1. Introduction

In recent years, robotic systems have advanced significantly, the increasing demand for robot intelligence in contemporary society requires that robots can adapt to more and more complex working environments and have the ability to navigate autonomously in unknown, dynamic, human-machine coexistence and other complex environments [1]. Therefore, it is necessary to empower mobile robots with autonomous learning and environmental adaptability, so that they can continuously optimize navigation strategies based on perceived information and cope with the challenges of unknown complex environments.

Traditional robotic control strategies, while effective in controlled environments, are inadequate for managing high-dimensional state spaces and dynamic, unpredictable settings. Traditional control methods face several limitations. This paper explores how Deep Reinforcement Learning (DRL) overcomes these challenges by providing robots with enhanced learning and decision-making abilities. Deep Reinforcement Learning combines deep neural networks with reinforcement learning principles. By introducing the memory reasoning module, the robot can establish a more long-term decision basis, thus greatly improving the autonomy of mobile robots and the ability to solve the local optimal. Therefore, the research on autonomous navigation technology of mobile robot based on deep reinforcement learning in complex environment has important theoretical significance and practical application value [2]. Its ability to process vast state-action spaces makes it ideal for complex robotic tasks that require real-time adaptability. The authors categorize DRL algorithms into value-based and policy gradient methods, each offering distinct advantages for robotic control. In particular, the study focuses on DRL applications in areas such as autonomous navigation and object manipulation, where robots must operate in dynamic environments. The paper further elaborates on specific challenges in real-world DRL implementation, focusing on sensor reliability and resource management.

2. Robot Autonomous Navigation System Based on Deep Reinforcement Learning

Dong Hao et al. presents a comprehensive overview of the advancements in robotic motion control systems through the lens of Deep Reinforcement Learning (DRL). The authors begin by addressing the increasing complexity of robotic tasks and the need for sophisticated control strategies that can adapt to dynamic and uncertain environments. Traditional control methods often struggle in high-dimensional spaces. In contrast, DRL shows great potential to enhance robotic autonomy in these complex environments.

A key contribution of this study lies in its systematic categorization of DRL algorithms, distinguishing between value-based and policy gradient methods. The authors provide a detailed analysis of various DRL algorithms, including Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), elucidating their characteristics and applications in robotic motion control. By comparing these methods, the paper elucidates their strengths and weaknesses, offering insights into when to apply each algorithm effectively.

The authors examine various robotic motion control applications, focusing on key areas such as autonomous navigation, object manipulation, and human-robot collaboration. Through this exploration, they demonstrate how DRL techniques have improved performance in real-time decision-making, adaptability, and learning efficiency in these contexts. The paper also addresses the challenges associated with transferring DRL models from simulation to real-world applications, particularly regarding sensor noise and computational resource constraints.

The authors conclude by discussing future research directions in this domain, emphasizing the importance of developing robust algorithms that can generalize across diverse environments. The authors suggest future research should focus on integrating DRL with other machine learning techniques, such as unsupervised learning or transfer learning, to further improve robotic capabilities in multi-agent systems Overall, this paper serves as a vital resource for researchers in the field, providing a solid foundation for understanding the intersection of DRL and robotic motion control, and it highlights the promising future of intelligent robotic systems equipped with advanced learning algorithms [3].

Building upon these theoretical foundations and general applications of DRL in robotics, subsequent research has focused on specific applications and implementations. Li Chen et al. explore the application of deep reinforcement learning (DRL) for achieving autonomous robot navigation in complex dynamic environments. It proposes a novel navigation system that combines Deep Q-Learning with a policy gradient algorithm to enhance decision-making capabilities in high-dimensional state spaces. The authors validated the algorithm through various simulation environments, demonstrating its superiority in obstacle avoidance and path optimization. Furthermore, the study addresses the challenges of transferring DRL from simulation to real-world scenarios, such as environmental perception errors and computational limitations. This research lays the groundwork for future DRL-based navigation systems, particularly in scenarios like disaster response or space exploration, where robots must navigate complex, unknown environments autonomously.

Here are three academic papers from the United States and the UK focusing on deep reinforcement learning (DRL) for robot autonomous navigation systems: Here are three recent papers from the US and UK related to deep reinforcement learning (DRL) for autonomous navigation in robots, along with summaries of their key academic contributions [4].

In the realm of autonomous navigation, this paper presents a groundbreaking DRL framework that promises to revolutionize how robots navigate in dynamic environments. At the heart of this research is an innovative 'survival penalty' function, seamlessly integrated into the DRL architecture. This function is introduced to address the challenge of sparse reward signals, a common issue in navigation tasks where robots rarely receive feedback until they complete the task or encounter a failure, such as a collision.

The authors employ advanced DRL techniques, particularly Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3), to facilitate continuous decision-making in environments filled with dynamic obstacles. By implementing a survival penalty, the system encourages the robot to avoid collisions, similar to how a human driver would try to stay safe on the road. This approach helps the robot learn more efficient paths over time. The framework is tested in several scenarios, including obstacle-dense areas such as intersections and parking lots. Results demonstrate that the proposed method significantly improves both convergence speed and navigation stability compared to conventional DDPG algorithms. The paper's findings offer a robust solution for real-time, mapless navigation in unpredictable, unstructured environments. This approach reduces reliance on predefined maps and enhances robots' adaptability to real-world conditions. This research advances DRL’s application in autonomous systems, particularly in settings where collision avoidance and real-time decision-making are critical [5].

Building upon traditional navigation systems with map-based approaches, recent research has shifted focus toward developing more dynamic and adaptable solutions using DRL frameworks. Degang Xu introduces a novel framework aimed at enhancing the autonomous navigation of Autonomous Mobile Robots (AMRs) in industrial settings. The authors explore how deep reinforcement learning (DRL) can be effectively applied to solve the challenge of navigation in unknown or dynamic environments without relying on pre-existing maps.

The study's key contribution is a mapless navigation system for AMRs. This system utilizes sensor data from LIDAR and cameras, enabling autonomous navigation in complex industrial settings. By leveraging DRL techniques, particularly a combination of Proximal Policy Optimization (PPO) and reward shaping, the system is trained to balance safe navigation with task efficiency. The framework encourages the robot to avoid obstacles, maintain smooth trajectories, and reach its destination in an optimal manner. One of the key innovations in this work is the reward function, which penalizes collisions and inefficient paths, while rewarding smooth and safe navigation through dynamic industrial spaces. The authors conducted extensive simulations and real-world tests to validate their framework. Results show a 30% improvement in navigation efficiency and a 50% reduction in collision rates compared to traditional methods, even in environments with rapidly changing layouts. The results indicate that the DRL-based approach outperforms traditional methods, offering enhanced flexibility, scalability, and efficiency in industrial robot navigation. This research contributes significantly to the advancement of AMR autonomy in industrial applications, particularly in environments where map-based navigation is impractical due to constant spatial changes [6].

In the bustling world of robotics, this paper tackles a pressing challenge: how to make robots navigate crowded spaces with the grace and efficiency of a human. The authors propose a deep reinforcement learning (DRL) approach, integrating crowd-aware models that allow the robot to account for both dynamic human behavior and environmental complexity while making real-time navigation decisions.

At the heart of this research lies an ingenious reward function, meticulously crafted to address the nuances of crowd navigation. The function balances safety, social compliance, and efficiency by rewarding the robot for maintaining safe distances from people, following human social norms (such as not cutting too closely in front of others), and minimizing navigation time to reach its destination. Traditional methods often rely on pre-programmed rules or static obstacle avoidance. In contrast, this DRL-based system empowers the robot to learn adaptive navigation strategies from its real-world experiences in dynamic environments.

The authors validate their framework through simulation environments that emulate real-world crowded spaces. The robot demonstrates the ability to handle complex pedestrian interactions, such as avoiding collisions in dense crowds and adapting to unpredictable human movements. The results are compelling: the DRL-based approach demonstrated a 40% improvement in navigation efficiency and a 30% reduction in near-miss incidents compared to conventional algorithms, particularly in crowded, real-world scenarios. This research makes a significant contribution to the field of autonomous robot navigation by enhancing a robot’s ability to interact with and navigate through human-dense environments, paving the way for future developments in service robotics, particularly in areas like public spaces, healthcare, and urban delivery systems. These studies showcase the power of DRL in enabling autonomous robot navigation in complex, dynamic environments. They address key challenges including generalization, safety, and real-time decision-making [7].

Wei Liu addresses the challenge of autonomous navigation for robots in complex dynamic environments by proposing a novel navigation framework based on Deep Reinforcement Learning (DRL). It begins by analyzing the limitations of traditional path planning algorithms in high-dimensional, unknown environments, highlighting their poor performance when dealing with complex obstacles and dynamic targets. The authors propose a novel navigation system to address this challenge. By integrating Deep Q-Learning (DQN) with policy gradient algorithms, they aim to boost the robot's decision-making abilities in complex scenarios.

A key innovation of this study is the introduction of a multi-objective optimization strategy, enabling robots to rapidly learn effective path planning methods in dynamic settings while balancing obstacle avoidance and target tracking. The authors validate the efficacy of their approach through a series of simulations, demonstrating that the system can achieve rapid and stable path planning in high-density obstacle environments and significantly improve navigation accuracy and responsiveness in various dynamic scenarios. The paper also addresses the challenges of moving from simulations to the real world, such as dealing with noisy sensor data and limited computing power. The authors suggest solutions like using more robust sensors and optimizing the AI algorithms to run on less powerful computers. This research enriches the theoretical application of DRL in robot autonomous navigation and provides essential references for practical industrial applications. It shows that DRL-based navigation methods exhibit strong adaptability and robustness when addressing complex, unknown environments, laying a solid foundation for the future development of intelligent robotic systems [8].

3. Conclusion

In conclusion, this paper highlights the transformative impact of Deep Reinforcement Learning (DRL) on robotic motion control systems, demonstrating its superiority over traditional methods in dynamic and complex environments. By categorizing DRL algorithms and comparing their applications, the authors provide valuable insights into the strengths and limitations of approaches like DQN and PPO. The paper’s exploration of real-world applications, such as autonomous navigation and human-robot interaction, showcases DRL’s potential to enhance robots’ decision-making, learning efficiency, and adaptability. However, transferring DRL models from simulations to real-world applications remains challenging due to factors like sensor noise and limited computational resources. Looking ahead, the authors suggest further research into integrating DRL with other machine learning techniques. This integration, particularly with unsupervised and transfer learning, could lead to more robust algorithms for multi-agent systems. This work serves as a critical resource for researchers and paves the way for future innovations in autonomous robotics.