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About

Kevin Marlon Soza Mamani is a researcher advancing the frontiers of autonomous multi-robot systems and swarm intelligence. His work primarily focuses on decentralized control, self-organized flocking, and real-time path planning for mobile robots. A key contribution is his "Flocking Model for Self-Organized Swarms" (2019, 6 citations), which explores the fundamental algorithms of aggregation and coordinated movement that enable large groups of individuals to move as a cohesive unit. He further developed the "MIMC-VADOC Model" (2022, 4 citations), a decentralized formation control framework specifically designed for differential-drive robots, bridging the gap between control theory and practical implementation. Addressing real-world challenges, his recent work on "Double Deep Q-Learning Network-Based Path Planning" (2024, 3 citations) tackles adaptive obstacle avoidance in complex mining environments. Complementing this, his "Low-Computational-Load Real-time Path Planning" (2023) demonstrates efficient trajectory control using Artificial Potential Fields, prioritizing practical, low-resource solutions for real-time maneuverability. Soza Mamani’s research is notable for its direct application to industrial and hazardous settings, offering computationally efficient, scalable solutions for autonomous navigation.