UAV Navigation using Local Computational Resources: Keeping a target in sight
Master's thesis presentation
Time: Tue 2021-01-19 13.00
Lecturer: Magnus Cardell
When tracking a moving target, an Unmanned Aerial Vehicle (UAV) must keep the target within its sensory range while simultaneously remaining aware of its surroundings. However, small flight computers must have sufficient environmental knowledge and computational capabilities to provide real-time control to function without a ground station connection. Using a Raspberry Pi 4 model B, this thesis presents a practical implementation for evaluating path planning generators in the context of following a moving target.
The practical model integrates two waypoint generators for the path planning scenario: A*and 3D Vector Field Histogram* (3DVFH*). The performances of the path planning algorithms are evaluated in terms of the required processing time, distance from the target, and memory consumption. The simulations are run in two types of environments. One is modelled by hand with a target walking a scripted path. The other is procedurally generated with a random walker. The study shows that 3DVFH* produces paths that follow the moving target more closely when the actor follows the scripted path. With a random walker, A* consistently achieves the shortest distance. Furthermore, the practical implementation shows that the A* algorithm’s persistent approach to detect and track objects has a prohibitive memory requirement that the Raspberry Pi 4 with a 2 GB RAM cannot handle. Looking at the impact of object density, the 3DVFH* implementation shows no impact on distance to the moving target, but exhibits lower execution speeds at an altitude with fewer obstacles to detect. The A* implementation has a marked impact on execution speeds in the form of longer distances to the target at altitudes with dense obstacle detection.
This research project also realized a communication link between the path planning implementations and a Geographical Information System (GIS) application supported by the Carmenta Engine SDK to explore how locally stored geospatial information impact path planning scenarios. Using VMap geospatial data, two levels of increasing geographical resolution were compared to show no performance impact on the planner processes, but a significant addition in memory consumption. Using geospatial information about a region of interest, the waypoint generation implementations are able to query the map application about the legality of its current position.
Keywords: Unmanned aerial vehicle, Path planning, On-board computation, Autonomy