Advanced Planning, Perception, and Control for Autonomous Vehicles and Robots

Dear Colleagues,

Modern autonomous vehicles and robots need to operate safely and efficiently under uncertainties caused by perception noise, environmental disturbances, dynamic obstacles, and imperfect models. Therefore, research in navigation and control has evolved beyond classical model-based approaches toward learning-enabled, data-driven, and hybrid frameworks. Techniques such as reinforcement learning, model predictive control, multi-modal sensor fusion, and vision–language models have been actively explored to enhance autonomy, adaptability, and generalization across tasks and platforms.

Despite these advances, many fundamental challenges remain. Ensuring safety, reliability, and interpretability of autonomous navigation and control algorithms is still an open problem. Bridging the gap between simulation and real-world deployment, achieving scalable coordination among multiple agents, and handling partial observability and communication constraints continue to motivate intensive research efforts. Addressing these challenges is essential for the widespread adoption of autonomous vehicles and robots in real-world settings.

This Special Issue aims to provide the latest theoretical developments, algorithmic innovations, and experimental validations in the field of autonomous navigation and control. Contributions that advance fundamental understanding as well as those demonstrating practical impact are highly encouraged. Submitted papers should present original research and offer meaningful insights into both current challenges and future directions. Topics of interest include, but are not limited to, the following:

• Navigation of autonomous ground, aerial, marine, and space vehicles and robots;
• Motion planning and decision-making under uncertainty;
• Learning-based and hybrid navigation and control methods;
• Perception-aware control and multi-modal sensor fusion;
• Vision-based, LiDAR-based, and semantic navigation;
• Safe, robust, and explainable control for autonomous systems;
• Multi-robot and multi-vehicle coordination and cooperation;
• Sim-to-real transfer and real-world experimental validation;
• Human–robot interaction and shared autonomy in navigation tasks.

We hope this Special Issue will stimulate cross-disciplinary discussions and foster new ideas that push the boundaries of autonomy in vehicles and robots.

Dr. Woochul Nam
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• intelligent navigation
• motion planning
• decision-making
• perception-aware control
• multi-modal sensor fusion
• multi-robot cooperation
• sim-to-real transfer