In autonomous robotics and unmanned aerial vehicle (UAV) guidance systems, real-time path planning algorithms must compute the minimum clearance distance between a drone's programmed flight trajectory and physical obstacles detected by onboard LiDAR point clouds.
A computer engineering team programs an autonomous surveillance drone executing an inspection survey over a power substation. In a local coordinate system (scaled in decameters, where ), the drone follows a pre-computed quadratic ascent path modeled by:
The drone's forward-looking solid-state LiDAR sensor detects an unmapped high-voltage transmission pylon located at coordinate point . The drone's collision-avoidance firmware requires calculating the closest point of approach (CPA) and the absolute minimum clearance distance between the flight path and the pylon to determine if a trajectory replanning interrupt must be triggered (threshold clearance: ).
y ^
| P(0, 4) [Pylon]
| o
| / \
| d / \
| / \
| v v
| * * [Closest Points]
| / \
| / y = 0.5x^2 \
+-------------------> x
- Formulate the Euclidean distance function between the pylon at and an arbitrary point on the flight trajectory.
- Apply the Squared-Distance Optimization Technique to define the simplified single-variable objective function .
- Differentiate to locate all critical numbers of the distance function along the flight corridor .
- Classify each critical point using the Second Derivative Test, identifying whether each represents a local maximum or local minimum clearance.
- Compute the coordinates of the closest approach points and the absolute minimum clearance distance (in decameters and meters). Does the drone satisfy the safety margin?