Review of Vision-Based Environmental Perception for Lower-Limb Exoskeleton Robots

Area of Science:

• Robotics
• Artificial Intelligence
• Biomechanics

Background:

• Exoskeleton robots are wearable electromechanical devices enhancing human physical capabilities and aiding rehabilitation.
• Advancements in visual sensors and deep learning have increased focus on exoskeleton environmental perception.
• Environmental perception enables autonomous decision-making, improving stability, safety, and human-machine-environment interaction.

Purpose of the Study:

• To review environmental perception technologies for lower-limb exoskeleton robots.
• To analyze key technologies including datasets, critical terrain detection, and adaptive gait planning.
• To identify limitations and propose future research directions in exoskeleton environmental perception.

Main Methods:

• Review of visual sensors and control systems in exoskeletons.
• Analysis of environmental perception technologies and datasets.
• Examination of terrain detection and adaptive gait planning strategies.

Main Results:

• Environmental perception enhances exoskeleton autonomy, safety, and interaction.
• Key technologies include visual sensing, deep learning for terrain analysis, and adaptive gait planning.
• Current limitations in perception hinder widespread exoskeleton adoption.

Conclusions:

• Environmental perception is crucial for advanced lower-limb exoskeleton functionality.
• Further research is needed to overcome current limitations and improve perception capabilities.
• Future directions involve enhancing autonomous navigation and human-robot collaboration.