Deep Learning-Based Autonomous Navigation In Unknown Environments
Dorin Mihail DINULESCU
Politehnic University of Bucharest
E-mail: dorinmihail.d@gmail.com
Abstract:
Within the scope of this article, our objective is to examine how deep learning algorithms can fundamentally transform a robot’s capacity to navigate autonomously within unfamiliar surroundings. Conventionally, robot operations rely on predetermined maps and planning algorithms, which encounter limitations when confronted with unforeseen changes in the internal environment. Through the implementation of deep learning, exemplified by the utilization of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), robots gain the ability to process data received from sensors and also construct internal representations of their surroundings. Consequently, this enables real-time navigation, obstacle avoidance, and independent identification of the optimal route. The advantages span diverse domains, such as mobile robotics and space exploration, and the solutions offered exhibit notable adaptability and flexibility in addressing potential challenges inherent to uncharted environments.
Keywords: autonomous navigation, recurrent neural networks, convolutional neural networks, unknown environments, deep learning.
1. INTRODUCTION
Regarding the development of intelligent robots, a particularly crucial objective is their autonomous navigation in unfamiliar environments. The ability of robots to move through unknown surroundings without human intervention holds immense and profoundly significant implications for space exploration, mobile robotics, disaster assistance, and the list goes on. As previously indicated, robots typically relied on predefined map creation and planning algorithms. However, these methods restricted the robots’ adaptability to changes in their environment. The fundamental paradigm shift in autonomous navigation occurred with the advent of deep learning, whose algorithms, notably including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), empower robots to learn from experience and construct internal representations of the environment they inhabit.All of these capabilities enable the robot to make real-time decisions using sensor data, effectively avoiding obstacles in its path and determining a suitable route.
The focal point of this article is the analysis of how deep learning facilitates the advancement of robots’ autonomous navigation in unfamiliar, unknown environments, and the exploration of corresponding technological progress. Additionally, we will explore how Convolutional Neural Networks and Recurrent Neural Networks can be harnessed for planning and visual perception, examining the multifaceted benefits of this approach. The potential of this technology represents a captivating field of interest due to its ability to develop autonomous robots capable of adapting and efficiently interacting with the surrounding world. These innovative approaches contribute to significant strides in the robotics domain, enabling the exploration of uncharted environments and enhancing robots capacity to engage with the world around them.
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