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Highly optimized Q‐learning‐based bees approach for mobile robot path planning in static and dynamic environments

https://doi.org/10.1002/rob.22052

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s42452-019-1791-7openalex-snapshot

Simulation based calculation of the inverse kinematics solution of 7-DOF robot manipulator using artificial bee colony algorithm

inferredcites →https://doi.org/10.1016/b978-008045157-2/50081-xopenalex-snapshot

The Bees Algorithm — A Novel Tool for Complex Optimisation Problems

inferredcites →https://doi.org/10.1016/c2013-0-01365-5openalex-snapshot

Introduction to Mobile Robot Control

inferredcites →https://doi.org/10.1016/j.asoc.2016.01.006openalex-snapshot

A new Reinforcement Learning-based Memetic Particle Swarm Optimizer

inferredcites →https://doi.org/10.1016/j.compeleceng.2014.12.014openalex-snapshot

An adaptive genetic algorithm for robot motion planning in 2D complex environments

inferredcites →https://doi.org/10.1016/j.desal.2021.115443openalex-snapshot

An efficient deep reinforcement machine learning-based control reverse osmosis system for water desalination

inferredcites →https://doi.org/10.1016/j.dt.2019.04.011openalex-snapshot

A review: On path planning strategies for navigation of mobile robot

inferredcites →https://doi.org/10.1016/j.jestch.2015.09.009openalex-snapshot

Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity

inferredcites →https://doi.org/10.1016/j.jksuci.2011.07.001openalex-snapshot

Using the Bees Algorithm to select the optimal speed parameters for wind turbine generators

inferredcites →https://doi.org/10.1016/j.procs.2017.11.213openalex-snapshot

Using simplified swarm optimization on path planning for intelligent mobile robot

inferredcites →https://doi.org/10.1016/j.procs.2018.01.113openalex-snapshot

Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning

inferredcites →https://doi.org/10.1016/j.procs.2018.07.018openalex-snapshot

Methodology for Path Planning and Optimization of Mobile Robots: A Review

inferredcites →https://doi.org/10.1080/10798587.2016.1196880openalex-snapshot

Training ANFIS Using the Enhanced Bees Algorithm and Least Squares Estimation

inferredcites →https://doi.org/10.1080/23311916.2018.1426539openalex-snapshot

Using the Bees Algorithm for wheeled mobile robot path planning in an indoor dynamic environment

inferredcites →https://doi.org/10.1108/ir.2004.31.6.534.3openalex-snapshot

Introduction to Autonomous Mobile Robots

inferredcites →https://doi.org/10.1109/cac.2017.8244061openalex-snapshot

Application of deep reinforcement learning in mobile robot path planning

inferredcites →https://doi.org/10.1109/icra.2017.7989436openalex-snapshot

Stochastic functional gradient for motion planning in continuous occupancy maps

inferredcites →https://doi.org/10.1109/lra.2020.3026638openalex-snapshot

Mobile Robot Path Planning in Dynamic Environments Through Globally Guided Reinforcement Learning

inferredcites →https://doi.org/10.1109/rios.2015.7270743openalex-snapshot

Optimization of dynamic mobile robot path planning based on evolutionary methods

inferredcites →https://doi.org/10.1109/tnn.1998.712192openalex-snapshot

Reinforcement Learning: An Introduction

inferredcites →https://doi.org/10.1109/tsmca.2012.2227719openalex-snapshot

A Deterministic Improved Q-Learning for Path Planning of a Mobile Robot

inferredcites →https://doi.org/10.1115/detc2019-97536openalex-snapshot

Mobile Robot Obstacle Avoidance Based on Deep Reinforcement Learning

inferredcites →https://doi.org/10.1155/2019/1932812openalex-snapshot

Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method

inferredcites →https://doi.org/10.1177/027836498600500106openalex-snapshot

Real-Time Obstacle Avoidance for Manipulators and Mobile Robots

inferredcites →https://doi.org/10.1243/09596518jsce1004openalex-snapshot

Using the Bees Algorithm with Kalman Filtering to Train an Artificial Neural Network for Pattern Classification

inferredcites →https://doi.org/10.1371/journal.pone.0200738openalex-snapshot

Reinforcement learning for solution updating in Artificial Bee Colony

inferredcites →https://doi.org/10.2316/journal.201.2011.4.201-2312openalex-snapshot

A NOVEL APPROACH TO PATH PLANNING FOR AUTONOMOUS MOBILE ROBOTS

inferredcites →https://doi.org/10.3390/app9153057openalex-snapshot

Multi-Robot Path Planning Method Using Reinforcement Learning

inferredcites →https://doi.org/10.3390/s20071880openalex-snapshot

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

inferredcites →https://doi.org/10.5772/63484openalex-snapshot

Dynamic Path Planning Algorithm for a Mobile Robot Based on Visible Space and an Improved Genetic Algorithm

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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M2CS: A Multimodal and Campus‐Scapes Dataset for Dynamic SLAM and Moving Object Perception

inferred← citeshttps://doi.org/10.1007/978-3-031-14537-7_12openalex-snapshot

Α New Method to Generate the Initial Population of the Bees Algorithm for Robot Path Planning in a Static Environment

inferred← citeshttps://doi.org/10.1007/978-3-031-18272-3_15openalex-snapshot

Q-Learning in a Multidimensional Maze Environment

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Eine neue Methode zur Erzeugung der Anfangspopulation des Bienenalgorithmus für die Roboterpfadplanung in einer statischen Umgebung

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inferred← citeshttps://doi.org/10.1016/j.eswa.2024.123539openalex-snapshot

Expected-mean gamma-incremental reinforcement learning algorithm for robot path planning

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Modifiedment the Performance of Q-learning Algorithm Based on Parameters Setting for Optimal Path Planning

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Dynamic Path Planning using a modification Q-Learning Algorithm for a Mobile Robot

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An Optimized and Stable CF 2 Mobile Robot Motion Planning Approach and Adaptation for Moving Targets

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Q-Learning of Bee-Like Robots through Obstacle Avoidance

inferred← citeshttps://doi.org/10.1109/iscer58777.2023.00036openalex-snapshot

Q-Learning for Path Planning of a UAV under Energy Consumption Constraints

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Behaviour-Aware Adaptive Path Planning With Q-Learning Weight Adaptation

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Human-Aware Path Planning With Improved Virtual Doppler Method in Highly Dynamic Environments

inferred← citeshttps://doi.org/10.1109/tte.2022.3151852openalex-snapshot

Sine Resistance Network-Based Motion Planning Approach for Autonomous Electric Vehicles in Dynamic Environments

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Addressing unpredictable movements of dynamic obstacles with deep reinforcement learning to ensure safe navigation for omni-wheeled mobile robot

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A Comprehensive Review of Fault-Tolerant Routing Mechanisms for the Internet of Things

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inferred← citeshttps://doi.org/10.3390/app15136956openalex-snapshot

Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning

inferred← citeshttps://doi.org/10.3390/machines11020167openalex-snapshot

On the Use of a Genetic Algorithm for Determining Ho–Cook Coefficients in Continuous Path Planning of Industrial Robotic Manipulators

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QL-HIT2F: A Q-Learning-Aided Adaptive Fuzzy Path Planning Algorithm with Enhanced Obstacle Avoidance

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