A Neural Network-Based Approach for the Identification and Compensation of Magnetic Disturbances in Mobile Robot Localization
- Resource Type
- Conference
- Authors
- Stefanoni, Massimo; Odry, Akos; Sarcevic, Peter
- Source
- 2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI) Applied Computational Intelligence and Informatics (SACI), 2023 IEEE 17th International Symposium on. :000393-000398 May, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Magnetometers
Service robots
Robot kinematics
Pose estimation
Artificial neural networks
Sensor fusion
Position measurement
magnetometer
disturbance compensation
mobile robot
neural network
localization
- Language
- ISSN
- 2765-818X
Magnetometers are widely used in sensor fusion frameworks of mobile robots due to their ability to work as compasses. The measurements of these sensors are largely influenced by hard iron and soft iron effects that are caused by nearby ferromagnetic objects. In this paper, an artificial neural network (ANN)-based method is proposed for the identification of such effects, which can be compensated from the measurements. The method utilizes both the distance and the angle of the detected object based on the coordinate frame of the mobile robot as the inputs of the Multi-Layer Perceptron (MLP) ANN. The MLP provides on its outputs the disturbance levels in the three axes, which need to be subtracted from the measurements to obtain the compensated values. The method was tested with three different types of objects to evaluate the performance of the method. Measurements were collected using an industrial robotic arm in a grid around the objects. The disturbance levels generated by the objects were computed by subtracting measurements recorded in an undisturbed scenario. Three-layer MLPs were tested with various numbers of hidden layer neurons to find the optimal configurations. The achieved results show that the disturbance levels can be significantly decreased using the proposed method.