Obstacle Detection and Navigation for Autonomous Ground Vehicles
Summary
Autonomous ground vehicles rely on robust perception and decision-making systems to traverse complex environments with minimal human intervention. Core capabilities include the detection of obstacles—both positive elements such as rocks and vehicles and negative features such as trenches or potholes—and the identification of drivable pathways. Sensors such as light detection and ranging (LiDAR), radar and optical cameras generate three-dimensional representations of the terrain, which are processed through algorithms that segment terrain types, classify objects and predict safe trajectories. Sensor fusion techniques combine complementary data streams to improve reliability in adverse conditions, while machine learning and geometric modelling underpin real-time scene understanding. Path-planning modules balance competing objectives of safety, efficiency and energy consumption, adapting dynamically to changes in the environment. Applications span on-road autonomous cars, off-road agricultural and exploration vehicles, and emergency-response robots that must operate in degraded visibility. Key challenges remain in ensuring robust performance under extreme weather and vegetation cover, detecting subtle or hidden obstacles and scaling solutions to large-scale deployment with constrained computational resources.
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Obstacle Detection and Navigation for Autonomous Ground Vehicles publication trend
The graph below shows the total number of articles in obstacle detection and navigation for autonomous ground vehicles across all publications each year (not limited to Nature Index journals).
Technical terms
LiDAR: A sensing technology that measures distances by timing laser pulses reflected from surrounding objects to generate detailed three-dimensional point clouds.
Negative obstacle: A drop-off or void in the terrain, such as a ditch or pothole, that traditional planar sensors may fail to detect without specialised analysis.
Sensor fusion: The process of integrating data from multiple sensor types to form a coherent environmental model, enhancing perception accuracy and robustness.
References
- A Discussion of Key Aspects and Trends in Self Driving Vehicle Technology. Journal of Machine and Computing (2023).
- Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review. Sensors (2022).
- Sensing Technology Survey for Obstacle Detection in Vegetation. Future Transportation (2021).
- Usability of Perception Sensors to Determine the Obstacles of Unmanned Ground Vehicles Operating in Off-Road Environments. Applied Sciences (2023).
- LiDAR-Based Negative Obstacle Detection for Unmanned Ground Vehicles in Orchards. Sensors (2024).
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