Abstract
To improve the energy efficiency of the Mecanum wheel, this article extends the dynamic window approach by adding a new energy-related criterion for minimizing the power consumption of autonomous mobile robots. The energy consumption of the Mecanum robot is first modeled by considering major factors. Then, the model is utilized in the extended dynamic window approach–based local trajectory planner to additionally evaluate the omnidirectional velocities of the robot. Based on the new trajectory planning objective that minimizes power consumption, energy-reduction autonomous navigation is proposed via the combinational cost objectives of low power consumption and high speed. Comprehensive experiments are performed in various autonomous navigation task scenarios, to validate the energy consumption model and to show the effectiveness of the proposed technique in minimizing the power consumption and reducing the energy consumption. It is observed that the technique effectively takes advantage of the Mecanum robot’s redundant maneuverability, can cope with any type of path and is able to fulfil online obstacle avoidance.
Highlights
The Ilon Mecanum wheel is one of the practical omnidirectional wheel designs utilized in the industry, and it has the advantage of high load capacity over other omnidirectional wheel designs.[1]
Based on the proposed extended dynamic window approach (DWA) that can minimize power consumption, energy-reduction autonomous navigation is proposed via the combinational cost objectives of low power consumption and high speed
The proposed technique can cope with any type of path and is able to fulfil online obstacle avoidance as the traditional DWA does
Summary
The Ilon Mecanum wheel is one of the practical omnidirectional wheel designs utilized in the industry, and it has the advantage of high load capacity over other omnidirectional wheel designs.[1]. A local trajectory planning method that both reduces overall energy consumption in the global sense and reactively avoids unexpected obstacles is essential to realize energy-efficient autonomous navigation. The modified Newton algorithm was used by Duleba and Sasiadek[7] to optimize the energy consumption in the nonholonomic motion planning These methods only apply to nonholonomic mobile robots and do not consider the online local energy-efficient trajectory planning problem
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