Advances in Intelligent Motion Planning, Control, and Perception for Robotic Systems

Dear Colleagues,

We are excited to present the scope and purpose of our proposed Special Issue, along with its relationship with the existing literature.

This Special Issue will focus on the synergistic integration of intelligent motion planning, adaptive control, and advanced perception in robotic systems. An emphasis will be placed on learning-based, reactive, and human-interactive approaches that enable robots to operate reliably in unstructured, dynamic, or uncertain environments.

Topics of interest include, but are not limited to, the following:

• Learning-based motion planning (e.g., reinforcement learning, imitation learning);
• Model predictive control and robust adaptive control for manipulators, mobile robots, and autonomous vehicles;
• Vision-based perception, multi-sensor fusion, and scene understanding for real-time decision making;
• Embodied AI and end-to-end planning–control–perception pipelines;
• Human-aware motion planning and shared autonomy;
• Verification and validation of intelligent robotic systems;
• Applications in autonomous driving, surgical robotics, industrial automation, and field robotics.

Our aim is to assemble high-quality original research and review articles that push the boundaries of how perception, planning, and control can be tightly coupled through intelligent algorithms. We aim to bridge the gap between theoretical advances and practical deployment, highlighting robustness, safety, and adaptability in real-world scenarios.

While the existing literature has separately addressed motion planning, control theory, and perception, there remains a lack of unified frameworks that leverage their deep interdependence. Traditional planning often assumes perfect state estimation, and conventional control may rely on simplified trajectories. Recent works in embodied AI and neural control have begun to merge these domains, but systematic treatments are still scarce.

This collection will usefully supplement the literature by

• Showcasing integrated architectures rather than isolated components;
• Encouraging comparisons between learning-based and model-based methods under identical task constraints;
• Highlighting emerging topics such as real-time perception-driven replanning and safety-certified learning control;
• Providing a platform for benchmarking and reproducible results in challenging, real-world settings (e.g., manipulation in clutter, navigation in dynamic crowds).

By focusing on advances—not merely incremental improvements—this Special Issue will serve as a state-of-the-art reference for researchers and practitioners seeking to build next-generation autonomous robotic systems.

I look forward to receiving your contributions.

Dr. Jianyong Long
Dr. Xingwen Zhou
Dr. Songyun Deng
Guest Editors

Manuscript Submission Information

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• intelligent motion planning
• adaptive control
• robotic perception
• learning-based planning
• model predictive control
• human–robot interaction
• embodied AI
• multi-sensor fusion
• real-time decision making
• autonomous robotic systems