Daniel Garces
Papers
1
Total Citations
1
H-Index
1
About
Daniel Garces is a rising researcher in autonomous robotics and multi-agent systems, with a focus on efficient task allocation and routing for robotic fleets. His work addresses critical challenges in logistics and automation, particularly in environments where robots must dynamically manage pickup-and-delivery tasks. Garces’s key contribution is the development of "Pro-routing," a proactive routing framework for autonomous multi-capacity robots, which optimizes task scheduling and path planning to minimize delays and maximize throughput. This approach, detailed in his 2025 paper, has already garnered initial citations, signaling its potential impact on real-world applications like warehouse automation and last-mile delivery. By integrating predictive algorithms with real-time decision-making, Garces advances the field of multi-robot coordination, offering scalable solutions for complex, time-sensitive operations. His work bridges theory and practice, making him a notable voice in the next generation of robotics researchers. As his citation count grows, Garces’s contributions are poised to influence both academic research and industrial implementations in autonomous systems.
Research Focus
Key Achievements
Top Papers
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