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Visual Pallet Detection & Autonomous Docking for Industrial Robots
The goal of this project is to develop a visual pallet detection software solution and a path planner for autonomous docking.
Keywords: Computer Vision, Object Detection, Deep Learning, Path Planning
Autonomous mobile robots (AMR), such as forklifts, operate in human populated factories. Picking, transporting and unloading of pallets is one of the standard tasks. For autonomous pallet picking, conventional solutions require that the position of the pallet is known within a few centimeters, and fine adjustment may then be ensured by small laser scanners in the AMR's forks. Because of the 24/7 operation of these robots, it is essential that these solutions are very reliable, otherwise high costs will arise.
In this project we want to develop a camera based autonomous pallet loading solution, which tolerates a wide range of initial positions, and ideally is also able to detect locations of multiple stacked pallets. We envision the deployment of such a system on real industrial robots, featuring the detection and exact localization of pallets and performing precise docking maneuvers, while avoiding any human workers surrounding the robots.
The goal of this project is to develop a visual pallet detection system that precisely estimates the presence and orientation of pallets. Furthermore, a suitable path planning algorithm will be designed for precise docking on these pallets. The software will be first tested in simulation and then be deployed and evaluated on a real robot with restricted computational power.
You will have a chance to work with us hand-in-hand on our perception and navigation system, involving object detection, mapping, and path planning to finally deploy your algorithms on commercial robotic platforms. By the end of the project, you will have developed a great amount of experience related to deep learning, computer vision, perception, volumetric mapping, path planning, software engineering and mobile robotics.
What We Offer: Possibility to contribute to ongoing research in the exciting and quickly developing fields of computer vision, path planning and deep learning. Work with and be part of 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.
Autonomous mobile robots (AMR), such as forklifts, operate in human populated factories. Picking, transporting and unloading of pallets is one of the standard tasks. For autonomous pallet picking, conventional solutions require that the position of the pallet is known within a few centimeters, and fine adjustment may then be ensured by small laser scanners in the AMR's forks. Because of the 24/7 operation of these robots, it is essential that these solutions are very reliable, otherwise high costs will arise.
In this project we want to develop a camera based autonomous pallet loading solution, which tolerates a wide range of initial positions, and ideally is also able to detect locations of multiple stacked pallets. We envision the deployment of such a system on real industrial robots, featuring the detection and exact localization of pallets and performing precise docking maneuvers, while avoiding any human workers surrounding the robots.
The goal of this project is to develop a visual pallet detection system that precisely estimates the presence and orientation of pallets. Furthermore, a suitable path planning algorithm will be designed for precise docking on these pallets. The software will be first tested in simulation and then be deployed and evaluated on a real robot with restricted computational power.
You will have a chance to work with us hand-in-hand on our perception and navigation system, involving object detection, mapping, and path planning to finally deploy your algorithms on commercial robotic platforms. By the end of the project, you will have developed a great amount of experience related to deep learning, computer vision, perception, volumetric mapping, path planning, software engineering and mobile robotics.
What We Offer: Possibility to contribute to ongoing research in the exciting and quickly developing fields of computer vision, path planning and deep learning. Work with and be part of 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 frameworks as well as current state-of-the-art object detection solutions.
- Design, train and evaluate different visual pallet detection methods.
- Design, implement and test an autonomous pallet docking path planner for simulation and hardware.
- Build upon the state of the art by developing your own ideas and your supervisor's input.
- Design and conduct experiments with a mobile robot to evaluate the selected approach.
- Make yourself familiar with our robotic perception and planning frameworks as well as current state-of-the-art object detection solutions. - Design, train and evaluate different visual pallet detection methods. - Design, implement and test an autonomous pallet docking path planner for simulation and hardware. - Build upon the state of the art by developing your own ideas and your supervisor's input. - 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 the fields of computer vision, path planning and deep learning
- Excellent programming skills, ideally in Python and C++
- 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 the fields of computer vision, path planning and deep learning - Excellent programming skills, ideally in Python and C++ - 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) and Yannick Huber (yannick.huber@sevensense.ch).
If you are interested, please send your transcripts and CV to Thomas Eppenberger (thomas.eppenberger@sevensense.ch) and Yannick Huber (yannick.huber@sevensense.ch).