Kewei Cai

Dalian Minzu University, Dalian Ocean University

Papers

Total Citations

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About

Kewei Cai is a rising researcher in robotics and artificial intelligence, with a focus on deep reinforcement learning for autonomous systems. His work addresses critical challenges in robotic navigation, manipulation, and multi-agent coordination. His most cited paper, "Deep Reinforcement Learning for Robotic Arm Path Planning in Multi-Obstacle Environments" (2024, 4 citations), introduces a novel state representation technique that enables picking robotic arms to navigate complex, cluttered spaces in real time. This contribution has immediate applications in manufacturing and logistics. Cai also developed the "FFT_YOLOX Model for Underwater Precious Marine Product Detection" (2022, 3 citations), which combines frequency-domain analysis with object detection to automate marine harvesting—a breakthrough for labor-intensive aquaculture. His latest work, "Queue Formation and Obstacle Avoidance Navigation Strategy for Multi-Robot Systems Based on Deep Reinforcement Learning" (2025, 2 citations), tackles the challenge of multi-robot coordination, enabling teams of robots to form orderly queues while avoiding collisions. Though early in his career, Cai’s publications demonstrate a clear trajectory toward scalable, intelligent robotic systems. His research is particularly notable for bridging simulation-based reinforcement learning with real-world deployment constraints, making him a promising voice in the next generation of robotics engineers.