• Dilip Kumar Pratihar

      Articles written in Sadhana

    • Evolutionary robotics—A review

      Dilip Kumar Pratihar

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      In evolutionary robotics, a suitable robot control system is developed automatically through evolution due to the interactions between the robot and its environment. It is a complicated task, as the robot and the environment constitute a highly dynamical system. Several methods have been tried by various investigators to solve this problem. This paper provides a survey on some of these important studies carried out in the recent past.

    • Balanced gait generations of a two-legged robot on sloping surface

      Pandu Ranga Vundavilli Dilip Kumar Pratihar

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      In this paper, dynamically balanced gait generation problem of a 7-DOF two-legged robot moving up and down through the sloping surface is presented. The gait of the lower links during locomotion is obtained after assuming suitable trajectories for the swing leg and hip joint. The trunk motion is initially generated based on the concept of static balance, which is different from the well-known semi-inverse method and then checked for its dynamic balance calculated using the concept of Zero-Moment Point (ZMP). Lagrange–Euler formulation is attempted for the determination of joint torques. Average power consumption at each joint is then determined based on the computed torques. Moreover, the variations of dynamic balance margin and average power consumption are studied for both ascending and descending through the sloping surface. Both of them are found to be more for the ascending gait generation compared to those for the descending case. The effects of variations of the slope have also been studied on the average dynamic balance margin and power consumption for both the cases.

    • Fuzzy clustering of mechanisms

      Amitabha Ghosh Dilip Kumar Pratihar M V V Amarnath Guenter Dittrich Jorg Mueller

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      During the course of development of Mechanical Engineering, a large number of mechanisms (that is, linkages to perform various types of tasks) have been conceived and developed. Quite a few atlases and catalogues were prepared by the designers of machines and mechanical systems. However, often it is felt that a clustering technique for handling the list of large number of mechanisms can be very useful,if it is developed based on a scientific principle. In this paper, it has been shown that the concept of fuzzy sets can be conveniently used for this purpose, if an adequate number of properly chosen attributes (also called characteristics) are identified. Using two clustering techniques, the mechanisms have been classified in the present work and in future, it may be extended to develop an expert system, which can automate type synthesis phase of mechanical design. To the best of the authors’ knowledge, this type of clustering of mechanisms has not been attempted before. Thus, this is the first attempt to cluster the mechanisms based on some quantitative measures. It may help the engineers to carry out type synthesis of the mechanisms.

    • Analysis of double support phase of biped robot and multi-objective optimization using genetic algorithm and particle swarm optimization algorithm

      Rega Rajendra Dilip Kumar Pratihar

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      This paper deals with multi-objective optimization in gait planning of a 7-dof biped robot during its double support phase, while ascending and descending some staircases. For determining dynamic balance margin of the robot in terms of zero-moment point, its double support phase has been assumed to be consisting of two single support phases on non-coincidental parallel surfaces. Thus, dynamic balance margin of the biped robot during its double support phase is obtained by using a virtual zero-moment point of the system. Moreover, a smooth transition from single to double support phases in a cycle is to be maintained for the walking robots. Two contrasting objectives, namely power consumption and dynamic balance margin have been considered during optimization. Pareto-optimal fronts of solutions are obtained using genetic algorithm and particle swarm optimization algorithm, separately. To the best of the authors' knowledge, it is the first attempt to solve multi-objective optimization problem in double support phase of a biped robot.

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