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This paper proposes a robot calibration method using an extended Kalman filter (EKF) and an artificial neural network (ANN) based on a butterfly and flower pollination algorithm (ANN-BFPA) to improve the robot's absolute pose (position and orientation) accuracy. After establishing a geometric error model, the EKF, a robust optimization algorithm for a nonlinear system with Gaussian noise, was used to estimate geometric parameter errors and compensate for geometric errors. However, nongeometric errors caused by joint clearance, gear backlash, and link deflection could still affect the pose accuracy and interfere with the correctness of the model. Therefore, the ANN-BFPA was proposed to compensate for these errors. The ANN model was used to establish the complex relationship between joint lengths and pose error. In addition, BFPA was used to optimize weights and bias of the neural network. The efficiency of the proposed calibration method was evaluated using a Stewart platform. Experimental results demonstrated that the proposed method significantly improved the robot's pose accuracy and showed better performance than previous techniques.

This article proposes a method to improve the accuracy of a vernier absolute magnetic encoder. The encoder consists of a master and a nonius multipolar magnetic track. Sinusoidal signals from the master and nonius tracks are used to infer the absolute information. Unfortunately, these signals are contaminated by nonideal factors such as different amplitudes, dc-offsets, phase shifts, and random noise. Moreover, harmonics existing in the encoder signals distort the vernier principle and significantly affect the accuracy of the encoder. To address these problems, the present article proposes an efficient method with three main parts. The first is an observer phase-locked loop (OPLL), which is used to estimate the phase and eliminate the nonideal factors. The second is nonlinear phase compensation, which is used to correct the vernier principle that deviated due to the existing harmonics. Finally, a pole pitch compensation method is introduced to modulate the master phase angle from the OPLL to eliminate the harmonic distortion. The proposed method can eliminate the nonideal factors, harmonic distortion and improve the accuracy of the encoder. All the proposed methods were implemented on an ARM STM32F407ZG. The experimental results confirm the validity of the proposed method for practical applications.

Mobile robots have received much of attention in the last three decades due to their very high potential of applications such as smart logistics, exploration, and intelligent services. One of important functions of mobile robots is the navigation in which robot must know their location, the maps of environment and perform path planning with obstacle avoidance. In this work, we introduce an improvement of control algorithm for mobile robot using multi-layer sensor fusion toward the target of efficient obstacle avoidance. Based on our method, we used three layers of sensors arranging in three height-different planes of robot’s housing for sensor fusion. A control algorithm, which is extended from the so-called bubble rebound algorithm and uses signal from sensor system, was proposed. Experimental implementation on a mobile robot, named EAI, shows that our algorithm can control the robot to navigate and avoid obstacles much efficiently, in which obstacles in forms of different shapes and height can also be avoided. A high repeatability and stability of the algorithm is obtained.

This article presents a self-referencing lookup table (LUT) algorithm to enhance the multiturn accuracy of a battery-free multiturn absolute magnetic encoder. The encoder consists of a single-turn angle for the measurement of the degree of rotation within $2\pi $ using a multipolar magnet to achieve high resolution and a multiturn counter to monitor the total number of revolutions made by the encoder’s shaft through the different angles of the four bipolar magnets (BPMs) based on four-shaft gears. However, the signals from the BPMs are affected by factors, such as amplitude mismatch, dc offsets, random noises, and harmonic components that are as a result of electrical and mechanical distortions. To solve this problem, this article suggested a method derived from the single-turn angle and the raw signals from the BPMs to generate the LUTs for multiturn angles. These angles eliminate the effects of the disturbances. Additionally, a multiturn compensation algorithm is suggested to compensate for the number of revolutions made by the encoder. The proposed methods are simulated in MATLAB software and implemented in ARM STM32F407ZG. The effectiveness of the proposed method is demonstrated by the obtained results.

This paper proposes a method to improve the accuracy of an absolute magnetic encoder by using harmonic rejection (HR) and a dual-phase-locked loop (DPLL). The encoder consists of two permanent magnets: an edge-located multipolar magnet (MPM) and a center-located bipolar magnet, in which the signal-processing accuracy of the MPM is crucial for achieving high accuracy of the entire encoder. However, the MPM signals are disturbed by nonidealities such as dc offsets, amplitude mismatch, low- and high-order harmonics, and random noises. In this paper, the HR approach investigates the characteristics of nonidealities of the phase detector and rejects them by using gradient descent. In addition, the DPLL remains robust by maintaining a zero steady-state error during a phase jump, a constant frequency, and a ramp frequency. The proposed method is simulated with MATLAB software and implemented in ARM STM32F407ZG. The obtained results demonstrate efficient performance. This method can be applied to any use of quadrature sinusoidal signals, such as in power grids and in permanent-magnet synchronous motor phase detection.

Absolute magnetic encoders (AMEs) use two magnets: a ring multipolar magnet (MPM) generating high-resolution and improving the accuracy for the encoder, and a bipolar magnet in the center calculating the number cycle of MPM signals. The phase outputs of these AMEs are tracked from the sinusoidal signals of the MPM. However, these sine/cosine signals are disturbed by amplitude differences, offsets, phase-shift, harmonic components, and random noise. In order to solve this problem, this paper presents an adaptive linear neuron based on a third-order phase-locked loop (ALN-PLL) to improve the accuracy of AMEs. The proposed approach consists of two main parts: The first part is an ALN algorithm that uses the phase feedback of the third-order PLL in order to build the mathematical model of input signals, and then reject the disturbances. The second part is a third-order PLL that is designed based on a dominant pole approximation algorithm. The proposed PLL can reduce noise and eliminate dc-error during the phase step, frequency step, and frequency ramp. The simulation and experimental results demonstrate the effectiveness of the proposed approach.

This paper introduces a design process for developing a humanoid mobile robot, namely VieBot. Stemming from the design process, it is easy to adjust the design and extent robot behaviors to meet customer needs. Our key solution is to split the design into three modules related to robot behavior, kinematic computation and motion control. Base on the results from testbed module under comprehensive testing, the design is gradually improved to satisfy sophisticated requirement of assisting disabled people.

This paper presents an inverse kinematic based control system of a novel self-developed humanoid robot using a Microsoft Kinect as 3D sensor. Based on the processed sensory data from the Kinect, human gestures and motions are captured, analyzed and mapped into joint variables by using inverse kinematic equations. A significant improvement in the inverse kinematic algorithm has been made to achieve better perception of human gestures. The performance of the proposed system is evaluated in a newly developed test-bed under real-world conditions, which effectively shows perception-related error control in order to further improve the system.

This paper presents an approach for the improvement of the accuracy of absolute magnetic encoders (AME). The encoders comprise the following two magnets: a multipolar magnet to increase the resolution and the accuracy, and a center-located bipolar magnet for the calculation of the absolute angle. The multipolar signal processing is crucial for the increasing of the encoder accuracy; however, the multipolar signals are not ideal, i.e, dc offsets, different amplitudes, phase shifts, and random noise. The present paper proposes a calibration method that is based on the adaptive linear neural network (ADALINE) for the reduction of the effect of the nonidealities. In addition, to optimize the loop-acquisition time and to enhance the random-noise reduction, the bandwidth is adapted using the fuzzy phase-locked-loop (F-PLL). This method is simulated using Matlab software and is implemented on the ARM STM32F405R. The study results demonstrate the efficient high performance that can be achieved with the use of the proposed method.

This paper presents a method for the improvement of the performance of a gear-system-based battery-less multiturn absolute magnetic encoder (BLMA-ME). these encoders, the magnetic absolute encoder requires several sub-shafts and gear systems for the detection of multiple rotations. This paper describes some of the problems of the encoder for which a gear system is used, and a method to increase the resolution and accuracy.

The signals of magnetic encoders (MEs) are a pair sinusoidal waves: a sine wave and a cosine wave. Due to many different causes, they contain error factors such as offsets, amplitude differences, phase shifts, and random noise. This paper proposes auto-calibration and noise-reduction methods to reduce these errors and to estimate the positions of MEs. This method includes auto-calibration and adaptive-bandwidth phase-locked loop (ABW-PLL) algorithms. The auto-calibration algorithm normalized the sinusoidal signals of the MEs to the range of [−1; 1]. The ABW-PLL algorithm accurately estimated the phase angle of the MEs. Unlike the traditional filters, the ABW-PLL almost eliminated the time lag self-adaptively based on the input signals. The effectiveness of the proposed method is demonstrated by the simulation and experiment results.

The dynamic tangential contact of rough surfaces of frictional elements of a stick-slip microdrive is theoretically investigated. By applying the method of dimensionality reduction, the contact areas of the frictional partners are modeled such that the physical properties of the contact can be fully considered and the influence of the roughness is taken into account. The dynamics of the microscopic rough contact is combined with a macroscopic movement of the drive’s runner in a hybrid dynamic simulation. The numerical results show a good agreement with experimental data. Furthermore, an analytical relation between maximal tangential contact displacement and normal force applied to the contact is analyzed, allowing the contact behavior to be theoretically predicted.

This paper gives an overview about problems of modeling of piezo-actuated stick-slip micro-drives. It has been found that existing prototypes of such devices have been investigated empirically. There is only few research dealing with the theory behind this kind of drives. By analyzing the current research activities in this field, it is believed that the model of the drive depends strongly on the friction models, but in most cases neglecting any influences of the guilding system.These analyses are of fundamental importance for an integrated model combining friction model and mechanical model offering promising possibilities for future research.

This paper draws a line from early attempts of modeling stick-slip microdrives to open questions from today’s research. As a basis, it contains a collection of substantial investigations on piezo-actuated stick-slip microdrives for nanomanipulation purposes. Friction models showing special characteristics and their mathematical representations are reviewed. It is found that the working properties of stick-slip drives strongly depend on friction characteristics of the contact points between the guiding elements, which is known for years. However, numerous publications in the field of friction and remaining problems — which cannot be explained by known friction models — indicate that there is a demand for even more friction-related research. Former attempts to model stick-slip drives are based on the so-called LuGre friction model, which is shortly presented. An empirical model called CEIM is also analyzed. It is an adaption of the elastoplastic model. The latter can cover not only the phenomenon “0-amplitude” (described by the authors in recent publications), but also stick-slip based force generation scenarios. Nevertheless, interesting friction characteristics such as the generation of µN forces with stick-slip drives, which are already proven, cannot be covered by known friction models. It is pointed out which characteristics have to be considered.