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About

Almira Budiyanto is a leading researcher in multi-agent robotics and autonomous systems, with a focus on cooperative transportation and UAV path planning. Her work bridges reinforcement learning, formation control, and dynamic obstacle avoidance, addressing critical challenges in real-world multi-robot coordination. Her most cited paper, "UAV obstacle avoidance using potential field under dynamic environment" (2015, 77 citations), introduced an elegant application of potential fields for collision-free navigation among multiple UAVs, establishing a foundational method for decentralized path planning. Building on this, she pioneered deep reinforcement learning approaches for cooperative transport, notably through the Deep Dyna-Q framework (2023, 10 citations), which enables rapid learning and improved formation achievement in multi-robot systems. Her recent work on SDPA-MAPPO (2024) further accelerates transfer learning for formation changes, demonstrating cutting-edge advances in scalable, attention-based multi-agent policy optimization. Budiyanto’s research directly impacts disaster mitigation, industrial automation, and autonomous logistics, where efficient, adaptive multi-robot coordination is essential. Her contributions are widely recognized for combining theoretical rigor with practical deployment, making her a key figure in the evolution of intelligent, cooperative autonomous systems.