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Motion energy perspective of tracked locomotion system of autonomous tractor-trailer robot

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  • Shafaei, S.M.
  • Mousazadeh, H.

Abstract

In realm of researches involved in autonomous tractor-trailer robot, novel purpose of this applied research has been dedicated to motion energy perspective of tracked locomotion system of the robot. Hence, motion energy indicators including motion energy consumption, motion energy dissipation, and drawbar pull energy of the robot were ascertained as affected by operational variables of robot forward speed (0.17, 0.33 and 0.5 m/s) and payload weight (1, 2, 3, 4 and 5 kN). Analytical results clarified that meaningful contribution of robot forward speed to the motion energy indicators was marginal (<10–35 times) in comparison with that of payload weight. Modeling results described that combinatorial effect of robot forward speed and payload weight on the motion energy indicators was synergetic. This disclosed linear increasing dependency of motion energy consumption (154.02–831.38 J/m2), motion energy dissipation (20.60–165.70 J/m2), and drawbar pull energy (135.96–800.4 J/m2) on concurrent proliferation of robot forward speed and payload weight. The aforementioned amplitudes divulge that 7.20–22.01% of motion energy consumption was dissipated by tracked locomotion system. Overall, these results are profitable for energy-efficient design and performance optimization of tractor-trailer robot with tracked locomotion system in order to proliferate autonomous transportation capacity of payloads, especially for indoor and outdoor shipping and warehouse of factories and industrial environments.

Suggested Citation

  • Shafaei, S.M. & Mousazadeh, H., 2023. "Motion energy perspective of tracked locomotion system of autonomous tractor-trailer robot," Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:energy:v:264:y:2023:i:c:s0360544222034065
    DOI: 10.1016/j.energy.2022.126520
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    References listed on IDEAS

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    4. Taghavifar, Hamid & Mardani, Aref, 2015. "Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference system," Energy, Elsevier, vol. 85(C), pages 586-593.
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