Bilel Allani

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About

Bilel Allani is a researcher focused on advancing autonomous navigation and energy efficiency in mobile robotics, with particular expertise in path planning for self-guided vehicles and industrial automation. His work addresses critical challenges in robot navigation, from large-scale indoor mapping to energy-conscious local path planning. Allani’s most cited paper, “An efficient indoor large map global path planning for robot navigation” (2024, 18 citations), introduces novel approaches for navigating expansive indoor environments, a key challenge for autonomous systems in warehouses and factories. His 2022 study on “Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position” (14 citations) makes a significant contribution by demonstrating how load positioning directly impacts energy consumption during navigation—a practical insight for extending battery life in industrial robots that must operate for hours on a single charge. Additionally, his work on “Machine Learning Approach for Charging Queue Waiting Time Prediction of Electrical Autonomous Forklifts Fleet” (2022, 3 citations) addresses the operational bottleneck of battery management in autonomous forklift fleets. Through these contributions, Allani is helping to create more efficient, longer-lasting autonomous systems for industrial applications.