Learning from Virtual Experience: Mapless Navigation with Neuro-Fuzzy Intelligence

Khaksar, Weria; Uddin, Md. Zia; Tørresen, Jim

doi:10.1109/is.2018.8710525

Cited by 5 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, the purpose of the navigation control method based on imitation learning is to learn the best mapping between the output motion action of the navigation controller and the input LiDAR sensing data and the relative target position. For example, Khaksar et al [15] proposed a mapless neuro-fuzzy motion planner learned from a virtual experience model, which generates enough training data to train the adaptive neuro-fuzzy inference system. Pfeiffer et al [16] proposed a data-driven motion planning approach based on a feature-based maximum entropy model, which is trained to predict the joint navigation behavior of heterogeneous groups of agents based on the demonstration of human-human interactions.…”

Section: Introductionmentioning

confidence: 99%

Mapless LiDAR Navigation Control of Wheeled Mobile Robots Based on Deep Imitation Learning

Tsai

Nisar

2021

IEEE Access

View full text Add to dashboard Cite

This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method for wheeled mobile robots based on deep imitation learning. The proposed method is a data-driven control method that directly uses LiDAR sensors and relative target position for mobile robot navigation control. A deep convolutional neural network (CNN) model is proposed to predict motion control commands of the mobile robot without the requirement of the global map to achieve navigation control of the mobile robot in unknown environments. While collecting the training dataset, we manipulated the mobile robot to avoid obstacles through manual control and recorded the raw data of the LiDAR sensor, the relative target position, and the corresponding motion control commands. Next, we applied a data augmentation method on the recorded samples to increase the number of training samples in the dataset. In the network model design, the proposed CNN model consists of a LiDAR CNN module to extract LiDAR features and a motion prediction module to predict the motion behavior of the robot. In the model training phase, the proposed CNN model learns the mapping between the input sensor data and the desired motion behavior through end-to-end imitation learning. Experimental results show that the proposed mapless LiDAR navigation control method can safely navigate the mobile robot in four unseen environments with an average success rate of 75%. Therefore, the proposed mapless LiDAR navigation control system is effective for robot navigation control in an unknown environment without the global map.INDEX TERMS Deep imitation learning, end-to-end learning, mapless LiDAR navigation control, behavior cloning.

show abstract

Section: Introductionmentioning

confidence: 99%