"Vision-Based Autonomous Robot Self-Docking and Recharging"

"This simulator models a vision-based autonomous robot self-docking and recharging system based on the ER-1 Evolution Robotics platform, reproducing the navigation control loop, object recognition dynamics, and mechanical\/electrical docking outcomes described in the paper. The simulator implements a finite-state-machine controller with four navigation tasks (Drive-Toward-Object, Turn-To-Object, Obstacle-Avoidance, and Proceed-And-Scan) operating in two room configurations with extracted experimental parameters including docking times of 57.4\u00b12.4s (Room I west), 51.6\u00b13.5s (Room I north), and 85.23\u00b120.84s (Room II). It reproduces 50-trial Monte Carlo experiments across five scenarios spanning obstacle probability, path length, and battery level, generating robot trajectories, feature-matching curves, battery dynamics, and success-rate statistics that mirror the paper's reported 98-100% mechanical and 96-100% electrical docking success rates. Researchers can use this simulator to tune navigation parameters, test new obstacle-avoidance heuristics, and benchmark docking reliability under varying environmental disturbances without physical hardware."