Introduction and Motivation
The Vector Field Histogram approach to Obstacle Avoidance allows real-time obstacle avoidance for our drones, through
Its improved method of clustering LiDAR detections for each oscillation into fixed angular sectors
Calculating obstacle densities for each sector based on distance and number of detections
Summarising the 2D FoV of our drone into a Polar Histogram for each LiDAR oscillation
Clustering safe sectors into ‘candidate valleys' based on obstacle-density data
Choosing the best candidate valley based on the drone’s immediate next waypoint
Deflecting the drone to the best candidate valley chosen (or stopping and reversing if no candidate valleys exist)
(The Vector Field Histogram approach is named such because it constructs a histogram grid representing obstacle densities in a polar coordinate system and uses a vector field to determine a feasible navigation path)
The previously implemented Bendy-Ruler approach relied on reactive bending around obstacles directly in front of it, whereas the improved approach (due to its broadened scope and applicability) extends beyond just reactive manoeuvres, giving us a more detailed understanding of obstacle distribution.
Key improvements of the updated system include:
Smarter Path Selection: Instead of making immediate reactive turns or complete stops when an obstacle is detected, the new approach evaluates multiple trajectory options and selects the most optimal path.
Improved Obstacle Representation through Density-Clustering: Clusters LiDAR obstacle data by density (calculated using proximity and number of readings in a sector) rather than shape, which is more relevant when choosing a safe path to deflect the drone.
Reduced Sensitivity to Sensor Noise: By aggregating LiDAR data for an entire oscillation into a histogram with obstacle-density values for discrete sectors calculated independently, VFH reduces the likelihood of considering ‘noise’, or faulty and isolated LiDAR readings.
Waypoint Integration: The optimised approach considers the drone’s next waypoint when making a deflection, balancing obstacle-avoidance with global path-following and keeping the drone on course while manoeuvring safely around unsafe areas.
Overview and Background
The key idea behind VFH is constantly visualising the entire 2D FoV of the drone by calculating obstacle density values for each (e.g 5o wide) sector, allowing us to make more informed, planned, and optimal path deflections as the LiDAR data flows through.
The system is designed as a modular pipeline that processes real-time LiDAR data, extracts obstacle densities, and computes safe navigation paths for the drone. The pipeline operates in a continuous loop, where LiDAR scans are parsed into oscillations, transformed into sector obstacle density maps, and fed into a polar histogram. For each oscillation, this histogram is then analysed to determine the best flight direction, and commands are relayed to the drone using the Flight Interface (switching to GUIDED mode during deflection) and then continuing the autonomous mission as planned.
For a more detailed read on the working and motivation behind this approach, please skim through this link: https://www.cs.cmu.edu/~motionplanning/papers/sbp_papers/integrated1/borenstein_VFHisto.pdf