A Furcated Visual Collision Avoidance System for an Autonomous Microrobot

Abstract

This paper proposes a secondary reactive collision avoidance system for microclass of robots based on a novel approach known as the furcated luminance-difference processing (FLDP) inspired by the lobula giant movement detector, a wide-field visual neuron located in the lobula layer of a locust nervous system. This paper addresses some of the major collision avoidance challenges: obstacle proximity and direction estimation, and operation in GPS-denied environment with irregular lighting. Additionally, it has proven effective in detecting edges independent of background color, size, and contour. The FLDP executes a series of image enhancement and edge detection algorithms to estimate collision threat-level which further determines whether the robot’s field of view must be dissected where each section’s response is compared against the others to generate a simple collision-free maneuver. Ultimately, the computation load and the performance of the model are assessed against an eclectic set of offline as well as real-time real-world collision scenarios validating the proposed model’s asserted capability to avoid obstacles at more than 670 mm prior to collision, moving at 1.2 ms−1 with a successful avoidance rate of 90% processing at 120 Hz on a simple single-core microcontroller, sufficient to conclude the system’s feasibility for real-time real-world applications that possess fail-safe collision avoidance system.