Robotic Navigation and Traversal in Complex Environments
Summary
Robotic navigation and traversal in complex environments addresses the capacity of autonomous systems to perceive, plan and execute movement through unstructured or dynamically changing settings. Key challenges include reliable localisation amid sensor noise, generation of traversability maps over uneven or deformable terrain, obstacle avoidance in cluttered or GPS-denied spaces, and real-time adaptation to unforeseen hazards. Recent advances integrate multi-modal sensing (for example LiDAR, vision and inertial measurement), probabilistic simultaneous localisation and mapping (SLAM) frameworks, machine-learning-based perception and motion-planning algorithms that account for terrain physics and kinematic constraints. Applications span urban search and rescue, planetary exploration, infrastructure inspection, precision agriculture and disaster relief. The global significance of this field lies in enhancing safety, extending human reach into hazardous zones and reducing operational costs through robotic autonomy in environments that are risky, remote or inaccessible.
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Robotic Navigation and Traversal in Complex Environments publication trend
The graph below shows the total number of articles in robotic navigation and traversal in complex environments across all publications each year (not limited to Nature Index journals).
Technical terms
Simultaneous Localisation and Mapping (SLAM): A computational method by which a mobile agent builds a map of an unknown environment while simultaneously determining its position within that map.
LiDAR: A remote-sensing technology using laser light to measure distances and generate high-resolution three-dimensional representations of surroundings.
Deep Reinforcement Learning (DRL): A machine-learning paradigm in which an agent learns optimal actions through trial-and-error interactions with an environment, guided by reward signals.
Convolutional Neural Network (CNN): A class of deep neural networks particularly effective at processing grid-structured data such as images, used for feature extraction and pattern recognition in visual inputs.
References
- Deep Reinforcement Learning for Flipper Control of Tracked Robots in Urban Rescuing Environments. Remote Sensing (2023).
- The Deep Convolutional Neural Network Role in the Autonomous Navigation of Mobile Robots (SROBO). Remote Sensing (2022).
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