arXiv subjects

Shantanu Rahman

Publications and source records attributed to Shantanu Rahman.

3 recordsLinked to original sources

arXiv2026

MonoSIM: An open source SIL framework for Ackermann Vehicular Systems with Monocular Vision

This paper presents an open-source Software-in-the-Loop (SIL) simulation platform designed for autonomous Ackerman vehicle research and education. The proposed framework focuses on simplicity, while making it easy to work with small-scale experimental setups, such as the XTENTH-CAR platform. The system was designed using open source tools, creating an environment with a monocular camera vision system to capture stimuli from it with minimal computational overhead through a sliding window based lane detection method. The platform supports a flexible algorithm testing and validation environment, allowing researchers to implement and compare various control strategies within an easy-to-use virtual environment. To validate the working of the platform, Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) algorithms were implemented within the SIL framework. The results confirm that the platform provides a reliable environment for algorithm verification, making it an ideal tool for future multi-agent system research, educational purposes, and low-cost AGV development. Our code is available at https://github.com/shantanu404/monosim.git.

cs.RO↗

arXiv2025

Development of a Testbed for Autonomous Vehicles: Integrating MPC Control with Monocular Camera Lane Detection

Autonomous vehicles are becoming popular day by day not only for autonomous road traversal but also for industrial automation, farming and military. Most of the standard vehicles follow the Ackermann style steering mechanism. This has become to de facto standard for large and long faring vehicles. The local planner of an autonomous vehicle controls the low-level vehicle movement upon which the vehicle will perform its motor actuation. In our work, we focus on autonomous vehicles in road and perform experiments to analyze the effect of low-level controllers in the simulation and a real environment. To increase the precision and stability of trajectory tracking in autonomous cars, a novel method that combines lane identification with Model Predictive Control (MPC) is presented. The research focuses on camera-equipped autonomous vehicles and uses methods like edge recognition, sliding window-based straight-line identification for lane line extraction, and dynamic region of interest (ROI) extraction. Next, to follow the identified lane line, an MPC built on a bicycle vehicle dynamics model is created. A single-lane road simulation model is built using ROS Gazebo and tested in order to verify the controller's performance. The root mean square error between the optimal tracking trajectory and the target trajectory was reduced by 27.65% in the simulation results, demonstrating the high robustness and flexibility of the developed controller.

cs.RO↗

arXiv2025

Flow-Based Path Planning for Multiple Homogenous UAVs for Outdoor Formation-Flying

Collision-free path planning is the most crucial component in multi-UAV formation-flying (MFF). We use unlabeled homogenous quadcopters (UAVs) to demonstrate the use of a flow network to create complete (inter-UAV) collision-free paths. This procedure has three main parts: 1) Creating a flow network graph from physical GPS coordinates, 2) Finding a path of minimum cost (least distance) using any graph-based path-finding algorithm, and 3) Implementing the Ford-Fulkerson Method to find the paths with the maximum flow (no collision). Simulations of up to 64 UAVs were conducted for various formations, followed by a practical experiment with 3 quadcopters for testing physical plausibility and feasibility. The results of these tests show the efficacy of this method's ability to produce safe, collision-free paths.

cs.RO↗