Abstract—This study systematically evaluates the performance of four hybrid path-planning algorithm combinations formed in two global planners (A* and Dijkstra) with two local planners, the Dynamic Window Approach (DWA) and the Time Elastic Band (TEB). Four combinations of the hybrid planner were conducted by autonomous differential-drive robots navigating in simulated environments. The proposed study tested across three grid map resolutions (0.025 m, 0.10 m, and 0.15 m) to understand the impact of environmental granularity on various environmental settings with variation of the velocity range (0.1 m/s–1 m/s). A comprehensive quantitative evaluation framework was implemented to justify the performance of the planner incorporating some key metrics such as the Spatial Coefficient (SPC), Temporal Coefficient (TEC), Smoothness Coefficient (SMC), Success Rate (SR), Failure Rate (FR), path length, execution time and Goal Convergence (GC). The collected experimental data were evaluated through a normalization process with the execution of some statistical analyses, such as Analysis of Variance (ANOVA), T-test, and Bonferroni correction, which revealed differences between planner performance and the impact of grid resolutions. Results revealed that Dijkstra + TEB consistently outperformed the other combinations and demonstrated superior SPC, TEC, and SMC values, particularly in complex environments. Conversely, the DWA-based planners suffered from low success rates and parameter sensitivities. This work offers a systematic benchmarking framework for evaluating path-planning strategies and serves as a valuable guide for deploying reliable autonomous navigation in real-world scenarios.

Keywords—autonomous navigation, global-local planner integration, grid resolution, A* + Time Elastic Band (TEB), Dijkstra + TEB

Cite: Rubel Ahmed, "A Multi-metric Benchmarking Study of Hybrid Path-planning Algorithms under Different Grid Resolutions," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 4, pp. 362-378, 2026. doi: 10.18178/ijmerr.15.4.362-378

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).