution for compliant navigation in crowds. - Design and conduct experiments with a mobile robot to evaluate the selected approach.
- Strong self-motivation and curiosity for solving challenging robotic problems.
- Previous experience in computer vision, or path planning.
- Excellent C++ programming skills, Python is a plus.
- Experience with Linux, ROS, and typical development tools such as git are advantageous.
- A very good academic record is desirable but may be compensated by expert knowledge in the areas mentioned above.
- Strong self-motivation and curiosity for solving challenging robotic problems. - Previous experience in computer vision, or path planning. - Excellent C++ programming skills, Python is a plus. - Experience with Linux, ROS, and typical development tools such as git are advantageous. - A very good academic record is desirable but may be compensated by expert knowledge in the areas mentioned above.
If you are interested, please send your transcripts and CV to Thomas Eppenberger (thomas.eppenberger@sevensense.ch), Dina Youakim (dina.youakim@sevensense.ch) and Renaud Dubé (renaud.dube@sevensense.ch).
If you are interested, please send your transcripts and CV to Thomas Eppenberger (thomas.eppenberger@sevensense.ch), Dina Youakim (dina.youakim@sevensense.ch) and Renaud Dubé (renaud.dube@sevensense.ch).