Shahnewaz Siddique
Papers
6
Total Citations
32
H-Index
3
About
Shahnewaz Siddique is a robotics researcher focused on developing intelligent, low-cost autonomous systems for real-world applications, particularly in warehouse logistics and disaster response. His work spans reinforcement learning for autonomous navigation, robotic manipulation, and rugged mobile robot design. His most cited paper, “Autonomous Warehouse Robot using Deep Q-Learning” (2021, 12 citations), introduces a Deep Reinforcement Learning approach for warehouse navigation, enabling agents to optimize space and avoid obstacles in unpredictable environments. Siddique has also made significant contributions to rescue robotics, including the design of Sigma-3 (2019, 8 citations), a 6-DOF robotic arm integrated into a rescue platform for hazardous environment assessment, and the low-cost MATRO robot (2021), aimed at rapid deployment in developing countries. His Alpha-N-V2 delivery robot (2020, 7 citations) demonstrates autonomous path planning and obstacle avoidance using vector maps. Through iterative designs like the 3-Survivor series, Siddique advances rough-terrain, teleoperated mobile robots with passive control and real-time object detection, showcasing a commitment to practical, deployable robotics that bridge the gap between advanced research and real-world humanitarian needs.
Research Focus
Key Achievements
Top Papers
- 1Autonomous Warehouse Robot using Deep Q-Learning12 citations · 2021
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