Goel Lal
Papers
1
Total Citations
5
H-Index
1
About
Goel Lal is a rising researcher at the intersection of robotics and artificial intelligence, with a primary focus on intelligent motion planning and autonomous navigation. His most cited work, "Obstacle Avoidance of a Point-Mass Robot using Feedforward Neural Network" (2021), demonstrates a core contribution: applying feedforward neural networks to enable real-time obstacle avoidance for point-mass robots. This research addresses a fundamental challenge in robotics—navigating dynamic, hazardous environments—by leveraging machine learning to improve robot autonomy and safety. With 5 citations, this paper has already sparked interest in the robotics community, particularly for applications in manufacturing, transportation, and hazardous area exploration, such as landmine detection. Lal’s work is notable for bridging classical control theory with modern neural network approaches, offering a computationally efficient solution for robots operating in unstructured spaces. As an early-career scholar, his contributions highlight the growing role of AI in making robots more adaptive and reliable, positioning him as a promising voice in the ongoing evolution of intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1