Control humanoid robot using intelligent optimization algorithms fusion with fourier series

In this article, the authors present a new method for humanoid robot motion planning, satisfying multiple objectives. In this method, the multiple objectives humanoid robot motion is formulated as a multiobjective optimization problem, considering each objective as a separate fitness function. Three different objectives are considered: (1) minimum energy consumption; (2) stability; and (3) walking speed. The advantage of the proposed method is that, in a single run of multiobjective evolution, generated humanoid robot motions satisfy each objective separately or multiple objectives simultaneously. Therefore, the humanoid robot can switch between different gaits based on environmental conditions. The results show that humanoid robot gaits generated by multiobjective evolution are similar to that of humans. To further verify the performance of optimal motions, they are transferred to the “Bonten-Maru” humanoid robot.

This paper proposed a new method for humanoid robot gait generation based on several objective functions. The proposed method is based on multiobjective evolutionary algorithm. In our work, we considered two competing objective functions: MCE and MTC. Based on simulation and experimental results, we conclude: Multiobjective evolution is efficient because optimal humanoid robot gaits with completely different characteristics can be found in one simulation run. The nondominated solutions in the obtained Pareto-optimal set are well distributed and have satisfactory diversity characteristics. The optimal gaits generated by simulation gave good performance when they were tested in the real hardware of "Bonten-Maru" humanoid robot. The optimal gait reduces the energy consumption and increases the stability during the robot motion. In the future, it will be interesting to investigate if the robot can learn in real time to switch between different gaits based on the environment conditions. In uneven terrains MTC gaits will be more

The solar home system (SHS) often experience a shading condition that caused by the objects near the PVs installment. The shading affect to the harvesting of power from generation side of the system. The system will experience lower output power because the characteristic of PVs have chance, such as the P-V curve. P-V curve changed by the rapid changing of irradiation level and temperature. In this curve, there is a point that produce highest rate of power harvesting, its called Maximum Power Point (MPP). The MPP Tracking is technique to track the MPP, so the system will run in optimal mode. In the other side, the partial shading to PV surface will cause more than one MPP, even though only one MPP that produce highest rate of output power. It is called global MPP, the other is local MPP. This article shows the genetic algorithm (GA) which is implemented in MPPT technique as a meta-heuristic search. The GA implemented in a DC/DC buck converter with the STM32 F4 microcontroller to run the algorithm and control the converter as real-time. The GA performance is compared to the PO’s by their system efficiency, power output, and tracking time. The experimental treatment done by a single tracking cycle and a single solar daily cycle in the roof of Herman Yohanes Building, Universitas Gadjah Mada. The test conducted for single solar daily cycle, shows that GA implementation on MPPT has 91% efficiency (in average) and PO implementation on MPPT has 89% efficiency (in average). In terms of output power, GA has an average output power + 10% higher than PO. While in terms of time tracking, GA has a mean time of + 61% longer than the PO

When a human needs to generate a large force, the human will try to apply an impulsive force cooperating whole body. However, exerting impulsive force is one of the most difficult tasks for humanoid robots, because a reaction force caused by the applied impulsive force may bring the humanoid robot down. This paper discusses a humanoid robot motion to generate a large force utilizing an impact. Breaking wooden boards by Karate-chop is taken as a case study because it is a typical example of tasks that utilize impulsive force. A humanoid robot motion for the Karate-chop is carefully designed to maximize the speed of a hand keeping the stability. In order to validate the designed motion, experiments are carried out using a small humanoid robot Fujitsu HOAP-2. In the experiments, three kinds of wooden boards are tested to break by the Karate-chop. The strength of wooden boards is estimated striking the boards with a free-falling plummet. Stabilizing control is also applied to the robot in order to stabilize the body after the impact. The experimentation results are reported in the paper

This paper proposes a method that minimizes the consumed energy by searching the optimal locations of the mass centers of the biped robot's links using Genetic Algorithm. This paper presents a learning controller for repetitive gait control of the biped walking robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of learning control to the biped robot's motion is shown via computer simulation and experimental results with 24 DOF biped walking robot.

Planning collision-free, dynamically-balanced movements for humanoid robots is a challenging problem. An effective approach consists of first planning a motion satisfying geometric and kinematic constraints (such as collision avoidance, joint angle limits, velocity limits, etc.) and, in a second stage, modifying this motion so that it respects dynamic balance criteria, such as those relative to the Zero Moment Point (ZMP). However, this approach currently suffers from the issue that the modified motion may give rise to new collisions with respect to the original motion, which can be very costly to deal with, especially for systems with many degrees of freedom and cluttered environments. Here we present an algorithm to modify the motions of humanoid robots under ZMP constraints without changing the original motion path, making thereby new collision checks unnecessary. We do so by adapting the minimum-time path parameterization under torque constraints algorithm of Bobrow et al. to the case of ZMP constraints. In contrast with a previous approach based on finite differences and iterative optimization to find the optimal path parameterization under ZMP constraints, our Bobrow-based algorithm finds this optimal parameterization in a single pass. We demonstrate the efficiency of this algorithm by simulations.

In this paper we focus on the issues of hardware implementation of genetic algorithms (GA) in hardware. In their classic implementation, the genetic algorithms search for a global minimum or maximum of a multidimensional function called the fitness function. If the problem, i.e. the fitness function, is too complex for a brute force search, we can look for a solution based on GA. In this situation we obtain desired results by performing parallel calculations on the “generation” of candidate solutions randomly distributed over the input data space. For these candidates we evaluate the fitness function and then we breed a next generation. During breeding we use operators that mimic chromosomal crossing to exchange of features between the candidates, and mutation operations to introduce new features into the population. The literature research shows that more than sixty different crossing algorithms are used with the GA in different purposes. Such a large number of crossing algorithms is a serious problem when developing a hardware solution. In this paper, we are reviewing a deployment of selected crossing operators in specialized hardware.

Acute Myeloid Leukimia (AML) is a type of cancer which attacks white blood cells from myeloid. AML subtypes M1, M2, and M3 are affected by the same type of cells called myeloblasts, so it needs more detailed analysis to classify.Momentum Backpropagation is used to classified. In its application, optimal selection of architecture, learning rate, and momentum is still done by random trial. This is one of the disadvantage of Momentum Backpropagation. This study uses a genetic algorithm (GA) as an optimization method to get the best architecture, learning rate, and momentum of artificial neural network. Genetic algorithms are one of the optimization techniques that emulate the process of biological evolution.The dataset used in this study is numerical feature data resulting from the segmentation of white blood cell images taken from previous studies which has been done by Nurcahya Pradana Taufik Prakisya. Based on these data, an evaluation of the Momentum Backpropagation process was conducted the selection parameter in a random trial with the genetic algorithm. Furthermore, the comparison of accuracy values was carried out as an alternative to the ANN learning method that was able to provide more accurate values with the data used in this study.The results showed that training and testing with genetic algorithm optimization of ANN parameters resulted in an average memorization accuracy of 83.38% and validation accuracy of 94.3%. Whereas in other ways, training and testing with momentum backpropagation random trial resulted in an average memorization accuracy of 76.09% and validation accuracy of 88.22%.

This paper describes the use of genetic algorithm (GA) implementation on tissue of breast cancer tumor and normal breast gene data sequences to analyze and discriminate among these data sequences. This discrimination is done between the breast cancer and non-breast cancer genetic factor data sequences based on optimal values generated after implementation of genetic algorithm in every single generation. Genetic algorithm is population-based evolutionary algorithm of soft computing techniques. Genetic algorithm utilizes arbitrary investigation of problem joined with evolutionary procedures like mutation (transformation) and crossover to improve the chance of predicting optimal solutions. GA provides different approaches to expand the chance of solving real-world genetic-related problems which enables to upgrade the execution of calculation. The main technique of GA is to produce possible guesses on provided information. The values calculated in the present work provide a way for progressive approach to design a framework to discriminate cancer and non-cancer data sequences.KeywordsGenetic algorithm (GA)Soft computing (SC)Breast cancer diagnosis

The car racing game is a game that has always been popular from the past until now. where this game has lots of interesting gameplay, especially when adding artificial intelligence (AI) which makes the gameplay of the game even more challenging because the game is more dynamic with various levels of difficulty. However, most research on the application of artificial intelligence (AI) to the gameplay of car racing games is only limited to the application of game opponents. using genetic algorithms for innovations in game gameplay. Optimization of the game configuration will also be carried out to determine the level or difficulty level of the game's gameplay. The research flow to be implemented is the indie development method where the development method is used by indie game makers. The output of this research is to make scientific publications in accredited national journals and patent rights for car racing game game products. The contribution to this research is to make gameplay that is different from previous research, namely in making gameplay or applying artificial intelligence (AI) to making arena or racing circuits and optimizing genetic algorithms by adding configuration settings to the genetic algorithm

Objectives: The implementation of Genetic algorithm in the symmetric block cipher Advanced Encryption Standard -128 (AES-128) algorithms to enhance the performance of cryptographic operations. Methods: Genetic algorithm is used for generating the best fit non-repetitive cipher key and for key distribution to design a dynamic Substitution box in AES-128. Findings: The study reveals that the efficiency of the cryptographic algorithm treated with Genetic algorithm is dependent on the variations in the number of generations and initial population size. The result shows that an optimum population size has less encryption and decryption time. Among the sample population size taken for the experiment, almost the average population size has minimum encryption and decryption time. Results from iteration variations shows that the average number of iterations has less encryption and decryption time. Improvements: The hybrid combination of Genetic algorithm and AES-128 can be further modified for images and audio messages also. Keywords: Genetic Algorithm, Iterations, Non-Repetitive Cipher Key, Population Size, Substitution Box

Economic dispatch is one of the most challenging problems of power system. Various algorithms for solving optimal power flow problem are found in the literature. The Genetic Algorithm (GA) based solution techniques are more promising than other techniques due to its capability of global searching, robustness, and it does not require derivative information. This paper presents a genetic-fuzzy based approach for solving economic dispatch problem. Algorithms for GA economic dispatch and GA-fuzzy economic dispatch are developed and compared. The results obtained show that GA-fuzzy based approach is faster than GA based approach and less noisy.

Whole-Body Control has been extensively used to achieve humanoid robot force and motion tasks simultaneously during recent years. However, most existing results have not incorporated low-level actuator dynamics and time delays yet. In this study, we propose a novel time-delayed Whole-Body Operational Space control (WBOSC) with series elastic actuator (SEA) dynamics. This type of controller generalizes our previously proposed distributed control structure to multi-input and multi-output free floating humanoid robotic systems. Namely, Cartesian stiffness control is adopted to design the WBOSC at the centralized level while motor damping control is implemented at the embedded level to remedy the stability deterioration caused by time delays. Additionally, embedded-level torque feedback control is formulated and physically interpreted as a shaping of the motor inertia. To ensure passivity, we separate the overall system into two subsystems interconnected in a feedback configuration. By the Lyapunov-Krasovskii functional technique, we propose a delay-dependent passivity criterion of the closed-loop system in the form of linear matrix inequalities (LMIs), and solve for the allowable maximum time delays via the passivity criterion. Numerical simulations of a dynamic locomotion process are used to validate the proposed passivity criterion and the WBOSC framework.

In the near future, humanoid robots will act as the partners of human beings in daily life. Among numerous human-like competencies, motion of humanoid robots is critical for providing humans with richer interactions with such robots. Motion plays an essential role in complementing spoken communication. Moreover, the motions of humanoid robots generate nonverbal communication in various contexts. Through this nonverbal communication, humans can interact with robots not only directly but also indirectly or even unconsciously, as if the robot were simply part of the environment. Before the developments of humanoid robots, embodied conversational agents (ECAs) were introduced as virtual embodied representations of humans that communicated multi-modally with humans and there has been a great deal of research on ECA behavior. ECAs and humanoid robots share many features in terms of how they communicate with humans. Nevertheless, simply adapting knowledge gathered from current ECA studies to a humanoid robot study is insufficient for the following reasons: 1. ECA studies lack knowledge focused on nonverbal communication, which has become more important in the physical world; 2. ECA studies have focused on developing agent-centered intelligence rather than a user- centered experience; 3. ECA studies have developed logics to generate motions automatically rather than to provide designers with the practical knowledge necessary to design desirable motions. Motivated by these three arguments, we seek to pioneer a new field of motion design between robot engineering and design discipline. To bring this motion study into design discipline, we focused on human-centered experience through nonverbal communication with a humanoid robot. This paper aims to outline sharable user experience in order to help designer create desirable motions for humanoid robots in various speechless contexts.

This paper presents a method for synthesizing motions of a humanoid robot that receives an object from a human, with focus on a natural object passing scenario where the human initiates the passing motion by moving an object towards the robot, which continuously adapts its motion to the observed human motion in real time. In this scenario, the robot not only has to recognize and adapt to the human action but also has to synthesize its motion quickly so that the human does not have to wait holding an object. We solve these issues by using a human motion database obtained from two persons performing the object passing task. The rationale behind this approach is that human performance of such a simple task is repeatable, and therefore the receiver (robot) motion can be synthesized by looking up the passer motion in a database. We demonstrate in simulation that the robot can start extending the arm at an appropriate timing and take hand configurations suitable for the object being passed. We also perform hardware experiments of object handing from a human to a robot.