Optimization of Energy Efficiency with a Predictive Dynamic Window Approach for Mobile Robot Navigation

Daniel Teso-Fz-Betoño (), Iñigo Aramendia, Jose Antonio Ramos-Hernanz, Daniel Caballero-Martin, Hicham Affou and Jose Manuel Lopez-Guede
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Daniel Teso-Fz-Betoño: Electrical Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), C/Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Iñigo Aramendia: Electrical Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), C/Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Jose Antonio Ramos-Hernanz: Electrical Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), C/Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Daniel Caballero-Martin: Department of System Engineering and Automation Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), C/Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Hicham Affou: Department of System Engineering and Automation Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), C/Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Jose Manuel Lopez-Guede: Department of System Engineering and Automation Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), C/Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain

Sustainability, 2025, vol. 17, issue 10, 1-18

Abstract: This study introduces an enhanced Predictive Dynamic Window Approach (P-DWA), developed as an offline trajectory planner for simulation-based analysis. The algorithm predicts nine candidate trajectories per iteration, evaluates their temporal and kinematic feasibility, and selects the top three based on energy efficiency. Results show an average reduction of approximately 9% in energy consumption compared to the traditional P-DWA, while maintaining efficient computational performance with average iteration times ranging from 15.6 ms to 18.5 ms. However, this gain in energy efficiency typically requires more iterations to complete a path, reflecting the algorithm’s more conservative motion strategy. The trade-off between energy savings and total simulation time underscores the value of this approach for testing sustainable navigation strategies. Overall, the proposed P-DWA provides a valuable tool for offline trajectory generation in autonomous mobile robotics, supporting energy-aware path planning under controlled simulation environments.

Keywords: DWA; P-DWA; mobile robots; energy efficiency; predictive controller (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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