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Discrimination of contact and aerodynamic forces during interaction with an MAV
High-accuracy interaction and manipulation of the environment with flying MAVs requires a precise knowledge of interaction forces and torques. This project intends to improve aerial interaction by optimally estimating interaction forces exploiting multiple sensor measurements.
Keywords: Aerial interaction, Micro Aerial Vehicle, Estimation, Filtering, EKF, Modeling and Control
Overactuated MAVs have received a growing attention in recent years as they allow for physical interaction with their environment.
Most interaction tasks so far have been conducted in controlled laboratory environments with little to no outside disturbances.
In order to perform high-accuracy interaction tasks in outdoor conditions, we need to investigate methods to reliably distinguish wrenches (i.e. forces and torques) that arise from interaction at the contact point from wrenches that arise from external disturbances (such as aerodynamic effects from wind).
The applicable methods to achieve this depend heavily on the used sensor equipment. We propose to use a combination of odometry and force/torque sensors, together with a numerical model of the platform dynamics, to estimate contact and disturbance forces simultaneously.
This project will include a preliminary literature research on model-based filtering methods (e.g. Kalman-Filter, EKF, UKF, Particle filter, also specifically for MAVs). Following this, an estimation framework will be designed and implemented in simulation. Real-world experiments will then verify the quality of the estimates (comparing them to ground-truth measurements).
Overactuated MAVs have received a growing attention in recent years as they allow for physical interaction with their environment. Most interaction tasks so far have been conducted in controlled laboratory environments with little to no outside disturbances. In order to perform high-accuracy interaction tasks in outdoor conditions, we need to investigate methods to reliably distinguish wrenches (i.e. forces and torques) that arise from interaction at the contact point from wrenches that arise from external disturbances (such as aerodynamic effects from wind). The applicable methods to achieve this depend heavily on the used sensor equipment. We propose to use a combination of odometry and force/torque sensors, together with a numerical model of the platform dynamics, to estimate contact and disturbance forces simultaneously. This project will include a preliminary literature research on model-based filtering methods (e.g. Kalman-Filter, EKF, UKF, Particle filter, also specifically for MAVs). Following this, an estimation framework will be designed and implemented in simulation. Real-world experiments will then verify the quality of the estimates (comparing them to ground-truth measurements).
1. Literature study on model-based filtering
2. Design of a filtering/estimation framework, given a set of available sensors
3. Implementation and evaluation in simulation, possibly re-iterations
4. Evaluation in real-world experiments
1. Literature study on model-based filtering 2. Design of a filtering/estimation framework, given a set of available sensors 3. Implementation and evaluation in simulation, possibly re-iterations 4. Evaluation in real-world experiments
Solid understanding of control theory with hands on experience
Experience with filtering algorithms and/or model-based control
Strong background in ROS, C++ and Python, Git
Independent problem solving skills
Desirable: Hands-on hardware experience
Solid understanding of control theory with hands on experience Experience with filtering algorithms and/or model-based control Strong background in ROS, C++ and Python, Git Independent problem solving skills Desirable: Hands-on hardware experience
Maximilian Brunner: maximilian.brunner@mavt.ethz.ch
Maximilian Brunner: maximilian.brunner@mavt.ethz.ch