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This paper presents a real-time machine learning control (MLC) of articulated robotic manipulators using artificial bee colony optimization (ABC) algorithm incorporated with fuzzy theory. The modified ABC with dynamic weight is used to optimize the fuzzy structure and fractional order. The fractional parameters, fuzzy membership functions and rule base are determined by means of the ABC computation. This ABC-fuzzy hybrid learning algorithm is applied to real-time MLC of robotic manipulators by including fractional order proportional-integral-derivative (FOPID) control strategy. The MLC's control gain parameters are online tuned via the ABC-fuzzy optimization. With the kinematics analysis of a six-degree-of-freedom (DOF) articulated arm via reverse coordinates approach, an ABC-fuzzy MLC is developed to achieve motion control. A real-time operating system (RTOS) on a microprocessor collaborates with the ABC-fuzzy MLC to meet critical timing constraint by considering the dynamics of actuators. Finally, the mechatronic design and experimental setup of a six-DOF articulated robotic manipulator are constructed. Experimental results and comparative works are provided to demonstrate the merit of the proposed methods. Compared with the conventional control schemes, the proposed ABC-fuzzy MLC has theoretical and practice significance in term of real-time capability, online parameter tuning, convergent behavior and hybrid MLC. The proposed MLC methodologies are applicable to designing real-time modern controllers in both industry and academia.

This article presents a networked multirobot cyber-physical system (CPS) that employs artificial immune fuzzy optimization to achieve distributed formation control of embedded mobile robots. The artificial immune system (AIS) algorithm is combined with a fuzzy system to develop a hybrid optimization technique, called AIS-fuzzy. This metaheuristic is applied to designing the intelligent third cyber and fourth cognition levels in the networked multirobot CPS 5C structure of embedded robots to accomplish distributed formation control. The proposed CPS cyber and cognition levels with AIS-fuzzy paradigm incorporate with the first, second, and fifth levels by means of robotic sensors and actuators. System-on-a-programmable-chip (SoPC) methodology is adopted to present a pragmatic networked multirobot CPS in the field-programmable gate array chips. The presented SoPC-based AIS-fuzzy multirobot CPS features the seamless integration of computation, networking, and physical robotic processes. Experimental results are obtained, and comparative analyses are provided to illustrate the effectiveness and merits of the proposed multirobot CPS to perform distributed formation control of embedded mobile robots.

This paper presents a hybrid metaheuristic embedded system for intelligent vehicles using hypermutated firefly algorithm (FA)-optimized radial basis function neural network (RBFNN), called FA-RBFNN. With the Mecanum vehicle's dynamic model, the FA with hypermutation is fused with RBFNN to develop a real-time optimal controller of the four-wheeled Mecanum vehicles in a field-programmable gate array (FPGA) chip. This hybrid metaheuristics takes the benefits of neural network, FA, real-time control, and FPGA realization. All the FA-RBFNN, dynamic controller, and hardware circuits are implemented in one FPGA chip using System-on-a-Programmable Chip methodology. Comparative works and experimental results clearly illustrate that the proposed FPGA-based FA-RBFNN optimal controller outperforms the conventional control methods.

This paper presents a collision-free fuzzy formation control strategy of swarm robotic cyber-physical systems (CPSs) using a robust orthogonal firefly algorithm (OFA). The classical FA is fused with the Taguchi method and the ranking mutation process to present a hybrid artificial intelligence. This computational intelligence is employed with the fuzzy theory to develop optimal cyber and cognition levels in swarm robotic CPS 5C architecture. With robotic sensors and actuators, the OFA-fuzzy-based cyber and cognition levels are incorporated with the smart connection, data-to-information conversion, and configuration levels to design a pragmatic swarm robotic CPS using system-on-a-programmable-chip technology. The broadcast distributed control strategy and potential field are employed to address the formation control problem of swarm robotic CPSs with obstacles. In the proposed swarm robotic CPS, the embedded central processing unit, operating system, networking intellectual property (IP), and robotic custom IPs are integrated into field-programmable gate array chips. The experimental results and comparisons with other methods show the merits of the proposed swarm robotic CPS in achieving collision-free distributed formation control.

This study presents a field-programmable gate array (FPGA)-based mechatronic design and real-time fuzzy control method with computational intelligence optimization for omni-Mecanum-wheeled autonomous vehicles. With the advantages of cuckoo search (CS), an evolutionary CS-based fuzzy system is proposed, called CS-fuzzy. The CS&rsquo, s computational intelligence was employed to optimize the structure of fuzzy systems. The proposed CS-fuzzy computing scheme was then applied to design an optimal real-time control method for omni-Mecanum-wheeled autonomous vehicles with four wheels. Both vehicle model and CS-fuzzy optimization are considered to achieve intelligent tracking control of Mecanum mobile vehicles. The control parameters of the Mecanum fuzzy controller are online-adjusted to provide real-time capability. This methodology outperforms the traditional offline-tuned controllers without computational intelligences in terms of real-time control, performance, intelligent control and evolutionary optimization. The mechatronic design of the experimental CS-fuzzy based autonomous mobile vehicle was developed using FPGA realization. Some experimental results and comparative analysis are discussed to examine the effectiveness, performance, and merit of the proposed methods against other existing approaches.

This paper presents a backstepping holonomic tracking control method of embedded wheeled robots using an evolutionary fuzzy system with qualified ant colony optimization (ACO). The Taguchi method is applied to design an optimal ACO via an orthogonal array. This qualified ACO is then employed to develop an evolutionary fuzzy system, called FS-TACO. The proposed hybrid intelligent fuzzy system FS-TACO is realized in a field-programmable gate array (FPGA) to dynamic tracking control of three-wheeled holonomic mobile robots. In comparison with conventional control systems, this approach takes the advantages of Taguchi quality method, fuzzy system, ACO and FPGA technique, thereby obtaining better population diversity, avoiding premature convergence, and achieving self-adaptive holonomic control. The FPGA realization using system-on-a-programmable chip methodology of the proposed FS-TACO is more effective in practice for real-world embedded applications. Experimental results and comparative works are conducted to exhibit the merits of the proposed FPGA-based FS-TACO controller for three-wheeled holonomic mobile robots.

This paper presents an optimal fuzzy controller design method using an evolutionary strategy-based particle swarm optimization (ESPSO) to four-wheeled redundant mobile robots, called FC-ESPSO. In comparison with conventional fuzzy controllers, this approach takes the advantages of evolutionary strategy, fuzzy control, and PSO, thereby obtaining better population diversity, avoiding premature convergence, and achieving self-adaptive redundant control. This hybrid computing is then applied to develop an optimal fuzzy controller of redundant wheeled robots. The optimal parameters of the dynamic controller are self-adaptive via the proposed FC-ESPSO to resolve trajectory-tracking problem. Simulation results are given to exhibit the merits of the proposed FC-ESPSO than other related control methods for redundant wheeled robots.

This paper presents an evolutionary radial basis function neural network with genetic algorithm and artificial immune system (GAAIS-RBFNN) for tracking control of autonomous robots. Both the GAAIS-RBFNN computational intelligence and online tracking controller are implemented in one field-programmable gate array (FPGA) chip to cope with the optimal control problem of real-world mobile robotics. The hybrid GAAIS paradigm incorporated with Taguchi quality method is employed to determine the optimal structure of RBFNN. The control parameters of tracking controller are online tuned by minimizing the performance index using the proposed GAAIS-RBFNN to achieve trajectory tracking. Experimental results and comparative works are conducted to show the effectiveness and merit of the proposed FPGA-based GAAIS-RBFNN tracking controller using system-on-a-programmable-chip technology. This FPGA-based online hybrid GAAIS-RBFNN intelligent controller outperforms the existing bio-inspired RBFNN controllers using individual GA and AIS algorithms.

This paper presents a biologically inspired deoxyribonucleic acid (DNA) algorithm for solving the inverse kinematics of five degree-of-freedom (DOF) robotic manipulators. Although the 5-DOF robotic manipulator agilely maneuvers from one pose to another and easily performs some tasks, the inverse kinematics problem of the system is redundant. This problem has no general closed-form solutions or analytical solutions for the 5-DOF manipulator. The DNA soft computing is proposed to solve the redundant inverse kinematics problem of the 5-DOF robotic manipulators more efficiently. Simulation results are conducted to illustrate the effectiveness and merit of the proposed methods in comparison with conventional genetic algorithm (GA) and particle swarm optimization.

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This article presents an intelligent system-on-a-programmable-chip-based SoPC ant colony optimization ACO motion controller for embedded omnidirectional mobile robots with three independent driving wheels equally spaced at 120 degrees from one another. Both ACO parameter autotuner and kinematic motion controller are integrated in one field-programmable gate array FPGA chip to efficiently construct an experimental mobile robot. The optimal parameters of the motion controller are obtained by minimizing the performance index using the proposed SoPC-based ACO computing method. These optimal parameters are then employed in the ACO-based embedded kinematic controller in order to obtain better performance for omnidirectional mobile robots to achieve trajectory tracking and stabilization. Experimental results are conducted to show the effectiveness and merit of the proposed intelligent ACO-based embedded controller for omnidirectional mobile robots. These results indicate that the proposed ACO-based embedded optimal controller outperforms the nonoptimal controllers and the conventional genetic algorithm GA optimal controllers.

This paper presents a parallel elite genetic algorithm (PEGA) and its application to global path planning for autonomous mobile robots navigating in structured environments. This PEGA, consisting of two parallel EGAs along with a migration operator, takes advantages of maintaining better population diversity, inhibiting premature convergence, and keeping parallelism in comparison with conventional GAs. This initial feasible path generated from the PEGA planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Both global path planner and smoother are implemented in one field-programmable gate array chip utilizing the system-on-a-programmable-chip technology and the pipelined hardware implementation scheme, thus significantly expediting computation speed. Simulations and experimental results are conducted to show the merit of the proposed PEGA path planner and smoother for global path planning of autonomous mobile robots.

This paper presents polar‐space kinematic and dynamic control methods to achieve both trajectory tracking and stabilization for an omnidirectional wheeled mobile robot with three independent, omnidirectional, driving wheels equally spaced at 120 degrees from one another. Both kinematic and dynamic models of the robot in polar coordinates are presented. A kinematic control method is proposed based on feedback linearization and the kinematic model, whereas a dynamic controller is synthesized using backstepping and the dynamic model. Experimental results are presented to show the efficacy and usefulness of the proposed control methods.

SUMMARYThis paper presents a polar-space kinematics control method to achieve simultaneous tracking and stabilization for an omnidirectional wheeled mobile robot with three independent driving omnidirectional wheels equally spaced at 120° from one another. The kinematic model of the robot in polar coordinates is presented. With the kinematic model, a kinematic control method based on feedback linearization is proposed in order to achieve simultaneous tracking and stabilization. The proposed method is easily extended to address the path following problem. Computer simulations and experimental results are presented to show the effectiveness and usefulness of the proposed control method at slow speeds.

This work presents an adaptive fuzzy cerebellar model articulation controller (AFCMAC) for solving the tracking control problem for an omni-directional mobile robot. First, fuzzy logic and CMAC are combined, coupled with a triangular basis function that is embedded in the hypercube receptive-field space to yield non-constant differentiable basis functions, which simplify the complex structure and reduce the number of input space dimensions of CMAC. Both adaptive and control laws are developed to tune all of the control parameters online, accommodating uncertainty in tracking control for an omni-directional mobile robot. Hardware agencies and drive circuits are developed. This proposed AFCMAC is implemented in a high-performance field-programmable gate array (FPGA) chip using a hardware/software co-design method and the Qsys design concept with a reusable user IP (Intellectual Property) core library. An omni-directional mobile robot is thus produced. Finally, the trajectory tracking controllability of the robot along an elliptical path is experimented to evaluate the effectiveness of the proposed AFCMAC control scheme.

This paper presents a metaheuristic artificial immune system (AIS) algorithm for mobile robot navigation in a structured environment with obstacles. The generated collision-free path is optimal in the sense of the shortest distance by defining the performance index. The initial feasible path generated from the AIS planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Simulations results are conducted to show the merit of the proposed AIS path planner and smoother for global path planning of autonomous robot navigation.

This paper presents an intelligent fuzzy motion controller for three-wheeled omnidirectional mobile robots to achieve trajectory tracking and stabilization. The controller parameters of motion controller are on-line tuned using the fuzzy theory. These optimized parameters are employed in the intelligent motion controller to obtain optimal performance for three-wheeled omnidirectional mobile robots. This approach outperforms the conventional trial-and-error approaches and the off-line tuning methods. Simulation results are conducted to show the effectiveness and merit of the proposed intelligent fuzzy motion controller for three-wheeled omnidirectional mobile robots.

This paper presents a particle swarm optimization algorithm (PSO) for solving the inverse kinematics of 7 degree-of-freedom (DOF) robotic manipulators. Although the 7-DOF robotic manipulator agilely maneuvers from one pose to another and easily performs some tasks, the inverse kinematics problem of the system is redundant. This problem has no general closed-form solutions or analytical solutions for the 7-DOF manipulator. The PSO algorithm is proposed to solve the redundant inverse kinematics problem of the 7-DOF robotic manipulators more efficiently. This PSO algorithm is shown to take advantage of powerful intelligent background and constructive cooperation between particles. Simulation results are conducted to illustrate the effectiveness and merit of the proposed methods.

This paper presents an intelligent motion controller based on genetic algorithm (GA)-particle swarm optimization (PSO) hybrid metaheuristic algorithm for four-wheeled omnidirectional mobile robots. The optimal parameters of motion controller are obtained by minimizing the performance index using the proposed GA-PSO hybrid algorithm. GA has been combined with PSO in evolving new solutions by applying crossover and mutation operators on solutions constructed by particles. These optimal parameters are used in the GA-PSO motion controller to obtain better performance for four-wheeled omnidirectional mobile robots to achieve both trajectory tracking and stabilization. Simulation results are conducted to show the effectiveness and merit of the proposed hybrid GA-PSO intelligent motion controller for four-wheeled omnidirectional mobile robots.

This paper presents an intelligent motion controller for four-wheeled omnidirectional mobile robots with four independent driving wheels equally spaced at 90 degrees from one another by using ant colony optimization (ACO). The optimal parameters of motion controller are obtained by minimizing the performance index using the metaheuristic ACO algorithm. These optimal parameters are used in the ACO motion controller to obtain better performance for four-wheeled omnidirectional mobile robots to achieve both trajectory tracking and stabilization. Simulation results are conducted to show the effectiveness and merit of the proposed ACO-based intelligent motion controller for four-wheeled omnidirectional mobile robots.

This paper presents a polar-space optimal kinematic controller design based on ant colony optimization (ACO) computing method for omnidirectional mobile robots with three independent driving wheels equally spaced at 120 degrees from one another. The optimal control parameters are obtained by minimizing the performance index using the proposed ACO computing method. These optimal parameters are used in the ACO-based polar-space kinematic controller to obtain better performance for omnidirectional mobile robots to achieve both trajectory tracking and stabilization. Simulation results are conducted to show the effectiveness and merit of the proposed ACO-based polar-space kinematic controller for omnidirectional mobile robots.

This paper presents an efficient parallel elite genetic algorithm (PEGA) for global path planning of an omnidirectional mobile robot moving in a static environment expressed by a grid-based map. This efficient PEGA, consisting of two parallel EGAs along with a migration operator, is proposed for global path planning of the mobile robots. The PEGA takes advantages of maintaining better population diversity, inhibiting premature convergence and keeping parallelism than conventional GAs do. The generated collision-free path is optimal in the sense of the shortest distance. The pipelined hardware implementation of IP (Intellectual Property) core library on a field-programmable gate array (FPGA) chip is employed to significantly speedup the processing time. Furthermore, a soft-core processor and a real-time operating system (RTOS) are embedded into the same chip to perform the global path planning using hardware/software co-design technique and SoPC (System-on-a-Programmable-Chip) concept. The merit and performance of the proposed SoPC-based PEGA are illustrated by conducting several simulations and experiments.

This paper presents adaptive sliding-mode control methods for self-balancing and yaw rate control of a dynamically two-wheeled human transportation vehicle (HTV) with mass variations and system uncertainties. The proposed controllers aim to provide consistent driving performance for system uncertainties and different drivers whose weights cause parameter variations of the HTV. By decomposing the overall system into the yaw subsystems and the self-balancing subsystems with parameters variations with respect to different riders, two adaptive sliding mode controls are proposed to achieve self-balancing and yaw control. Numerical simulations and experimental results on different terrains show that the proposed adaptive sliding mode controllers are capable of achieving satisfactory control actions to steer the vehicle.

This paper develops methodologies and techniques for autonomous navigation of a tour-guide robot with a human-robot interaction system. The designed navigation system includes global localization, dynamic path planning, local goal-seeking, safe obstacle-avoidance, behavior fusion, and autonomous robot control. The RFID module for global pose initialization is presented based on the RSSI measurements, the proposed calibration method and least square method. With the gross initial robot pose, accurate robot pose tracking are achieved by fusing the RFID data and laser scanning measurements utilizing an extended Kalman filter. The global path is generated by dynamic programming method. The Petri-net model is employed to construct the event-driven logic control sequence from the present states of the robot and assigned tour missions. An operational scenario is used to show the effectiveness of the proposed autonomous navigation method.

This paper presents methodologies and techniques for multi-sensory fuzzy hybrid navigation of an active mobile robot assistant for the elderly people in known, cluttered and dynamic indoor environments. For self-localization, the geometric and topological representations are combined together to maintain the advantages of them and counteract their disadvantages. By using RFID system, the estimation of position hypothesis for similar regions in a known environment can be reduced. The global optimal path composed of via points is determined by using Dijkstra's dynamic programming approach. Fuzzy adaptive speed control law is designed to make the robot adapt to various users' speeds. Safe fuzzy hybrid navigation is achieved by fusing adaptive speed, goal-seeking and obstacle- avoidance from the sensing data of laser scanner and ultrasonic rangers. In addition, Experimental results are conducted to show the effectiveness and merits of the proposed methods.