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mobile robot control Using Pursuit-Evasion Differential Game Strategy for Double Integrator Dynamic control with Deep Reinforcement Learning

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DYNAMIC GAMES AND APPLICATIONS 2025年 1-29页

作者： Li, Zhenxiang Alnfiai, Mrim M. Jung, Low Tang Alotaibi, Nouf Nawar Bajuri, Mohd Yazid Alnefaie, Salma Mohsen M. Ghoneim, Mohamed E. Sichuan Vocat Coll Informat Technol Fac Software Guangyuan Sichuan Peoples R China Taif Univ Coll Comp & Informat Technol Dept Informat Technol POB 11099 Taif 21944 Saudi Arabia Univ Teknol Petronas Comp & Informat Sci Dept Seri Iskandar 32610 Malaysia Najran Univ Coll Educ Dept Special Educ Najran Saudi Arabia Univ Kebangsaan Malaysia UKM Fac Med Dept Orthopaed & Traumatol Kuala Lumpur Malaysia Taif Univ Coll Sci POB 11099 Taif 21944 Saudi Arabia Damietta Univ Fac Comp & Artificial Intelligence Dumyat Egypt Umm Al Qura Univ Fac Sci Math Dept Mecca Saudi Arabia

mobile robot control in pursuit-evasion scenarios poses significant challenges due to rapidly changing environments, unpredictable adversaries, and the need for real-time decision-making. Existing approaches often struggle to adapt dynamically while ensuring optimal performance regarding evasion success, resource efficiency, and safety. To solve this problem, we propose a novel Adaptive Model Predictive control (A-MPC) framework integrated with Deep Reinforcement Learning (DRL) and differential game theory. A-MPC distinguishes itself by dynamically adjusting control actions in response to real-time environmental changes, allowing mobile robots to predict adversarial behavior and optimize their strategies continuously. Double integrator dynamics ensures smooth control over position and velocity, while an adaptive reward structure balances critical objectives like evasion, energy efficiency, and collision avoidance. Deep Q-Network (DQN) techniques are also used in a feedback loop to refine decision-making, enhancing learning efficiency and robustness. The novelty of this approach lies in the seamless integration of A-MPC with DRL, enabling precise, adaptive maneuvers in complex multi-agent environments. Experimental evaluations demonstrate significant improvements in pursuit-evasion tasks, including reduced capture time, enhanced resource utilization, and superior agent coordination. This framework offers a comprehensive solution for real-time, intelligent evasion strategies, making it ideal for applications such as autonomous drones and robotic systems requiring high adaptability in dynamic environments.

关键词： mobile robot control Deep reinforcement learning DRL Double integrator dynamic control Adaptive model predictive control (A-MPC) Deep Q-network (DQN)

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Redesign of a Towing mobile robot control Architecture and Implementation of Outdoor Experiments Using the Transverse Function Approach

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1566-1566页

作者： Krysiak, Bartlomiej Pazderski, Dariusz Majchrzak, Jaroslaw Kotlarek, Marcin Mieszala, Piotr Michalski, Mateusz Maciolek, Krzysztof Nowak, Pawel Poznan Univ Tech Inst Automat Control & Robot Fac Control Robot & Elect Engn PL-60965 Poznan Poland B&R Automatyka Przemyslowa Sp zoo Ul Malachowskiego 10 PL-61129 Poznan Poland pmLogic Sp zoo Ul Lesna 43 PL-62070 Pokrzywnica Poland Poznan Univ Tech Inst Elect Engn & Elect Fac Automat Control Robot & Elect Engn PL-60965 Poznan Poland InterNow Pawel Nowak Liszkowo 21 PL-88180 Zlotniki Kujawskie Poland

This article discusses the redesign of a towing mobile robot to obtain a modern system for implementing mobile robot control research. controller architecture issues are presented, and a selected control algorithm is considered in detail. The reconstruction of the robot is also intended to ensure that the current standards for the electronic architecture controlling the robot are met and that this architecture can be easily developed to include components related to the safety of the robot's operation. The discussion of the control architecture is divided into a description of the high-level controller responsible for the position stabilization algorithm and a description of the low-level controller responsible for the drive motor control and robot safety. The high-level control algorithm is responsible for a trajectory tracking task realized with use of a transverse function approach algorithm. A time elastic band algorithm was also used to generate a reference trajectory, allowing the robot to be guided through waypoints. The low-level controller is comprehensively described with details on the industrial controller architecture used, the communication between the controller modules, and the interaction of these modules with the on-board computer. The redesign of the towing mobile robot was summarized by the implementation of outdoor experiments where the task of driving through reference points was completed.

关键词： control architecture mobile robot control transverse functions

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Hand Gesture Recognition Using Densely Connected Deep Residual Network and Channel Attention Module for mobile robot control

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷

作者： Sahoo, Jaya Prakash Sahoo, Suraj Prakash Ari, Samit Patra, Sarat Kumar Natl Inst Technol Rourkela Dept Elect & Commun Engn Rourkela 769008 Odisha India Vellore Inst Technol Sch Elect Engn SENSE Vellore 632014 Tamil Nadu India Indian Inst Informat Technol Dept Elect & Commun Engn Gandhinagar 382028 Gujarat India

Navigating a mobile robot in an indoor or outdoor environment is an interesting research area for human-robot collaboration (HRC). In such a scenario, hand gesture offers some unique abilities for HRC to provide nonverbal communication between the user and the robot. This article proposes a novel real-time hand gesture recognition (HGR) technique for mobile robot control application. A compact convolutional neural network (CNN)-based HGR system, denoted as densely connected residual channel attention module (DRCAM), is proposed to recognize the vision-based hand gestures effectively. Since fingers are the most vital sign for HGR, an attention mechanism using multiscale representation is proposed, which emphasizes finger information more effectively. The proposed CNN model employs the cascading structure of residual blocks with a multiscale channel attention module to learn low- to high-level information of hand gestures. In addition, the cascading structures are connected through dense connectivity, which strengthens the feature propagation and facilitates feature reuse. Experiments are conducted on an ingenuously developed dataset and a publicly available American sign language finger-spelling (ASL-FS) benchmarked dataset to evaluate the performance of the proposed technique. The experimental result illustrates that the proposed DRCAM network outperforms the state-of-the-art methods in terms of mean accuracy (MA) using the leave-one-subject-out cross-validation (LOO CV) test. Finally, the training model is used to develop a software-based user interface system for the control of a mobile robot in a real-time environment.

关键词： Convolutional neural networks Gesture recognition robots mobile robots Three-dimensional displays Computer architecture Cameras Convolutional neural network (CNN) dense connectivity densely connected residual channel attention module (DRCAM) depth sensor hand gesture recognition (HGR) human-robot collaboration (HRC) mobile robot control multiscale attention

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Preliminary Comparative Experiments of Support Vector Machine and Neural Network for EEG-based BCI mobile robot control

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JOURNAL OF robotICS NETWORKING AND ARTIFICIAL LIFE 2019年第4期5卷 269-272页

作者： Bandou, Yasushi Hayakawa, Takuya Kobayashi, Jun Kyushu Inst Technol Dept Syst Design & Informat Iizuka Fukuoka 8208502 Japan

Here we present experimental results of Electroencephalogram (EEG)-based Brain Computer Interface (BCI) for mobile robot control by means of Support Vector Machine (SVM) and Neural Network (NN). The authors had trained NNs using EEGs collected from subjects and verified the performance as BCI;however, the results were unsatisfactory for practical use. In this study, we have used SVM with Radial Basis Function (RBF) kernel function for further improvement and compared the performance with the NNs. Consequently, the SVMs outperformed the NNs in almost all cases. (C) 2019 The Authors. Published by Atlantis Press SARL.

关键词： Brain computer interface electroencephalography support vector machine neural network mobile robot control

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A Study on mobile robot control by Hand gesture Detection 3

A Study on Mobile Robot Control by Hand gesture Detection

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3rd International Conference on Information Technology Research (ICITR)

作者： Ikegami, Shinichi Premachandra, Chinthaka Sudantha, B. H. Sumathipala, S. Shibaura Inst Technol Dept Elect Engn & Comp Sci Grad Sch Engn & Sci Tokyo Japan Univ Moratuwa Fac Informat Technol Moratuwa Sri Lanka

ISBN: (纸本)9781728114705

Recently, method of controlling machine with natural control called Natural User Interface has attracted attention. particularly, human hand is considered to be communication media that can communicate information most naturally. In this paper, user hand is detected by following acquired skin color of the user's face, through face detection. After detecting the user hand, hand gesture is determined. Finally, a robot is controlled to have multi movements according to detected several hand gesture patterns. We succeeded in acquiring the skin color information from user's face and detecting both user's hand and hand gesture following that. In this paper, we developed a mobile robot to conduct experiments. We also confirmed that robot can be controlled by human hand gesture.

关键词： NUI Image Processing Raspberry pi mobile robot control

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A Study of mobile robot control using EEG Emotiv Epoch Sensor 3

A Study of Mobile Robot Control using EEG Emotiv Epoch Senso...

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3rd International Conference on Electrical Systems, Technology and Information (ICESTI)

作者： Yasin, Timothius Victorio Pasila, Afelix Lim, Resmana Petra Christian Univ Elect Engn Dept Jl Siwalankerto 121-131 Surabaya 60234 Indonesia

The study was using an EEG Emotiv Epoc+ sensor to recognize brain activity for controlling a mobile robot's movement. The study used Emotiv control Panel software for EEG command identification. The commands will be interfaced inside Mind Your OSCs software and processing software which processed inside an Arduino controller. The output of the Arduino is a movement command (ie. forward, backward, turn left, and turn right). The training methods of the system composed of three sets of thinking mode. First, thinking with doing facial expressions. Second, thinking with visual help. Third, thinking mentally without any help. In the first set, there are two configurations thinking with facial expression help as command of the mobile robot. Final results of the system are the second facial expressions configuration as the best facial expressions method with success rate 88.33 %. The second facial expression configuration has overall response time 1.60175 s faster than the first facial expressions configuration. In these two methods have dominant signals on the frontal lobe. The second facial expressions method have overall respond time 6.12 and 9.53 s faster than thinking with visual, and thinking without help respectively.

关键词： EEG emotiv epoch sensor mobile robot control

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Gesture recognition system for real-time mobile robot control based on inertial sensors and motion strings

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 66卷 33-48页

作者： Stancic, Ivo Music, Josip Grujic, Tamara Univ Split Fac Elect Engn Mech Engn & Naval Architecture Rudera Boskovica 32 Split 21000 Croatia

Navigating and controlling a mobile robot in an indoor or outdoor environment by using a range of body-worn sensors is becoming an increasingly interesting research area in the robotics community. In such scenarios, hand gestures offer some unique capabilities for human-robot interaction inherent to nonverbal communication with features and application scenarios not possible with the currently predominant vision-based systems. Therefore, in this paper, we propose and develop an effective inertial-sensor-based system, worn by the user, along with a microprocessor and wireless module for communication with the robot at distances of up to 250 m. Possible features describing hand-gesture dynamics are introduced and their feasibility is demonstrated in an off-line scenario by using several classification methods (e.g., random forests and artificial neural networks). Refined motion features are then used in K-means unsupervised clustering for motion primitive extraction, which forms the motion strings used for real-time classification. The system demonstrated an Fl score of 90.05% with the possibility of gesture spotting and null class classification (e.g., undefined gestures were discarded from the analysis). Finally, to demonstrate the feasibility of the proposed algorithm, it was implemented in an Arduinobased 8-bit ATmega2560 microcontroller for control of a mobile, tracked robot platform. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Human-robot interaction mobile robot control Inertial sensors Machine learning Real-time classification Hand gestures

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Open Eyes Influence on Electroencephalography Signals for Constructing Neural Network Classifiers as mobile robot control Brain-Computer Interface

Open Eyes Influence on Electroencephalography Signals for Co...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Hayakawa, Takuya Kobayashi, Jun Kyushu Inst Technol Dept Syst Design & Informat Iizuka Fukuoka Japan

ISBN: (纸本)9781538626023

This study aims to develop an EEG-based BCI using multilayered neural networks for mobile robot control, which you can utilize with open eyes actually watching the robot's behavior. In this study, EEG measurement experiments were conducted under the following experimental conditions: subjects closed or opened their eyes while they were imagining a desired mobile robot movement. To examine influence of open eyes on EEG signals, the authors trained multilayered neural networks for EEG classification using the EEG signals measured in the experiments, and confirmed that opening eyes had a negative effect on the classification performance. Furthermore, the experimental results showed that you need to take into account the date you recorded EEG signals in the training process of neural networks.

关键词： brain computer interface electroencephalography neural network mobile robot control

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Using an Evolutionary Fuzzy Neural Network for Sensor-based Wall-following control of a mobile robot

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SENSORS AND MATERIALS 2020年第11期32卷 3627-3645页

作者： Chen, Cheng-Hung Jeng, Shiou-Yun Lin, Cheng-Jian Natl Formosa Univ Dept Elect Engn Huwei Township 632 Yunlin Taiwan Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 411 Taiwan Natl Taichung Univ Sci & Technol Coll Intelligence Taichung 404 Taiwan

We propose an efficient evolutionary fuzzy neural network (EFNN) for mobile robot control. The proposed EFNN combines a fuzzy neural network (FNN) and an improved artificial bee colony (IABC) algorithm to implement the wall-following control of a mobile robot. To evaluate the wall-following control performance of the FNN, an efficient fitness function is defined. The three control factors (CFs) in the fitness function are the maintenance of the robot-wall distance, the avoidance of robot-wall collision, and the successful movement of the robot along a wall to travel around a stadium. The traditional ABC emulates the intelligent foraging behavior of honey bee swarms, but this algorithm performs favorably at exploration and poorly at exploitation. Therefore, the proposed IABC algorithm uses mutation strategies to balance exploration and exploitation. Furthermore, a new reward-based roulette wheel selection (RRWS) mechanism is adopted to obtain a more favorable solution during the learning process. Experimental results demonstrate that the proposed IABC obtains a smaller root mean square error (RMSE) than other methods in wall-following control.

关键词： mobile robot control fuzzy neural network artificial bee colony algorithm wall-following control differential evolution

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Implementation and Testing of Voice control in a mobile robot for Navigation

Implementation and Testing of Voice Control in a Mobile Robo...

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

作者： Sham, Sudeep Trung Quoc Nguyen Nauth, Peter Araujo, Rui Frankfurt Univ Appl Sci Fac Comp Sci & Engn Frankfurt Germany Univ Coimbra Inst Syst & Robot ISR UC Polo 2 PT-3030290 Coimbra Portugal Univ Coimbra Dept Elect & Comp Engn DEEC UC Polo 2 PT-3030290 Coimbra Portugal

ISBN: (纸本)9781728124933

Recently, the usage of the assistive robots will become a common device interface with the voice recognition system and controlled by the common spoken languages. To control the robots by voice has always triggered the humans desired. The speech/voice recognition system becomes comfortable and important system to control the machines in today's life and become a one of the hot topic in the development of the assistive robots. In this paper, a voice control software system is integrated with the SLAM algorithm available in robotic Operating Systems (ROS) and implemented to a mobile system of a robot "ROSWITHA". This paper also expresses the different ways of controlling the robot by developing a Graphical User Interface (GUI) with different controlling tabs including start navigation on created/available map, voice detection, enter the destination location and stop tabs for spontaneous abort of the system. The experiment and test is performed in a real-time and with different environments as well with different speakers to analyze the accuracy of the system. Herewith, this papers also describe the successful implementation of voice system with robot's movement according to humans spoken commands.

关键词： Voice/Speech Recognition Voice command Assistive robot SLAM RTAB-Map Navigation GUI mobile robot control

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