This is the README file for the OxNet Science Programme seminar delivered by Prof. Nick Hawes about robot navigation. If you are on the OxNet programme and have any questions about the data or code, please talk to the OxNet support team or get in touch with Thomas Hird [email protected].
The data is provided in a comma-separated value file data/tsc_y3_nav_stats.csv. This file contains the navigation data from a mobile robot from the STRANDS project (a European robotics project) moving over a navigation graph structure (which we refer to a "topological map") in a office environment. For more information on the STRANDS project and the robotics technology, see the article The STRANDS Project: Long-Term Autonomy in Everyday Environments.
Each line in the data file describes an attempt to navigate an edge in the robot's topological map. The edge being navigated is indicated by the edge_id. This is indicated by the edge_id. The navigation attempt starts from the origin node with the aim of reaching target. It may successfully reach that node, or it may end up somewhere else. The place it ends up is final_node. Each node is a 2D point plus an influence zone which is an area around that point. The operation_time field indicates the duration (in seconds) of the navigation action until the influence zone is reached. The time_to_waypoint is the time spend in the navigation action after the influence zone is reached until the action is terminated. Termination conditions may be the robot reaching the 2D waypoint for the node, having a new action superseding the current one, or failing (in which case the action is cancelled by another process due to a timeout or another failure condition). These durations can be matched to the dates as follows, operation_time = date_finished - date_started, time_to_waypoint = date_finished - date_at_node but they were recorded with sub-second accuracy.
You are provided with a Jupyter notebook in notebooks/robot_navigation_statistics.ipynb. This notebook provides the Python code necessary to load the data and manipulate it in various ways.
For the homework you should work through the provided Jupyter notebook, reading each entry and doing your best to understand each cell of Python code. Feel free to edit the code, or run additional tests, as you wish. At two locations in the file there are lists of tasks for you to attempt in order to explore the data further. Everyone should attempt at least two tasks, i.e. the first task in each list. Save all your attempts in either the provided notebook or a separate one, and then you can submit your homework by submitting your edited notebook.
You will have a Q&A specifically on this homework on Thursday 9th March.