Development of Sensing Algorithms for Object Tracking and Predictive Safety Evaluation of Autonomous Excavators
| Autor: | Jaho Seo, Abdullah Rasul, Amir Khajepour |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Technology LiDAR Computer science QH301-705.5 QC1-999 0211 other engineering and technologies motion prediction 02 engineering and technology 020901 industrial engineering & automation Match moving 021105 building & construction General Materials Science Biology (General) Instrumentation QD1-999 object tracking construction safety Fluid Flow and Transfer Processes Process Chemistry and Technology Physics General Engineering safety evaluation Collision Engineering (General). Civil engineering (General) Object detection Computer Science Applications Construction site safety autonomous excavation Excavator Chemistry Lidar Video tracking IMM-UK-JPDA Key (cryptography) TA1-2040 Algorithm excavator working area |
| Zdroj: | Applied Sciences, Vol 11, Iss 6366, p 6366 (2021) Applied Sciences Volume 11 Issue 14 |
| ISSN: | 2076-3417 |
| Popis: | This article presents the sensing and safety algorithms for autonomous excavators operating on construction sites. Safety is a key concern for autonomous construction to reduce collisions and machinery damage. Taking this point into consideration, our study deals with LiDAR data processing that allows for object detection, motion tracking/prediction, and track management, as well as safety evaluation in terms of potential collision risk. In the safety algorithm developed in this study, potential collision risks can be evaluated based on information from excavator working areas, predicted states of detected objects, and calculated safety indices. Experiments were performed using a modified mini hydraulic excavator with Velodyne VLP-16 LiDAR. Experimental validations prove that the developed algorithms are capable of tracking objects, predicting their future states, and assessing the degree of collision risks with respect to distance and time. Hence, the proposed algorithms can be applied to diverse autonomous machines for safety enhancement. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|