Research

I work on learning-based perception and control for autonomous systems. Both papers below are 2024 preprints under journal review — full text and code are linked.

Google Scholar CV

arXiv · cs.RO Preprint · Under review 2024

Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation

This study trains a deep reinforcement learning agent to enable a wheeled mobile robot to navigate autonomously and collision-free toward a target in complex, obstacle-rich environments. Using only raw LiDAR observations and the goal pose, a deep neural network maps perception directly to continuous velocity commands. The work compares DDPG and PPO and introduces an enhanced PPO network architecture together with a tailored reward function, validated in both obstacle and obstacle-free Gazebo environments.

Hamid Taheri, Seyed Rasoul Hosseini, Mohammad Ali Nekoui

arXiv:2405.16266 → Details →

arXiv:2403.19782 arXiv · cs.CV

arXiv · cs.CV Preprint · Under review 2024

ENet-21: An Optimized Light CNN Structure for Lane Detection

This study develops an optimized, lightweight CNN for lane detection that combines binary segmentation with Affinity Fields. Lane instances are recovered by clustering the segmentation and affinity outputs, which lets the method handle a varying number of lanes and lane-change scenarios without a fixed lane-count assumption. Using a less complex network than existing approaches, the method is demonstrated on the TuSimple dataset.

Seyed Rasoul Hosseini, Hamid Taheri, Mohammad Teshnehlab

arXiv:2403.19782 → Details →