Leonard Loh

Singapore University of Technology and Design

Papers

Total Citations

H-Index

About

Leonard Loh is a leading researcher in multi-robot systems and autonomous exploration, with a particular focus on enabling efficient coordination in unknown and challenging environments. His most impactful contribution is the development of a temporal memory-based Rapidly-Exploring Random Tree (RRT) exploration strategy for multi-AGV (Automated Guided Vehicle) systems, detailed in his highly cited 2022 paper (41 citations). This work addresses a critical bottleneck in robotics: how multiple agents can collaboratively and intelligently explore unmapped spaces without prior knowledge. By integrating temporal memory into the frontier-based RRT framework, Loh's approach significantly improves exploration efficiency and reduces redundant path planning, allowing robot teams to adapt dynamically to new obstacles and environmental changes. His research bridges the gap between theoretical path planning and practical multi-robot deployment, offering scalable solutions for applications in search-and-rescue, industrial automation, and planetary exploration. Loh's work is widely recognized for its practical impact, with his key paper serving as a foundational reference for subsequent studies in cooperative exploration. He continues to push the boundaries of autonomous navigation, making him a notable figure in the field of multi-agent robotics.