Minimizing Energy Cost in Multi-Legged Walking Machines

Teresa Zielińska


Due to their ability to avoid obstacles and to move over difficult terrain, moreover having the ability to adjust their posture, walking machines for many years have been considered as very promising devices for inspection, exploration and surveyance tasks, however still they have not been widely applied. One of the main limitations is the power supply. Six legged walking machines are robust from the point of view of their walking stability in difficult terrain, but their actuators (18 if each leg has active 3 DOF’s) adds to their weight what increases the energy consumption. The higher energy consumption requires more efficient batteries, but usually those are heavier, what again increases the energy demand. Therefore at the design stage a detailed analysis is required of how to decrease the energy consumption. This paper studies energy consumption considering the tripod gait of hexapods. The method used for energy evaluation is presented and the results are discussed. The discussion of energy saving both for the leg transfer phase and during the support phase, which is the most demanding phase, is presented. The energy consumption is expressed in the normalized form, depending on the normalized leg proportions, body height and step length. The straight line forward/backward and side walking are analyzed. The aim of the studies is to provide to the designers the information about favorable leg proportions taking into account the reduction of required energy and to provide the information which leg posture should be selected.
Author Teresa Zielińska (FPAE / IAAM)
Teresa Zielińska,,
- The Institute of Aeronautics and Applied Mechanics
Journal seriesJournal of Intelligent & Robotic Systems, ISSN 0921-0296
Issue year2016
Publication size in sheets0.8
Keywords in EnglishWalking machines, Energy optimal design, Energy favourable leg posture, Hexapods energy efficiency, Hexapods
ASJC Classification2208 Electrical and Electronic Engineering; 1702 Artificial Intelligence; 2209 Industrial and Manufacturing Engineering; 2210 Mechanical Engineering; 2207 Control and Systems Engineering; 1712 Software
Languageen angielski
Score (nominal)25
Score sourcejournalList
ScoreMinisterial score = 20.0, 06-07-2020, ArticleFromJournal
Ministerial score (2013-2016) = 25.0, 06-07-2020, ArticleFromJournal
Publication indicators WoS Citations = 1; Scopus Citations = 1; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.653; WoS Impact Factor: 2016 = 1.512 (2) - 2016=1.647 (5)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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