Shibendu Shekhar Roy

Effects of turning gait parameters on energy consumption and stability of a six-legged walking robot

Minimization of energy consumption plays a key role in the locomotion of a multi-legged robot used for various purposes. Turning gaits are the most general and important factors for omni-directional walking of a six-legged robot. This paper presents an analysis on energy consumption of a six-legged robot during its turning motion over a flat terrain. An energy consumption model is developed for statically stable wave gaits in order to minimize dissipating energy for optimal feet forces distributions. The effects of gait parameters, namely angular velocity, angular stroke and duty factors are studied on energy consumption, as the six-legged robot walks along a circular path of constant radius with wave gait. The variations of average power consumption and energy consumption per unit weight per unit traveled length with the angular velocity and angular stroke are compared for the turning gaits of a robot with four different duty factors. Computer simulations show that wave gait with a low duty factor is more energy-efficient compared to that with a high duty factor at the highest possible angular velocity. A stability analysis based on normalized energy stability margin is performed for turning motion of the robot with four duty factors for different angular strokes.

Kinematics, Dynamics and Power Consumption Analyses for Turning Motion of a Six-Legged Robot

This paper deals with kinematics, dynamics and power consumption analyses of a six-legged robot generating turning motions to follow a circular path. Direct and inverse kinematics analysis has been carried out for each leg in order to develop an overall kinematics model of the six-legged robot. It aims to estimate energy-optimal feet forces and joint torques of the six-legged robot, which are necessary to have for its real-time control. To determine the optimum feet forces, two approaches are developed, such as minimization of norm of feet forces and minimization of norm of joint torques using a least square method, and their performances are compared. The developed kinematics and dynamics models are tested through computer simulations for generating turning motion of a statically stable six-legged robot over flat terrain with four different duty factors. The maximum values of feet forces and joint torques decrease with the increase of duty factor. A power consumption model has been derived for the statically stable wave gaits to minimize the power requirement for both optimal foot force distributions and optimal foot-hold selection. The variations of average power consumption with the height of the trunk body and radial offset have been analyzed in order to find out energy-optimal foothold. A parametric study on energy consumption has been carried out by varying angular velocity of the robot to minimize the total energy consumption during locomotion. It has been found that the energy consumption decreases with the increase of angular velocity for a particular traveled distance.

Soft computing-based expert systems to predict energy consumption and stability margin in turning gaits of six-legged robots

Turning gaits are the most general and very important ones for omni-directional walking of a six-legged robot. Soft computing-based expert systems have been developed in the present work to predict specific energy consumption and stability margin of turning gait of a six-legged robot. Besides back-propagation neural network, three approaches based on adaptive neuro-fuzzy inference system have been developed and their performances are compared with each other. Genetic algorithm-tuned multiple adaptive neuro-fuzzy inference systems are found to perform better than other approaches. This could be due to a more exhaustive search conducted by the genetic algorithm in place of back-propagation algorithm and the use of two separate adaptive neuro-fuzzy inference systems for two different outputs.

Inverse kinematics of redundant serial manipulators using interval method in handling uncertainties

The paper presents an application of Interval method to solve the inverse kinematics of a serially connected redundant manipulator, aiming its use in design optimization of manipulators. The article attempts to solve inverse kinematics, when the lengths of the links of the manipulator are not precisely known. The sources of uncertainties include manufacturing tolerances, approximations for complex link geometries, inaccuracies in joint angle measurements etc. The inverse kinematics is intended to produce solutions for joint variables in interval of tolerances for specified end effector accuracy range. The redundancy resolution is cast as an optimization problem with arm isotropy as performance metric. In solving for the inverse kinematics, two stage interval optimization method is implemented, where, in the first stage, bisection technique is applied and in the second stage interval discrete random variable method is used. As exemplar problem solving, two cases, namely a planar3-degrees-of-freedom and a spatial 5-degrees-of-freedom serial link manipulators are considered.

Dynamic modeling and energy consumption analysis of crab walking of a six-legged robot

In this paper, an attempt has been made to develop a detailed dynamic model of a realistic six-legged robot during its crab motion. An energy consumption model has been derived for statically stable wave-crab gaits after considering a minimum of dissipating energy for optimal feet forces distributions. Two approaches, such as minimization of norm of feet forces and minimization of norm of joint torques have been developed. The performances of these approaches have been compared with each other for different values of duty factor. The effects of walking parameters, namely velocity, stroke, duty factor and crab angle are studied on energy consumption during crab walking. Wave gait with a lower duty factor is found to be more energy-efficient compared to that with the higher duty factor at the highest possible velocity.

Energy Optimal Trajectory Planning of a Robotic Manipulator Using Genetic Algorithm

The present paper deals with collision free optimal trajectory planning of a 3 degrees of freedom (DOF) spatial manipulator and the minimization of energy required by the actuators to perform the desired trajectory. A fourth degree polynomial of time is assumed to describe the trajectory of the manipulator. The energy required to perform a specific trajectory depends on the torque requirements at each instant of time while the manipulator moves along the trajectory. Genetic algorithm is used to optimize the coefficients of the polynomial so that the trajectory consumes minimum energy. Moreover, an optimal collision free is calculated with the help of a multi‐objective genetic algorithm which minimizes the torque and maximizes the distance to the obstacle simultaneously.

Modelling and predicting surface finish in turning using an adaptive neuro-fuzzy inference system

Surface finish is a key factor in the machining process, and it is used to evaluate and determine the quality of a product. Therefore, modelling and predicting of surface finish of a workpiece in turning play an important role in manufacturing industry since turning is most common machining operation. This paper illustrates the application of adaptive neuro-fuzzy inference system ANFIS for modelling and predicting the surface finish in turning operation for set of given cutting parameters, namely spindle speed, feed rate and depth of cut. Three different membership functions, triangular, trapezoidal and generalised bell shaped, were adopted during the hybrid-training process i.e., combination of backpopagation gradient descent method and least square method of ANFIS in order to compare the prediction accuracy of surface finish by the three membership functions. The predicted surface finish values obtained from ANFIS were compared with experimental data. The comparison indicates that the adoption of triangular, trapezoidal and generalised bell shaped membership functions in proposed system achieved satisfactory accuracy. The generalised bell-shaped membership function in ANFIS achieves slightly higher prediction accuracy than other membership functions.

Soft computing-based approaches to predict energy consumption and stability margin of six-legged robots moving on gradient terrains

Soft computing-based approaches have been developed to predict specific energy consumption and stability margin of a six-legged robot ascending and descending some gradient terrains. Three different neuro-fuzzy and one neural network-based approaches have been developed. The performances of these approaches are compared among themselves, through computer simulations. Genetic algorithm-tuned multiple adaptive neuro-fuzzy inference system is found to perform better than other three approaches for predicting both the outputs. This could be due to a more exhaustive search carried out by the genetic algorithm in comparison with back-propagation algorithm and the use of two separate adaptive neuro-fuzzy inference systems for two different outputs. A designer may use the developed soft computing-based approaches in order to predict specific energy consumption and stability margin of the robot for a set of input parameters, beforehand.

Dynamic Modeling and Optimal Foot Force Distribution of Quadruped Walking Robot

In the present paper, an attempt has been made to carry out kinematic and dynamic analysis of a quadruped walking robot. The direct and inverse kinematic analysis for each leg has been considered in order to develop an overall kinematic model of the robot, when it follows a straight path with two phase discontinuous gait. This study also aims to estimate optimal foot force distributions of quadruped robot, which is necessary for its real-time control. Three different formulations namely, tip-point force formulation, joint torque formulation and joint power formulation, have been developed. Simulation result shows that joint power formulation is more energy efficient foot force formulation than other two formulations.

An Application of ANFIS-Based Intelligence Technique for Predicting Tool Wear in Milling

In this work, an attempt has been made to design an intelligence technique-based expert system using adaptive neuro-fuzzy inference system (ANFIS) for predicting tool wear in milling operation. An artificial neural network is used for designing an optimized fuzzy logic system, so that the tool wear can be modeled for a set of input cutting parameters, namely feed rate, depth of cut, and cutting force. The proposed method uses two different learning approaches, namely back-propagation gradient descent method alone and hybrid method (i.e., combination of the least squares method and back-propagation algorithm) for training of first-order Sugeno-type fuzzy system. The predicted tool wear values derived from proposed ANFIS were compared with the experimental data.