Research Topic

Robotic Algorithms and Technologies for Real-World Unstructured Environment Applications

About this Research Topic

Real-world environments are characterized by unstructured, unpredictable, cluttered, and varying conditions. These conditions pose a number of high-level challenges for robots to operate in. The challenges include developing suitable and effective robotic systems such as multi-legged or hybrid robotic systems, robust perception that can accurately perceive the environment under all-weather conditions, enabling the robot to move properly in different conditions, and whole-body motion control methods that permit the robot to operate successfully, or learning tools which endow the robot with skills on how to recover itself from failures when operating in such environment to name a few. Such an environment may also require a multitude of heterogeneous robots collaborating to complete the required task. Despite recent advances in these areas, there are still many unsolved challenges to realizing a successful robot that can robustly operate in such environments.

This research topic aims to discuss the recent advances and future trends in robotic algorithms and technologies r required to enable emerging robots to operate effectively in real-world unstructured environment applications. It targets to collect contributions in system development, algorithmic advancements in perception, motion and interaction control, as well as machine learning approaches used to enable robots to operate autonomously in such challenging environments. Use case applications include agriculture, construction, inspection and maintenance and disaster response.

Topics of interest include, but are not limited to the followings:
• Optimized robot design
• Legged and hybrid locomotion
• Loco-manipulation and whole-body motion control
• Reactive and adaptive physical interaction
• Robust perception system under all-weather condition
• Perception-based dynamic robot motion control
• Self-adaptation in unseen environment
• Failure recovery
• Training and testing database for machine learning
• Real-world applications in agriculture, construction, inspection and maintenance and disaster response


Keywords: failure recovery, whole body loco-manipulation, physical interaction planning and control, perception in unstructured environments, robust robotic


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.

Real-world environments are characterized by unstructured, unpredictable, cluttered, and varying conditions. These conditions pose a number of high-level challenges for robots to operate in. The challenges include developing suitable and effective robotic systems such as multi-legged or hybrid robotic systems, robust perception that can accurately perceive the environment under all-weather conditions, enabling the robot to move properly in different conditions, and whole-body motion control methods that permit the robot to operate successfully, or learning tools which endow the robot with skills on how to recover itself from failures when operating in such environment to name a few. Such an environment may also require a multitude of heterogeneous robots collaborating to complete the required task. Despite recent advances in these areas, there are still many unsolved challenges to realizing a successful robot that can robustly operate in such environments.

This research topic aims to discuss the recent advances and future trends in robotic algorithms and technologies r required to enable emerging robots to operate effectively in real-world unstructured environment applications. It targets to collect contributions in system development, algorithmic advancements in perception, motion and interaction control, as well as machine learning approaches used to enable robots to operate autonomously in such challenging environments. Use case applications include agriculture, construction, inspection and maintenance and disaster response.

Topics of interest include, but are not limited to the followings:
• Optimized robot design
• Legged and hybrid locomotion
• Loco-manipulation and whole-body motion control
• Reactive and adaptive physical interaction
• Robust perception system under all-weather condition
• Perception-based dynamic robot motion control
• Self-adaptation in unseen environment
• Failure recovery
• Training and testing database for machine learning
• Real-world applications in agriculture, construction, inspection and maintenance and disaster response


Keywords: failure recovery, whole body loco-manipulation, physical interaction planning and control, perception in unstructured environments, robust robotic


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.

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Submission Deadlines

03 December 2021 Abstract
01 February 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

03 December 2021 Abstract
01 February 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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