A unified framework for operational range estimation of mobile robots operating on a single discharge to avoid complete immobilization

Kshitij Tiwari, Xuesu Xiao, Ashish Malik, Nak Young Chong

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
44 Downloads (Pure)


Mobile robots are being increasingly deployed in fields where human intervention is deemed risky. However, in doing so, one of the prime concern is to prevent complete battery depletion which may in turn lead to immobilization of the robot during the mission. Thus, we need to carefully manage the energy available to explore as much of the unknown environment as feasible whilst guaranteeing a safe return journey to home base. For this, we need to identify the key components that draw energy and quantify their individual energy requirements. However, this problem is difficult due to the fact that most of the robots have different motion models, and the energy consumption usually also varies from mission to mission. It is desirable to have a generic framework that takes into account different locomotion models and possible mission profiles. This paper presents a methodology to unify the energy consumption models for various robotic platforms thereby allowing us to estimate operational range in both offline and online fashions. The existing models consider a given mission profile and try to estimate its energy requirements whilst our model considers the energy as a given resource constraint and tries to optimize the mission to be accomplished within these constraints. The proposed unified energy consumption framework is verified by field experiments for micro UGV and multi-rotor UAV test-beds operating under myriad of environmental conditions. The online model estimates operational range with an average accuracy (measured with respect to true range across multiple field trials) of 93.87% while the offline model attains 82.97%.
Original languageEnglish
Pages (from-to)173-187
Number of pages15
JournalMechatronics: the science of intelligent machines
Publication statusPublished - 29 Dec 2018
MoE publication typeA1 Journal article-refereed


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