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Journals / Gazi University Journal of Science Part A: Engineering and Innovation / 2026 / Volume: 13 Issue: 3

Energy-Efficient Trajectory Optimization for a 4-DOF Serial Manipulator Using Adaptive Dolphin-Eagle Algorithm with Joint-Aware Energy Weighting

Ahmet Saygın Öğülmüş(OSTIM TECHNICAL UNIVERSITY)0000-0001-6498-4318
Mehmet İrfan Gedik(OSTIM TECHNICAL UNIVERSITY)0000-0003-2836-2735
Fatih Baykal(OSTIM TECHNICAL UNIVERSITY)0000-0002-2842-584X
Burak Yenipınar(OSTIM TECHNICAL UNIVERSITY)0000-0002-5997-944X
Pages
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Publication Date
2026-09-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Unlike baseline methods that plateau mid-run, ADEA exhibits sustained convergence, offering a robust framework for energy-aware motion planning in industrial tasks.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Energy efficiency in industrial robotic manipulators is vital for reducing operational costs and carbon footprints.
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Key Findings & Data:This paper presents the Adaptive Dolphin-Eagle Algorithm (ADEA), a hybrid metaheuristic for energy-optimal trajectory planning of a 4-DOF serial manipulator.
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Conclusion & Impact:Unlike baseline methods that plateau mid-run, ADEA exhibits sustained convergence, offering a robust framework for energy-aware motion planning in industrial tasks.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Energy-Efficient Trajectory Planning · Bio-Inspired Optimization · Adaptive Dolphin-Eagle Algorithm

Abstract

Energy efficiency in industrial robotic manipulators is vital for reducing operational costs and carbon footprints. This paper presents the Adaptive Dolphin-Eagle Algorithm (ADEA), a hybrid metaheuristic for energy-optimal trajectory planning of a 4-DOF serial manipulator. ADEA combines dolphin echolocation-based global exploration with eagle Lévy-flight exploitation. It is augmented by Joint-Aware Energy Weighting (JAEW), a physics-informed mechanism that amplifies search steps for high-activation joints, embedding dynamic load structures into the optimization without altering the fitness function. Based on a CAD-accurate robot model under full Newton-Euler inverse dynamics, trajectories are parameterized using quintic polynomial segments through two intermediate waypoints to minimize mechanical energy. Comparative evaluations against Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and a JAEW-augmented Starfish Optimization (SFO) baseline across 30 independent runs demonstrate that ADEA achieves the lowest mean energy consumption. Significant performance gains are confirmed by Wilcoxon signed-rank tests (p < 0.05). Unlike baseline methods that plateau mid-run, ADEA exhibits sustained convergence, offering a robust framework for energy-aware motion planning in industrial tasks.

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Cite This Article

Öğülmüş, A. S., Gedik, M. İ., Baykal, F., Yenipınar, B. (2026). Energy-Efficient Trajectory Optimization for a 4-DOF Serial Manipulator Using Adaptive Dolphin-Eagle Algorithm with Joint-Aware Energy Weighting. Gazi University Journal of Science Part A: Engineering and Innovation, Volume: 13 Issue: 3. https://doi.org/10.54287/gujsa.1962818

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