Decision-Making and Control under Uncertainties for Robotic and Autonomous Systems

Dear Colleagues,

The integration of robotic and autonomous systems across various industries, from transportation and manufacturing to healthcare and agriculture, has marked significant technological advancement. These systems are designed to undertake complex tasks autonomously by making decisions and executing actions in real time. However, the ever-present uncertainties inherent in dynamics, environmental conditions, and interactions with other agents pose significant challenges to the seamless functioning of these systems. Reliable and effective decision-making and control mechanisms in the face of uncertainties are crucial for the safety and efficiency of these systems. In light of these imperatives, this Special Issue endeavors to delve into the forefront of advancements in decision-making and control techniques tailored for robotic and autonomous systems operating within uncertain contexts.

This Special Issue cordially invites original research articles, comprehensive reviews, and illuminating case studies that revolve around the pivotal theme of decision-making and control for robotic and autonomous systems operating under uncertainties. The topics encompassed will encompass a wide spectrum, including but not limited to:

1. Robust, adaptive, and data-driven control;
2. Risk-aware decision-making under perceptual and/or dynamics uncertainties;
3. Safe learning in robotics;
4. Deep learning approaches for decision-making in uncertain environments;
5. Sensor fusion and perception algorithms for reliable decision-making;
6. Fault detection and diagnosis techniques;
7. Human-robot interactions and collaboration in uncertain environments;
8. Decision-making under resource constraints and limited information;
9. Decision-making and control in real-world robotic and autonomous systems.

Dr. Pan Zhao
Dr. Minghui Zheng
Dr. Yu Zhang
Dr. Naira Hovakimyan
Guest Editors

Manuscript Submission Information

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