A Soar-Based Space Exploration Algorithm for Mobile Robots

Luo, Fei; Zhou, Qin; Fuentes, Joel; Ding, Weichao; Gu, Chunhua

doi:10.3390/e24030426

Cited by 10 publications

(7 citation statements)

References 30 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Search and Rescue: Mobile robots are used in search and rescue operations in disasterstricken areas to locate and rescue victims, providing a safer and more efficient approach (Py et al, 2022). • Space Exploration: Mobile robots are used in space exploration missions to explore remote planets and moons, gather data, and perform tasks that are too risky or impossible for humans (Luo et al, 2022). • Environmental Monitoring: Mobile robots are employed in environmental monitoring tasks such as air and water quality monitoring, weather forecasting, and pollution control, providing more accurate and timely data .…”

Section: Mobile Robot Application In 21st Centurymentioning

confidence: 99%

A review of methodologies for path planning and optimization of mobile robots

Sahoo¹,

Choudhury²

2023

J Process Man & New Technol

View full text Add to dashboard Cite

This research paper provides a comprehensive review of methodologies for path planning and optimization of mobile robots. With the rapid development of robotics technology, path planning and optimization have become fundamental areas of research for achieving efficient and safe autonomous robot navigation. In this paper, we review the classic and state-of-the-art techniques of path planning and optimization, including artificial potential fields, A* algorithm, Dijkstra's algorithm, genetic algorithm, swarm intelligence, and machine learning-based methods. We analyze the strengths and weaknesses of each approach and discuss their application scenarios. Moreover, we identify the challenges and open problems in this field, such as dealing with dynamic environments and real-time constraints. This paper serves as a comprehensive reference for researchers and practitioners in the robotics community, providing insights into the latest trends and developments in path planning and optimization for mobile robots.

show abstract

Section: Mobile Robot Application In 21st Centurymentioning

confidence: 99%

A review of methodologies for path planning and optimization of mobile robots

Sahoo¹,

Choudhury²

2023

J Process Man & New Technol

View full text Add to dashboard Cite

show abstract

“…RL is an important branch of machine learning. It can be represented as a closed-loop system composed of agents, environment, a state space s t

S , an action space a t

A , and a reward function r t = R ( s t , a t , s t +1 ), as shown in Figure 2 , in which the state space s t

S describes the set of information received by the agent and the action space a t

A describes the set of agents’ decision-making in state space s t

S [ 28 ].…”

Section: Basic Theories Involvedmentioning

confidence: 99%

A Fault Detection Method Based on an Oil Temperature Forecasting Model Using an Improved Deep Deterministic Policy Gradient Algorithm in the Helicopter Gearbox

Wei

Cheng

et al. 2022

Entropy

View full text Add to dashboard Cite

The main gearbox is very important for the operation safety of helicopters, and the oil temperature reflects the health degree of the gearbox; therefore establishing an accurate oil temperature forecasting model is an important step for reliable fault detection. Firstly, in order to achieve accurate gearbox oil temperature forecasting, an improved deep deterministic policy gradient algorithm with a CNN–LSTM basic learner is proposed, which can excavate the complex relationship between oil temperature and working condition. Secondly, a reward incentive function is designed to accelerate the training time costs and to stabilize the model. Further, a variable variance exploration strategy is proposed to enable the agents of the model to fully explore the state space in the early training stage and to gradually converge in the training later stage. Thirdly, a multi-critics network structure is adopted to solve the problem of inaccurate Q-value estimation, which is the key to improving the prediction accuracy of the model. Finally, KDE is introduced to determine the fault threshold to judge whether the residual error is abnormal after EWMA processing. The experimental results show that the proposed model achieves higher prediction accuracy and shorter fault detection time costs.

show abstract

“…Specially, artificial intelligence (AI) algorithms enable high performance for edge servers [6]. Therefore, this paper tries to utilize one basic AI algorithm of the cognitive computing architecture, such as SOAR [7] and ACT-R [8], i.e., reinforcement learning, to resolve the problem. However, the traditional deep reinforcement learning (DRL) algorithm [9] is time-consuming because it needs to train various types of tasks to learn the latest strategy in a new environment.…”

Section: Introductionmentioning

confidence: 99%

A Multiagent Meta-Based Task Offloading Strategy for Mobile-Edge Computing

Ding

Luo

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

View full text Add to dashboard Cite

Task offloading in mobile edge computing (MEC) improves the efficacy of mobile devices in terms of computing performance, data storage, and energy consumption by offloading computational tasks to edge servers. Efficient task offloading can leverage MEC technology to reduce task processing latency and energy consumption. By integrating the reasoning ability and machine intelligence of the cognitive computing architecture, such as SOAR and ACT-R, reinforcement learning (RL) algorithms have been applied to resolve the task offloading in MEC. To solve the problem that conventional Deep RL (DRL) algorithms cannot adapt to dynamic environments, this paper proposed a task offloading scheduling strategy which combined multiagent reinforcement learning and meta-learning. In order to make the two actions of charging time and offloading strategy fully considered at the same time, we implemented a learning network of two agent on a mobile device. To efficiently train the policy network, we proposed a first order approximation method based on clipped surrogate objective. Finally, the experiments are designed with variety of the number of subtasks, transmission rate, and edge server performance, and the results show that the MRL-based strategy has the overwhelming overall performance and can be quickly applied in various environments with good stability and generalization.

show abstract

A Soar-Based Space Exploration Algorithm for Mobile Robots

Cited by 10 publications

References 30 publications

A review of methodologies for path planning and optimization of mobile robots

A review of methodologies for path planning and optimization of mobile robots

A Fault Detection Method Based on an Oil Temperature Forecasting Model Using an Improved Deep Deterministic Policy Gradient Algorithm in the Helicopter Gearbox

A Multiagent Meta-Based Task Offloading Strategy for Mobile-Edge Computing

Contact Info

Product

Resources

About