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About

Xiqiang Ma is a robotics researcher whose work focuses on autonomous navigation, path planning, and environmental perception for mobile robots. His most cited paper, "PRM-D* Method for Mobile Robot Path Planning" (2023, 33 citations), introduces a hybrid algorithm that combines probabilistic roadmap methods with dynamic replanning to enable fast, reliable obstacle avoidance in dynamic environments—a critical challenge for real-world robotic deployment. Ma has also made notable contributions to bio-inspired robotics, as seen in his study of bird neck structures (2021, 10 citations), where he analyzed natural motion principles to inform actuator design. His recent work on sensor fusion, such as fusing YOLO-SCG object detection with point cloud clustering (2024, 6 citations), addresses the limitations of single-sensor systems for robust environment understanding. Additionally, his 2025 paper on autonomous exploration via real-time map optimization (5 citations) tackles efficiency in unknown environments, while his D*-KDDPG algorithm (2024, 3 citations) enhances deep reinforcement learning for kinematically aware path planning. With a growing body of work spanning from theoretical algorithms to practical perception systems, Ma is advancing the capabilities of autonomous robots in complex, unstructured settings.