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Autonomous, Information-Based Radiation Mapping using a UAV
The goal of this problem is to develop and implement a exploration and mapping strategy for autonomous radiation mapping using a small Unmanned Aerial vehicle (UAV), equipped with visual, inertial and radiation sensors onboard.
Unmanned systems can be considered as an ideal tool to collect data in hazardous areas without putting humans in danger. An example of a hazardous environment is one following nuclear events, such as after accidents or terrorist attacks with dirty bombs”. Being able to quickly increase the situational awareness, e.g. with respect to the actual level of contamination or the presence of human casualties, is an important requirement to limit hazards to first responders, such as search and rescue or decontamination teams and to increase their planning efficiency.
Starting with zero knowledge about its area of interest, the UAV should explore its environment autonomously while estimating a map of the radiation field and avoid obstacles on its way. The path of the UAV should be selected with the objective to maximize the information gathered by the UAV taking into account the inherent limitations of the UAV and the radiation sensor (such as limited battery lifetime and sensing sensitivity). Finally as the motion estimation for the UAV is not perfect, the corresponding resulting uncertainty should be taken into account in both mapping and the planning strategy.
The student taking this project needs to be highly motivated, preferably with strong analytical skills,
while experience in coding in C/C++ would be beneficial. The student will have the opportunity to
work with a real setup and equipment offered by V4RL. This work is part of a project for civilian
protection with Armasuisse and a successful method will directly be used within in the framework
developed for this project.
Unmanned systems can be considered as an ideal tool to collect data in hazardous areas without putting humans in danger. An example of a hazardous environment is one following nuclear events, such as after accidents or terrorist attacks with dirty bombs”. Being able to quickly increase the situational awareness, e.g. with respect to the actual level of contamination or the presence of human casualties, is an important requirement to limit hazards to first responders, such as search and rescue or decontamination teams and to increase their planning efficiency.
Starting with zero knowledge about its area of interest, the UAV should explore its environment autonomously while estimating a map of the radiation field and avoid obstacles on its way. The path of the UAV should be selected with the objective to maximize the information gathered by the UAV taking into account the inherent limitations of the UAV and the radiation sensor (such as limited battery lifetime and sensing sensitivity). Finally as the motion estimation for the UAV is not perfect, the corresponding resulting uncertainty should be taken into account in both mapping and the planning strategy.
The student taking this project needs to be highly motivated, preferably with strong analytical skills, while experience in coding in C/C++ would be beneficial. The student will have the opportunity to work with a real setup and equipment offered by V4RL. This work is part of a project for civilian protection with Armasuisse and a successful method will directly be used within in the framework developed for this project.
1. Literature research on active sensing and radiation mapping.
2. Development of a basic mapping method allowing to asses the gathered information.
3. Development of a planning strategy to reduce the uncertainty of the radiation map assuming known poses.
4. Extend the planning/mapping strategy to incorporate uncertainty on the robot's poses.
5. Evaluate the approach on a real setup
1. Literature research on active sensing and radiation mapping. 2. Development of a basic mapping method allowing to asses the gathered information. 3. Development of a planning strategy to reduce the uncertainty of the radiation map assuming known poses. 4. Extend the planning/mapping strategy to incorporate uncertainty on the robot's poses. 5. Evaluate the approach on a real setup
- C++ programming experience
- Background in visual SLAM/sensor fusion and path planning desired
- Experience in mobile robotics, Linux, ROS are beneficial
- C++ programming experience - Background in visual SLAM/sensor fusion and path planning desired - Experience in mobile robotics, Linux, ROS are beneficial
Interested Students please send CV and Master transcripts to Marco Karrer (marco.karrer@mavt.ethz.ch) with CC to Luca Bartolomei (lbartolomei@ethz.ch).
Interested Students please send CV and Master transcripts to Marco Karrer (marco.karrer@mavt.ethz.ch) with CC to Luca Bartolomei (lbartolomei@ethz.ch).