Adds the Binary Search Adherer (formally referred to as the Exponential Adherer), which rotates by an initial angle, then rotating by exponentially smaller angles to converge upon the boundary until a desired number of samples is reached.
Maximum error from boundary: $d \cdot \sin \left ( \frac{\theta_0}{2^{N-1}} \right)$, where $d$ is the jump distance, $\theta_0$ is the initial angle, and $N$ is the number of samples
Adds the Binary Search Adherer (formally referred to as the Exponential Adherer), which rotates by an initial angle, then rotating by exponentially smaller angles to converge upon the boundary until a desired number of samples is reached.
Maximum error from boundary: $d \cdot \sin \left ( \frac{\theta_0}{2^{N-1}} \right)$, where $d$ is the jump distance, $\theta_0$ is the initial angle, and $N$ is the number of samples