Reinforcement Learning-Based Path Planning Algorithm for Mobile Robots

Liu, ZiXuan; Wang, Qingchuan; Yang, Bingsong

doi:10.1155/2022/1859020

Cited by 8 publications

(5 citation statements)

References 29 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simplified route to the initial task space is then resolved using a path resolution technique, which finally yields the exact inspection path. An improved wolf pack algorithm (IWPA) was used to design a guidance path (Liu, Wang, et al, 2022). Initially, position‐order coding is used to study route optimization in the discrete domain.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

Tang,

Xue,

et al. 2024

Journal of Field Robotics

View full text Add to dashboard Cite

In contemporary power transmission systems, substation monitoring stands as a vital but challenging task. While robotics offers promise in this regard, its potential is still nascent, struggling to replicate human intelligence. This article's core aim was to optimize robot path planning (RPP). Employing the enhanced red deer algorithm (ERDA), we sought to bolster RPP for more efficient substation inspections. The key methods used seem to be modeling, experimentation, comparative analysis, and some elements of data benchmarking to systematically evaluate and validate their proposed technique and models both in simulation and the real world. Research aims to enhance substation inspection effectiveness and bolster the safety of power usage in society. Proposed hybrid approach, combining proportional–integral–derivative (PID) with ERDA (PID–ERDA), underpins an Intelligent Intelligent RPP framework tailored to substation inspections. Examining the PID–ERDA model's performance, it significantly improved path length by 18%–29% and reduced response times by 14%–26% compared with PID or ERDA alone. PID–ERDA consistently achieved optimal solutions in 40–60 trials out of 85, while PID and ERDA managed 20–40 trials with inconsistent optimization. Additionally, it reduced average response times to 17–20 s from 21 to 27 s observed when using PID and ERDA separately. PID–ERDA also demonstrated superior path accuracy, surpassing methods like improved adaptive control algorithm‐feedforward neural network, enhanced unified algorithm‐susceptible‐infected‐removed, and bounded behavior‐particle swarm optimization by 7%–13%. The study affirms that the PID–ERDA model significantly enhances path planning for substation inspections, representing a milestone in RPP for power station inspections within modern power transmission systems. The primary contribution of this research is the significant improvement it brings to RPP for power station inspections, especially in substation monitoring within modern power transmission systems.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

Tang,

Xue,

et al. 2024

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…This algorithm discretizes obstacle information and target direction information into finite states, designs a continuous reward function, and improves training performance. The algorithm was tested in a simulation environment and on a real robot [10]. Another study focused on using deep reinforcement learning to train real robot primitive skills such as go-to-ball and shoot the goalie [11].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Machine Learning Approaches for Mobile Robot Control

Rybczak,

Popowniak,

Lazarowska

2024

Robotics

View full text Add to dashboard Cite

Machine learning (ML) is a branch of artificial intelligence that has been developing at a dynamic pace in recent years. ML is also linked with Big Data, which are huge datasets that need special tools and approaches to process them. ML algorithms make use of data to learn how to perform specific tasks or make appropriate decisions. This paper presents a comprehensive survey of recent ML approaches that have been applied to the task of mobile robot control, and they are divided into the following: supervised learning, unsupervised learning, and reinforcement learning. The distinction of ML methods applied to wheeled mobile robots and to walking robots is also presented in the paper. The strengths and weaknesses of the compared methods are formulated, and future prospects are proposed. The results of the carried out literature review enable one to state the ML methods that have been applied to different tasks, such as the following: position estimation, environment mapping, SLAM, terrain classification, obstacle avoidance, path following, learning to walk, and multirobot coordination. The survey allowed us to associate the most commonly used ML algorithms with mobile robotic tasks. There still exist many open questions and challenges such as the following: complex ML algorithms and limited computational resources on board a mobile robot; decision making and motion control in real time; the adaptability of the algorithms to changing environments; the acquisition of large volumes of valuable data; and the assurance of safety and reliability of a robot’s operation. The development of ML algorithms for nature-inspired walking robots also seems to be a challenging research issue as there exists a very limited amount of such solutions in the recent literature.

show abstract

“…We note that, for such sensor-based path planning and/or target detection problems, machine learning-based methods have been on the agenda recently. In particular, reinforcement learning (RL) approach appears to be gaining popularity as an adaptive optimization methodology [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection

Alpdemir,

Sezgin

2024

Neural Comput & Applic

View full text Add to dashboard Cite

Ground penetrating radar (GPR) systems are effective sensors for discovering various types of objects buried underground, such as military mines, metal objects, and pieces of underground infrastructures. A GPR system can be manually operated by a human or can be an integral part of a host platform. The host platform may be semi- or fully autonomous and may operate in different environments such as land vehicles or more recently air-borne drones. One challenge for the fully or semi-autonomous host platforms in particular is to find an efficient search procedure that would reduce the operation time and optimize resource utilization. Most of the current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper, we introduce a method that combines a coarse and therefore relatively low cost initial search pattern with a Reinforcement Learning (RL) driven efficient navigation path for eventual target detection, by exploiting the signal processing pipeline of the onboard GPR. We illustrate the applicability of the method using a well-known, high fidelity GPR simulation environment and a novel RL framework. Our results suggest that combination of a coarse navigation scheme and an RL-based training procedure based on GPR scan returns can lead to a more efficient target discovery procedure for host platforms.

show abstract

Reinforcement Learning-Based Path Planning Algorithm for Mobile Robots

Cited by 8 publications

References 29 publications

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

A Survey of Machine Learning Approaches for Mobile Robot Control

A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection

Contact Info

Product

Resources

About