scholarly journals Intention-based Prediction for Pedestrians and Vehicles in Unstructured Environments

2018 ◽  
Author(s):  
Stefan Kerscher ◽  
Norbert Balbierer ◽  
Sebastian Kraust ◽  
Andreas Hartmannsgruber ◽  
Nikolaus Müller ◽  
...  
Keyword(s):  
Unstructured Environments

A nonparametric-learning visual servoing framework for robot manipulator in unstructured environments

2021 ◽  
Vol 437 ◽  
pp. 206-217
Author(s):  
Xungao Zhong ◽  
Xunyu Zhong ◽  
Huosheng Hu ◽  
Xiafu Peng
Keyword(s):  
Visual Servoing ◽  
Robot Manipulator ◽  
Unstructured Environments ◽  
Nonparametric Learning

Optimization-Based Terrain Analysis and Path Planning in Unstructured Environments

2019 ◽  
Author(s):  
Ueli Graf ◽  
Paulo Borges ◽  
Emili Hernandez ◽  
Roland Siegwart ◽  
Renaud Dube
Keyword(s):  
Path Planning ◽  
Terrain Analysis ◽  
Unstructured Environments

Autonomous Navigation in Unstructured Environments

2004 ◽  
Vol 37 (14) ◽  
pp. 25-29 ◽  
Author(s):  
Hugh Durrant-Whyte
Keyword(s):  
Autonomous Navigation ◽  
Unstructured Environments

An autonomous excavator system for material loading tasks

2021 ◽  
Vol 6 (55) ◽  
pp. eabc3164
Author(s):  
Liangjun Zhang ◽  
Jinxin Zhao ◽  
Pinxin Long ◽  
Liyang Wang ◽  
Lingfeng Qian ◽  
...  
Keyword(s):  
Real World ◽  
Material Handling ◽  
Texture Classification ◽  
Detection Algorithms ◽  
Unstructured Environments ◽  
Ground Collapse ◽  
Dump Trucks ◽  
Multimodal Perception ◽  
World Environment ◽  
Indoor And Outdoor

Excavators are widely used for material handling applications in unstructured environments, including mining and construction. Operating excavators in a real-world environment can be challenging due to extreme conditions—such as rock sliding, ground collapse, or excessive dust—and can result in fatalities and injuries. Here, we present an autonomous excavator system (AES) for material loading tasks. Our system can handle different environments and uses an architecture that combines perception and planning. We fuse multimodal perception sensors, including LiDAR and cameras, along with advanced image enhancement, material and texture classification, and object detection algorithms. We also present hierarchical task and motion planning algorithms that combine learning-based techniques with optimization-based methods and are tightly integrated with the perception modules and the controller modules. We have evaluated AES performance on compact and standard excavators in many complex indoor and outdoor scenarios corresponding to material loading into dump trucks, waste material handling, rock capturing, pile removal, and trenching tasks. We demonstrate that our architecture improves the efficiency and autonomously handles different scenarios. AES has been deployed for real-world operations for long periods and can operate robustly in challenging scenarios. AES achieves 24 hours per intervention, i.e., the system can continuously operate for 24 hours without any human intervention. Moreover, the amount of material handled by AES per hour is closely equivalent to an experienced human operator.

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