This repository contains the datasets and scripts used in the experimentation for localization algorithm using Wireless signal Collaborative Direction of Arrival (CDOA) estimated using a mobile robot - wireless sensor network (WSN) cooperation mechanism in real-time. Three set of experimentational content is provided in this repository: 1. Simulation (Datasets and Codes), 2. Real-world public datasets from the literature (Dataset1 [1] and Dataset2 [2]), 4. Real Robot Hardware (ROS package and its Dataset). Each content in enclosed in separate folder with the respective name. Simulation scripts generate data internally, However, realworld exprimentation require data files which are included with the respective folder in the repository.
The proposed localization framework can be visualizes as:
On the Left, the Wireless Sensor Network (WSN) anchor nodes are placed at the corners and the Mobile Robot can be localized within the WSN's boundary polygon.
On the Right, the Access Points (AP) are placed at the corers and the mobile robot can be localized within thie AP boundaries.
Although we present the experimental results for the WSN nodes/AP anchors in a specific square/triangular shape, the approach can be generalized to other configurations as long as all the nodes are not colinear.
A hardware demonstration experiment setup is shown below using the configuration of WSN anchor nodes.
Below, we present different datasets and associated robot localization trajectories or information.
Simulation experimentations taken place for 6 x 6 meter of bounded region where Access Point is placed on the top of the moving robot and wireless sensor nodes places at the fixed positions (corners) which are (0,0), (0,6), (6,0), and (6,6) respectively. Robot followed three different trajectoriers: Inside, Boundary and Diagonal. For each of the trajectory we predicted the position using multiple localization techniques and proposed approach as well.
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In the Simulation folder there is a separate python script file for each localization technique.
To execute the script run: $ python3 <script_file>
It contains script files same as simulation to run algorithms on data available in csv files.
To execute the script run: $ python3 <script_file> <dataset file>
Experimenatation Testbed and datapoints can be visualized as:
The reference at [1] povides details of the dataset completely.
It contains script files same as simulation to run algorithms on data available in csv files.
To execute the script run: $ python3 <script_file> <dataset file>
Experimenatation Testbed and datapoints can be visualized as:
The reference at [2] povides details of the dataset completely.
This folder uses a ROS package (ros_network_analysis) which consists of wireless publisher node scripts along with the server subscriber script. For latest source code on this package, please download the ros_network_analysis package from its source code following the instructions at https://github.com/herolab-uga/ros-network-analysis
To launch the network_analysis run: $ roslaunch network_analysis wireless_quality.launch <node_id>
Another ROS package named "ros_rssi_collaboration" needs to be installed to run the RSSI node collaboration algorithm and receive rssi from each node in a synchronous way.
To launch the rssi_collaboration run: $ roslaunch rssi_collaboration rssi_collaboration.launch
Furthemore, a folder contains dataset in the form of rosbags, one can easily extract data rosbags. However, we also have provided dataset int he form of .csv for convinience in the respective folders. It also contains script file to run algorithms on data available in csv files.
To run localziation over recorded odom and rssi, execute the script run: $ python3 <script_file> <dataset file>
ROS package named "ros_pf_doa_localization" needs to be installed to run the online localization over received rssi through node collaboration in a synchronous fasion.
To launch the ros_pf_doa_localization run: $ roslaunch ros_pf_doa_localization ros_pf_doa_localization.launch
It will launch online localization script which receives RSSI from connected nodes and provide real-time pose estimation along with the ground truth.
Experimenatation Testbed and datapoints can be visualized as:
[1] Pachos et al. "Rssi dataset for indoor localization fingerprinting." [Online]. Available: https://github.com/pspachos/RSSI-Dataset-for-Indoor-Localization-Fingerprinting.git
[2] P. Szeli ́nski and M. Nikodem, "Multi-channel BLE RSSI measurements for indoor localization," 2021. [Online]. Available: https://dx.doi.org/10.21227/hc74-3s30
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Ehsan Latif - PhD Candidate
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Dr. Ramviyas Parasuraman - Principal Investigator
Heterogeneous Robotics Lab (HeRoLab), School of Computing, University of Georgia. http://hero.uga.edu
For further information, contact Ehsan Latif ehsan.latif@uga.edu or Prof. Ramviyas Parasuraman ramviyas@uga.edu
