Affiliation:
1. Cognition & Transportation ICT Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea
2. Postal and Logistics Technology Research Center Electronics and Telecommunications Research Institute Daejeon Republic of Korea
Abstract
AbstractFor 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.
Subject
Electrical and Electronic Engineering,General Computer Science,Electronic, Optical and Magnetic Materials
Cited by
12 articles.
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