About this Research Topic
The rapid advancement of robotics technology has transformed the way we live and interact with our environment. At the heart of this revolution lies the ability of robots to perceive their surroundings and plan their actions accordingly. Advanced perception and planning technologies have become the cornerstone of modern robotics, enabling robots to navigate complex environments, interact with humans, and perform tasks with unprecedented precision and autonomy.
From self-driving cars to search and rescue robots, the ability to perceive and respond to the world around them is crucial for robots to operate efficiently and effectively. Recent breakthroughs in computer vision, machine learning and artificial intelligence have significantly enhanced the perception capabilities of robots, allowing them to detect and respond to their environment in real-time. Moreover, advanced planning algorithms have enabled robots to reason about their actions, anticipate outcomes and adapt to changing situations.
The primary goal of this Research Topic is to showcase the latest advancements and innovations in advanced perception and planning technology in robotics, highlighting the significant progress achieved in the field over the past few years. More specifically, this Research Topic seeks to investigate the latest developments in perception technologies, including computer vision, sensor fusion, and machine learning, and examine the role of planning technologies, including motion planning, task planning, and decision-making, in robotics.
Furthermore, this Research Topic aims to bring together researchers, practitioners and industry experts to share their insights and expertise on the cutting-edge technologies, applications and challenges that are shaping the future of robotics. Through this Research Topic, we aspire to inspire and promote interdisciplinary collaboration and innovation, catalyzing the development of new research directions, applications, and solutions in the domain of advanced perception and planning technology in robotics.
This Research Topic will cover a range of topics within Advanced Perception and Planning Technology in Robotics, including but not limited to:
● Multi-Modal Sensor Perception: Developing novel approaches to integrate data from various sensors to enhance robotic perception e.g., pose estimation
● Deep Learning Algorithms for Robot Perception: Investigating the application of deep learning technology in robot perception
● Active Perception: Designing robots that can actively control their sensors to gather information about their environment
● Geometric vision: Solving robot perception tasks by computer geometry vision.
● Motion Planning for Complex Tasks: Developing planning algorithms for robots to perform complex tasks, such as combined task with generative design of EoA tools
● Task Planning under Uncertainty: Investigating planning approaches that can handle uncertainty and incomplete information
● Human-Robot Collaboration: Developing planning algorithms that enable robots to collaborate with humans
● Multi Agent systems: Collaboration of multiple robots and multi agent action control to solve complex tasks
From self-driving cars to search and rescue robots, the ability to perceive and respond to the world around them is crucial for robots to operate efficiently and effectively. Recent breakthroughs in computer vision, machine learning and artificial intelligence have significantly enhanced the perception capabilities of robots, allowing them to detect and respond to their environment in real-time. Moreover, advanced planning algorithms have enabled robots to reason about their actions, anticipate outcomes and adapt to changing situations.
The primary goal of this Research Topic is to showcase the latest advancements and innovations in advanced perception and planning technology in robotics, highlighting the significant progress achieved in the field over the past few years. More specifically, this Research Topic seeks to investigate the latest developments in perception technologies, including computer vision, sensor fusion, and machine learning, and examine the role of planning technologies, including motion planning, task planning, and decision-making, in robotics.
Furthermore, this Research Topic aims to bring together researchers, practitioners and industry experts to share their insights and expertise on the cutting-edge technologies, applications and challenges that are shaping the future of robotics. Through this Research Topic, we aspire to inspire and promote interdisciplinary collaboration and innovation, catalyzing the development of new research directions, applications, and solutions in the domain of advanced perception and planning technology in robotics.
This Research Topic will cover a range of topics within Advanced Perception and Planning Technology in Robotics, including but not limited to:
● Multi-Modal Sensor Perception: Developing novel approaches to integrate data from various sensors to enhance robotic perception e.g., pose estimation
● Deep Learning Algorithms for Robot Perception: Investigating the application of deep learning technology in robot perception
● Active Perception: Designing robots that can actively control their sensors to gather information about their environment
● Geometric vision: Solving robot perception tasks by computer geometry vision.
● Motion Planning for Complex Tasks: Developing planning algorithms for robots to perform complex tasks, such as combined task with generative design of EoA tools
● Task Planning under Uncertainty: Investigating planning approaches that can handle uncertainty and incomplete information
● Human-Robot Collaboration: Developing planning algorithms that enable robots to collaborate with humans
● Multi Agent systems: Collaboration of multiple robots and multi agent action control to solve complex tasks
Keywords: Perception, Deep Learning, Planning, Interaction, Vision
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
