Fuzzy Motion Research Articles

A real‐time fuzzy motion planning system for unmanned aerial vehicles in dynamic 3D environments

This paper presents a new fuzzy potential system to plan the motion of Unmanned Aerial Vehicles (UAVs) in dynamic 3D Space. The system consists of two fuzzy subsystems representing the attractive model and the repulsive model of virtual forces in 3D. The attractive model will generate the attractive force required to pull the UAV in a smooth and optimized trajectory to land softly on a moving or stationary target. The repulsive model will generate the required repulsive force to avoid stationary or moving obstacles in 3D Space. The attractive fuzzy inference system takes the relative position and relative velocity between UAV and the target in the x, y, and z directions as inputs. It generates the required attractive force in the x, y, and z directions. The repulsive fuzzy inference system takes the relative position between UAV and obstacle in the xyz directions as input. Fuzzy associative memory (FAM) models the inputs and generates the required repulsive force in the x, y, and z directions. As a result, the UAV is considered to be moving under the influence of fuzzy virtual attractive and repulsive forces simultaneously. Accordingly, it will be able to change both its altitude and projected planner position concurrently and resolves the local minima problem if occurred. On the other hand, many classical models in dynamic environments require several additional inputs, such as the relative position and relative velocity, which increase the requirement on the measurement system to localize the moving objects in the 3D Space. Several experiments were performed and discussed to verify the robustness and effectiveness of the proposed motion planner with real-time implementations. The system performance was validated using three robotics platforms, two quadcopter drones, and one ground robot. The position and orientation of each robot were defined using a motion capture system with 6 opti-track cameras. The motion planning system produces a quadcopter drone's efficient and accurate low-frequency trajectory. The generated trajectory allows the drone to track the ground robot and avoid collision effectively with the second drone in the vicinity.

A modified artificial bee colony based fuzzy motion tracking scheme for mobile robot

In this study a new modified artificial bee colony algorithm for the optimization of the fuzzy control scheme for motion tracking of mobile robot is developed. The modification is based on using some features from the particle swarm optimization algorithm to improve solution quality. The modified artificialbee colony (MABC) balance the exploration and exploitation of the original one. This balancing results in going through the global search space and increases the convergence speed and solution accuracy. MABC is then used for the design of an efficient fuzzy system that perform motion tracking for mobile robot more accurate through minimizing a suitable selected objective function. Results illustrate the high quality of the proposed method.

The Design and the Development of a Biped Robot Cooperation System

The aim of this paper is to design a fuzzy motion control algorithm for a developed monocular vision system based on a cooperative transportation system of two humanoid robots. The control strategies of the cooperation transportation system contain three stages, including object searching, walking toward the transported object, and cooperatively moving the transported object. To have different moving speeds, the gait step size was pre-planned as two different modes, i.e., one of the gaits is selected to let the HR have large variations of motion and another gait is to make the HR with small variations. The fuzzy motion control algorithm is utilized to select the appropriate mode of gait. Both humanoid robots can actively search and move to the front of the target object, then cooperatively lift the target and carry it to the platform. The task of synchronous movement is controlled with fuzzy techniques through the control terminal. From the experimental results, it can be seen that both robots can distinguish the orientation of the target, move to the appropriate position, and then successfully raise the target together.

Application of Fuzzy PID Position Control Algorithm in Motion Control System Design of Palletizing Robot

In view of the low efficiency in traditional palletizing robot problem of poor control precision, this paper introduces fuzzy PID position control algorithm, based on the actual operation situation of palletizing robot; determined as palletizing robot FPGA hardware platform, hardware platform based on this fuzzy PID position control algorithm is applied to implement palletizing robot motion control system design. The simulation model of fuzzy PID motion control was established by MATLAB software for testing to determine that the fuzzy PID position control algorithm reflects the time quickly in the motion control of palletizing robot, and the actual overshooting is small, which is more suitable for the motion control algorithm of palletizing robot. Under this condition, the modular method is adopted to complete the system application design on the FPGA hardware platform.

A Real-Time Fuzzy Motion Planning System for Unmanned Aerial Vehicles in Dynamic 3d Environments

A Real-Time Fuzzy Motion Planning System for Unmanned Aerial Vehicles in Dynamic 3d Environments

Autonomous Underwater Robot Fuzzy Motion Control System with Parametric Uncertainties

The paper describes the design of a fuzzy motion control system of an autonomous underwater vehicle. A mathematical model of the underwater vehicle is synthesized. A fuzzy regulator for controlling the depth of immersion autonomous underwater vehicle is designed. The quality of control for step control, harmonic control, as well as various types of exogenous disturbances, is investigated. The comparison of the functioning quality of the designed fuzzy controller with the proportional–derivative controller is made. It is shown that the designed fuzzy controller provides a higher quality of control compared to the proportional–derivative controller. The proposed fuzzy controller provides high-quality control of the plant under uncertainties.

Motion control of multiple humanoids using a hybridized prim’s algorithm-fuzzy controller

Prim’s algorithm has demonstrated a very effective and selective method of solving the minimum spanning tree optimization problems. It is a greedy algorithm that starts from an empty spanning tree and reaches its goal by picking the minimum weight edges which alternately optimizes the path in less possible time. In this paper, the capability of prim’s algorithm in designing the behavioural controller of a humanoid robot has been shown. Here, a new hybrid PA–Fuzzy motion planning approach has been proposed that uses the concept of minimizing the distance between the robot and obstacles as well as robot and target. An optimal turning angle is generated by the hybrid controller that helps to avoid the obstacles present in the arena to create a collision-free path. The results obtained from hybrid PA–Fuzzy motion planning procedure show the capability of the controller in achieving the optimal paths in different environments with both static and dynamic obstacles. The results observed from simulation and experimental arenas are found to be in satisfactory agreement with each other producing minimal error limits. The developed hybrid technique is compared with some existing methodologies, and significant improvement is found in relation to path length and computational time.

Double-loop fuzzy motion control with CoG supervisor for two-wheeled self-balancing assistant robots

Though adding manipulator arms gives a two-wheeled self-balancing assistant robot (TSAR) the ability of fetching object, the degenerated balancing performance would occur without considering the effect of robot center-of-gravity (CoG) position when the manipulator arms operate. To tackle this problem, a double-loop fuzzy motion control (DFMC) system is proposed to control the TSAR moving, turning, and reaching a desired position while keeping TSAR balanced. A CoG supervising controller is proposed to control the body pitch angle of TSAR with respect to manipulator arms operate. To show the effectiveness of the CoG supervising controller, three engagement scenarios are applied in the presence of external disturbances and system uncertainties. The experimental results show that the proposed DFMC system with considering CoG supervising controller can achieve better motion performance than without considering that. Further, a visual serving technique is used for doing object recognition and a fuzzy guidance control (FGC) is proposed to let the TSAR can implement the object tracking mission. The experimental results show that not only the DFMC system can maintain TSAR balance but also the FGC system can lead TSAR to fetch target object successfully.

Human Action Recognition using Rule based Fuzzy Motion Feature Templates

This paper proposes a technique for human activity recognition in a video stream. To achieve high accuracy in activity recognition results, the method in its initial step deploys temporal template matching to recognize activities. As temporal templates are susceptible to get affected by speed, style and performance pattern of activity, so it becomes difficult to accurately differentiate among closely similar activities (e.g walking, running and jogging). The confusion in recognizing activities is reconciled by subsequent rule based activity distinction. The proposed method recognizes the human activities in video on various bench-marked data sets including KTH Dataset and Weizmann Dataset. Experimental results demonstrate the novelty of method with a wide spectrum of varied conditions. The average accuracy of the method is 97.20% under standard conditions.

Fuzzy Motion Planning for Nonholonomic Mobile Robot Navigation in Unknown Indoor Environments

Fuzzy Motion Planning for Nonholonomic Mobile Robot Navigation in Unknown Indoor Environments

Road Traffic Anomaly Detection Based on Fuzzy Theory

The road traffic scenes are usually complex and the traffic video is vulnerable to external factors such as light, weather, and obstructions. It is difficult to extract the traffic parameters and detect the traffic anomaly exactly with the existing image processing and analysis technologies under such uncertainty factors. Considering the advantages of the fuzzy theory in dealing with uncertain information, we use the fuzzy theory to handle the complex issues in traffic video surveillance and put forward a traffic anomaly detection algorithm. First, the fuzzy traffic flow is designed based on the virtual detection lines and the fuzzy theory. Second, the fuzzy traffic density is designed based on the pixel statistics and the fuzzy theory. Third, the target’s fuzzy motion state is designed based on the vehicle trajectory and the fuzzy theory. Besides, the relevant membership functions for these traffic parameters are designed to perform state evaluation. Finally, the traffic anomaly detection algorithm is designed based on above-mentioned fuzzy traffic parameters and fuzzy control rules. The experimental results show that the algorithm proposed performs well in road anomaly detection.

Sensor fusion of camera, GPS and IMU using fuzzy adaptive multiple motion models

A tracking system that will be used for Augmented Reality (AR) applications has two main requirements: accuracy and frame rate. The first requirement is related to the performance of the pose estimation algorithm and how accurately the tracking system can find the position and orientation of the user in the environment. Accuracy problems of current tracking devices, considering that they are low-cost devices, cause static errors during this motion estimation process. The second requirement is related to dynamic errors (the end-to-end system delay; occurring because of the delay in estimating the motion of the user and displaying images based on this estimate. This paper investigates combining the vision-based estimates with measurements from other sensors, GPS and IMU, in order to improve the tracking accuracy in outdoor environments. The idea of using Fuzzy Adaptive Multiple Models (FAMM) was investigated using a novel fuzzy rule-based approach to decide on the model that results in improved accuracy and faster convergence for the fusion filter. Results show that the developed tracking system is more accurate than a conventional GPS-IMU fusion approach due to additional estimates from a camera and fuzzy motion models. The paper also presents an application in cultural heritage context.

A Highly Accurate Model-Free Motion Control System with a Mamdani Fuzzy Feedback Controller combined with a TSK Fuzzy Feed-forward Controller

In this paper, a new intelligent robot motion control architecture --- a highly accurate model-free fuzzy motion control- is proposed in order to achieve improved robot motion accuracy and dynamic performance. Its architecture combines a Mamdani fuzzy proportional (P) and a conventional integral (I) plus derivative (D) controller for the feedback part of the system, and a Takagi-Sugeno-Kang fuzzy controller for the feed-forward, nonlinear part. The fuzzy P + ID controller improves the performance of the nonlinear system, and the TSK fuzzy controller uses a TSK fuzzy inference system based on extended subtractive- clustering method which integrates information on joint angular displacement, velocity and acceleration for torque identification. The advantage of this kind of model-free control is that it uses the information directly from the input/output of the nonlinear system, without any complex robot model computation, in order to decrease the control system's sensitivity to any dynamical uncertainty. Furthermore, parametric search for clustering parameters in extended subtractive clustering secures the high accuracy of the system identification. Consequently, this proposed model-free fuzzy motion control benefits from the advantages of two kinds of fuzzy system. It not only incorporates flexible design, good performance and simple conception but also ensures precise motion control and great robustness. Comparisons with other intelligent models and results from numerical studies on a 4-bar planar parallel mechanism show the effectiveness and competitiveness of the proposed control.

Topological Gaussian ARTs with Short-Term and Long-Term Memory for Map Building and Fuzzy Motion Planning

This paper proposes a cognitive architecture for building a topological map incrementally inspired by beta oscillations during place cell learning in hippocampus. The proposed architecture consists of two layer: the short-term memory layer and the long-term memory layer. The short-term memory layer emulates the entorhinal and the ? is the orientation system; the long-term memory layer emulates the hippocampus. Nodes in the topological map represent place cells (robot location), links connect nodes and store robot action (i.e. adjacent angle between connected nodes). The proposed method is formed by multiple Gaussian Adaptive Resonance Theory to receive data from various sensors for the map building. It consists of input layer and memory layer. The input layer obtains sensor data and incrementally categorizes the acquired information as topological nodes temporarily (short-term memory). In the long-term memory layer, the categorized information will be associated with robot actions to form the topological map (long-term memory). The advantages of the proposed method are: 1) it is a cognitive model that does not require human defined information and advanced knowledge to implement in a natural environment; 2) it can generate the map by processing various sensors data simultaneously in continuous space that is important for real world implementation; and 3) it is an incremental and unsupervised learning approach. Thus, the authors combine their Topological Gaussian ARTs method (TGARTs) with fuzzy motion planning to constitute a basis for mobile robot navigation in environment with slightly changes. Finally, the proposed approach was verified with several simulations using standardized benchmark datasets and real robot implementation.

Rotation invariant multi-frame image super resolution reconstruction using Pseudo Zernike Moments

The purpose of multi-frame super resolution (SR) is to combine multiple low resolution (LR) images to produce one high resolution (HR) image. The major challenge of classic SR approaches is accurate motion estimation between the frames. To address this problem, fuzzy motion estimation method has been proposed that replaces value of each pixel using the weighted average of all its neighboring pixels in all LR images. However, in case of rotation between LR images, comparing the gray level of blocks is not a suitable criterion for calculating the weight. Hence, magnitude of Zernike Moments (ZM) has been used as a rotation invariant feature. Considering the more robustness of Pseudo Zernike Moments (PZM) to noise and its higher description capability for the same order compared to ZM, in this paper, we propose a new method based on the magnitude of PZM as a rotation invariant descriptor for representing the pixels in the weight calculation. Also, due to the fact that the phase of PZM provides significant information for image reconstruction, we propose a new phase-based PZM descriptor for SR by making the phase coefficients invariant to rotation. Experimental results on several image sequences demonstrate that the proposed algorithm outperforms other currently popular SR techniques from the viewpoint of PSNR, SSIM and visual image quality.

Singularity Avoidance for a Redundant Robot Using Fuzzy Motion Planning

The purpose of this study is to design a redundant robot meeting the specific task and tracking the specific trajectory. Although the Moore-Penrose pseudo-inverse kinematic is commonly using to solve the inverse kinematic problem, it cannot solve the singularity exists in some situations that the rank of Jacobian matrix is not full rank for the redundant robot. Thus, a fuzzy motion planning algorithm is proposed to solve the inverse kinematic with singularity. Finally, we can obtain the position of five axes robot manipulator using fuzzy motion planning mapping method, and the errors of the singular points are approximately to zero. The results prove the fuzzy inverse kinematic mapping method can robust singular point when the tracking path with singular points.

Design of Control System for Wave Compensation Crane Based on Fuzzy Adaptive Method and Motion Prediction Model

The wave compensation crane control system is a kind of artificial intelligence system which can be guided by fuzzy adaptive method and motion prediction model in the paper. Its purpose is to overcome the problem of time-delay in wave compensation actuator and the problem of non-linearity and time-variability in wave movement. A control system based on fuzzy adaptive method and motion prediction model is designed in this paper, including its hardware and software design. Then, a control strategy including the fuzzy adaptive algorithm and the motion prediction model is presented for the control system. At last, by the experimental verification in the crane, the research results show that the whole control system has a good feasibility and adaptability.

Research on ELM-based Image Restoration Algorithm

As the rapid development of the multi-media technology, more and more digital devices are used to take pictures. However, sometimes the images may be degraded due to disoperation, the environment, and system and so on. The main task for image recovery is combining the degraded images and some priori information to acquire the best estimation of the original image by using some kind of restoration algorithm. In this paper, a new image restoration algorithm based on ELM Neural Network and some edge information was proposed, aiming at the fuzzy motion images. As the experiment shows that comparing traditional BP Neural Network, the new restoration algorithm based on ELM Neural Network is simpler, faster, easier to implement, what's more, it's more suitable for real-time processing.

Dynamic Balance Control for Biped Robot Walking Using Sensor Fusion, Kalman Filter, and Fuzzy Logic

The walking ability is a fundamental research for biped robots, and there always needs to be a criterion used for judging the walking stability. The zero moment point (ZMP) criterion is one of the useful standards for measuring biped robot walking. Here, a new ZMP trajectory model with adjustable parameters is proposed to modulate the ZMP trajectory both in sagittal and lateral planes and make the ZMP trajectory more flexible. A dynamic balance control (DBC), which includes Kalman filter (KF) and the fuzzy motion controller (FMC), is also designed to keep the body balance and make the biped walking following the desired ZMP reference. In addition, KF is utilized to estimate the system states and reduce the effect caused by noise. Using sensor fusion technique, ZMP error and trunk inclination measured by the force sensor and accelerometer are served as the inputs for FMC, which is presented to correct each joint of the biped robot dynamically. When a biped robot walks under different ground conditions, the coordination of the designed ZMP trajectory and proposed DBC can achieve a successful biped walking. Finally, there are several experiments presented to demonstrate the feasibility and effectiveness of the proposed control scheme.

Efficient object motion prediction using fuzzy Petri net based modelling in a robot navigational environment

Predicting the next instance position of a moving object in a dynamic navigational environment is a critical issue as it involves uncertainty. This paper proposes a fuzzy rule-based motion prediction algorithm for predicting the next instance position of a moving object. The algorithm is robust in handling the uncertain data of real-life situation. The fuzzy rule base modeling is done using Fuzzy Petri Net (FPN) formalism. The prediction algorithm is tested for real-life bench-marked data sets and compared with existing motion prediction techniques. The performance of the algorithm is comparable to the existing prediction methods.