Autonomous Navigation of Mobile Robot Systems
Summary
Autonomous navigation of mobile robot systems integrates sensing, perception, planning and control to enable machines to move purposefully in dynamic and unstructured environments. Advances in sensor technologies—from lidar and stereo cameras to inertial measurement units—provide rich data for the robot to construct real-time representations of its surroundings. At the algorithmic core, simultaneous localisation and mapping methods and visual odometry allow the system to build and update maps while estimating its own pose. Machine-learning approaches, including convolutional neural networks and Gaussian process models, have enhanced robustness in decision making and obstacle avoidance, particularly in complex or GPS-denied settings. Practical implementations span aerial drones performing environmental surveys, ground vehicles conducting inspection or delivery tasks, and assistive platforms such as autonomous wheelchairs. Research efforts continue to push the boundaries of computational efficiency, adaptability to novel scenarios and safe interaction with humans. As these systems mature, their global significance grows across agriculture, logistics, urban mobility and healthcare, promising to transform industries and improve quality of life.
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Autonomous Navigation of Mobile Robot Systems publication trend
The graph below shows the total number of articles in autonomous navigation of mobile robot systems across all publications each year (not limited to Nature Index journals).
Technical terms
Simultaneous localisation and mapping (SLAM): A computational process by which a robot constructs or updates a map of an unknown environment while simultaneously keeping track of its location.
Visual odometry: Estimation of a mobile robot’s motion by analysing sequential camera images to infer changes in position and orientation.
Euclidean Signed Distance Field (ESDF): A spatial representation that stores the shortest distance and direction from each point in space to the nearest obstacle, facilitating rapid trajectory planning.
Obstacle avoidance: The process by which a mobile robot detects and navigates around static or dynamic objects to ensure safe, collision-free movement.
References
- UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review. Journal of Field Robotics (2024).
- A Neural Network-Based Navigation Approach for Autonomous Mobile Robot Systems. Applied Sciences (2022).
- Exploiting a Variable-Sized Map and Vicinity-Based Memory for Dynamic Real-Time Planning of Autonomous Robots. Robotics (2025).
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