Semi-Autonomous Collaborative Control of Multi-Robotic Systems for Multi-Task Multi-Target Pairing

2011 ◽  
Author(s):  
Yushing Cheung ◽  
Jae H. Chung ◽  
Ketula Patel
Keyword(s):  
Mobile Robots ◽  
Potential Field ◽  
Control Method ◽  
Contact Forces ◽  
Robotic Systems ◽  
Simulation Studies ◽  
Sensory Data ◽  
Collaborative Control ◽  
Single Target ◽  
Robot Task

In many applications, it is required that heterogeneous multi-robots are grouped to work on multi-targets simultaneously. Therefore, this paper proposes a control method for a single-master multi-slave (SMMS) teleoperator to cooperatively control a team of mobile robots for a multi-target mission. The major components of the proposed control method are the compensation for contact forces, modified potential field based leader-follower formation, and robot-task-target pairing method. The robot-task-target paring method is derived from the proven auction algorithm for a single target and is extended for multi-robot multi-target cases, which optimizes effect-based robot-task-target pairs based on heuristic and sensory data. The robot-task-target pairing method can produce a weighted attack guidance table (WAGT), which contains benefits of different robot-task-target pairs. With the robot-task-target pairing method, subteams are formed by paired robots. The subteams perform their own paired tasks on assigned targets in the modified potential field based leader-follower formation while avoiding sensed obstacles. Simulation studies illustrate system efficacy with the proposed control method.

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

scholarly journals Motion Planning for a Chain of Mobile Robots Using A* and Potential Field

Robotics ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 20 ◽  
Author(s):  
A poorva ◽  
Rahul Gautam ◽  
Rahul Kala
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Potential Field ◽  
A Chain

Visual servoing of dynamic wheeled mobile robots with anti-interference finite-time controllers

2018 ◽  
Vol 38 (5) ◽  
pp. 558-567 ◽  
Author(s):  
Hua Chen ◽  
Lei Chen ◽  
Qian Zhang ◽  
Fei Tong
Keyword(s):  
Mobile Robots ◽  
Finite Time ◽  
Visual Servoing ◽  
Control Method ◽  
External Disturbance ◽  
Nonholonomic Systems ◽  
Stability Control ◽  
Interference Control ◽  
Wheeled Mobile Robots ◽  
Content Type

Purpose The finite-time visual servoing control problem is considered for dynamic wheeled mobile robots (WMRs) with unknown control direction and external disturbance. Design/methodology/approach By using finite-time control method and switching design technique. Findings First, the visual servoing kinematic WMR model is developed, which can be converted to the dynamic chained-form systems by using a state and input feedback transformation. Then, for two decoupled subsystems of the chained-form systems, according to the finite-time stability control theory, a discontinuous three-step switching control strategy is proposed in the presence of uncertain control coefficients and external disturbance. Originality/value A class of discontinuous anti-interference control method has been presented for the dynamic nonholonomic systems.

scholarly journals Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4181 ◽  
Author(s):  
Chun-Hui Lin ◽  
Shyh-Hau Wang ◽  
Cheng-Jian Lin
Keyword(s):  
Mobile Robots ◽  
Control Method ◽  
Fuzzy Controller ◽  
Bee Colony ◽  
Load Carrying ◽  
Navigation Control ◽  
Interval Type ◽  
Navigation Method ◽  
Neural Fuzzy

In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently.

scholarly journals Finite-Time Switching Control of Nonholonomic Mobile Robots for Moving Target Tracking Based on Polar Coordinates

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hua Chen ◽  
Shen Xu ◽  
Lulu Chu ◽  
Fei Tong ◽  
Lei Chen
Keyword(s):  
Mobile Robots ◽  
Finite Time ◽  
Control Method ◽  
Tracking Error ◽  
Switching Control ◽  
Polar Coordinates ◽  
Control Law ◽  
Moving Target ◽  
Nonholonomic Mobile Robots ◽  
Time Switching

In this paper, finite-time tracking problem of nonholonomic mobile robots for a moving target is considered. First of all, polar coordinates are used to characterize the distance and azimuth between the moving target and the robot. Then, based on the distance and azimuth transported from the sensor installed on the robot, a finite-time tracking control law is designed for the nonholonomic mobile robot by the switching control method. Rigorous proof shows that the tracking error converges to zero in a finite time. Numerical simulation demonstrates the effectiveness of the proposed control method.

scholarly journals Zoning Control Technology of Gob-Side Roadway Driving with Small Coal Pillar Facing Mining in a Special Isolated Island Working Face: A Case Study

2021 ◽  
Vol 11 (22) ◽  
pp. 10744
Author(s):  
Changliang Han ◽  
Houqiang Yang ◽  
Nong Zhang ◽  
Rijian Deng ◽  
Yuxin Guo
Keyword(s):  
Control Method ◽  
Coal Pillar ◽  
Control Technology ◽  
Stability Zone ◽  
Pressure Relief ◽  
Collaborative Control ◽  
Working Face ◽  
Underground Coal Mines ◽  
Engineering Practices

The gob-side roadway in an isolated island working face is a typical representative of a strong mining roadway, which seriously restricts the efficient and safe production of underground coal mines. With the engineering background of the main transportation roadway 1513 (MTR 1513) of the Xinyi Coal Mine, this paper introduces the engineering case of gob-side roadway driving with small coal-pillar facing mining in an isolated island working face under the alternate mining of wide and narrow working faces. Through comprehensive research methods, we studied zoning disturbance deformation characteristics and stress evolution law of gob-side roadway driving under face mining. Based on the characteristics of zoning disturbance, MTR 1513 is divided into three zones, which are the heading face mining zone, the mining influenced zone, and the mining stability zone. A collaborative control technology using pressure relief and anchoring is proposed, and the differentiated control method is formed for the three zones. For the heading face mining zone, the control method of anchoring first and then pressure relief is adopted; for the mining influenced zone, the control idea of synchronous coordination of pressure relief and anchorage is adopted; for the mining stability zone, the control method of anchoring without pressure relief is adopted. Engineering practices show that the disturbance influence distance of working face 1511 on MTR 1513 changes from 110 m advanced to 175 m delay. At this time, the surrounding rock deformation is effectively controlled, which verified the rationality of the division and the feasibility of three zoning control technology. The research results can provide reference for gob-side roadway driving with small coal pillar facing mining in a special isolated island working face.

Automated Quantum Entanglement and Cryptography for Networks of Robotic Systems

2021 ◽  
Author(s):  
Farbod Khoshnoud ◽  
Maziar Ghazinejad
Keyword(s):  
Mobile Robots ◽  
Quantum Entanglement ◽  
Quantum Control ◽  
Single Photon ◽  
Autonomous Systems ◽  
Photon Counting ◽  
Robotic Systems ◽  
Single Photons ◽  
Alignment Procedure ◽  
Automated Alignment

Abstract In this paper the procedure for automating the photon quantum experiments for mobile robotic applications is presented. Due to the rapid advances of quantum technologies and quantum engineering, the integration of quantum capabilities in robotic and autonomous systems will be inevitable, and therefore the study and investigation of compatibility and adaptability of quantum systems and classical autonomous systems is of great importance. In a quantum-classical hybrid setup, the source of single photon generation is placed on a leader robot which can send correlated single photons to robot followers. In the case of quantum entanglement, spontaneous parametric down-conversion process using nonlinear paired BBO crystals is implemented which sends entangled photons to the single photon counting modules installed on mobile robots. In the case of quantum cryptography, single photons are sent from Alice robot to Bob robot, where Alice has the course of single photon and Bob has a polarizing beamsplitter and two detectors and that can detect the polarization of photons as vertical and horizontal. Bob then can convert the polarizations to a digital signals as zeros and ones and use them as communication information for control purposes through a classical channel. Motorized optics equipment can automatically align the source of photons to detectors on the mobile robots. The automated alignment procedure is one of the key enabling technologies in integrating quantum capabilities with control of mobile robotic systems. In this paper, in particular, the automated alignment is studied while considering the uncertainties in the dynamic of the system which can potentially cause the alignment task very challenging. The uncertainty analysis in the automated alignment is implemented by Optimal Uncertainty Quantification technique to ensure achieving the quantum control of the robotic systems and presented here for the first time.

Observer-based robust control for flexible-joint robot manipulators: A state-dependent Riccati equation-based approach

2020 ◽  
Vol 42 (16) ◽  
pp. 3135-3155
Author(s):  
Neda Nasiri ◽  
Ahmad Fakharian ◽  
Mohammad Bagher Menhaj
Keyword(s):  
Robust Control ◽  
Control Problem ◽  
Riccati Equation ◽  
Power Transmission ◽  
Control Method ◽  
Main Idea ◽  
Robotic Systems ◽  
Flexible Joint ◽  
State Dependent ◽  
Highly Nonlinear

In this paper, the robust control problem is tackled by employing the state-dependent Riccati equation (SDRE) for uncertain systems with unmeasurable states subject to mismatched time-varying disturbances. The proposed observer-based robust (OBR) controller is applied to two highly nonlinear, coupled and large robotic systems: namely a manipulator presenting joint flexibility due to deformation of the power transmission elements between the actuator and the robot known as flexible-joint robot (FJR) and also an FJR incorporating geared permanent magnet DC motor dynamics in its dynamic model called electrical flexible-joint robot (EFJR). A novel state-dependent coefficient (SDC) form is introduced for uncertain EFJRs. Rather than coping with the OBR control problem for such complex uncertain robotic systems, the main idea is to solve an equivalent nonlinear optimal control problem where the uncertainty and disturbance bounds are incorporated in the performance index. The stability proof is presented. Solving the complicated robust control problem for FJRs and EFJRs subject to uncertainty and disturbances via a simple and flexible nonlinear optimal approach and no need of state measurement are the main advantages of the proposed control method. Finally, simulation results are included to verify the efficiency and superiority of the control scheme.

scholarly journals Energy Modeling and Experimental Validation of Four-Wheel Mecanum Mobile Robots for Energy-Optimal Motion Control

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1372
Author(s):  
Liang Zhang ◽  
Jongwon Kim ◽  
Jie Sun
Keyword(s):  
Power Consumption ◽  
Mobile Robots ◽  
Motion Control ◽  
Prediction Accuracy ◽  
Experimental Validation ◽  
Control Method ◽  
Energy Modeling ◽  
Optimal Motion ◽  
Consumption Model ◽  
Mecanum Wheel

Four-wheel Mecanum mobile robots (FWMRs) are widely used in transportation because of their omnidirectional mobility. However, the FWMR trades off energy efficiency for flexibility. To efficiently predict the energy consumption of the robot movement processes, this paper proposes a power consumption model for the omnidirectional movement of an FWMR. A power consumption model is of great significance for reducing the power consumption, motion control, and path planning of robots. However, FWMRs are highly maneuverable, meaning their control is complicated and their energy modeling is extremely complex. The speed, distance, path, and power consumption of the robot can vary greatly depending on the control method. This energy model was mathematically implemented in MATLAB and validated by our laboratory’s Mecanum wheel robot. The prediction accuracy of the model was over 95% through simulation and experimental verification.

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