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Detail
Journal Article
A robust collision prediction and detection method based on neural network for autonomous delivery robots
- Authors
- Seonghun Seo, Hoon Jung
- Issue Date
- 2023-04
- Citation
- ETRI Journal, v.45, no.2, pp.329-337
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.
- KSP Keywords
- Acceleration time, Autonomous robot, Collision Prediction, Detection Method, LIght Detection And Ranging(LIDAR), Neural network model, Relative Distance, Robot Motion, Softmax Function, Time series data, complex environment
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
