EEFOLLM - LLM-guided reward shaping for electric eel foraging optimization in robot path planning: Evaluation Design and Construct Validity
Keywords:
robotic path optimization, evaluation design and construct validity, evidence synthesis, reproducibility, research evaluationAbstract
Evaluation is persuasive only when the measured outcome corresponds to the construct claimed by the study and the comparison answers the stated research question. This structured evidence review evaluates "EEFOLLM: LLM-guided reward shaping for electric eel foraging optimization in robot path planning" alongside nine author-disjoint, topically matched publications in robotic path optimization. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through evaluation design and construct validity, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda aligns claims, outcomes, comparators, sampling, and uncertainty before any performance estimate is interpreted.
References
- Yuan, C., Zeng, J., Que, H., Liu, Q., Xiao, J., Wang, R., Jin, C., Chen, H., Du, X., Sun, T., Ai, Q., & Shao, W. (2026). EEFOLLM: LLM-guided reward shaping for electric eel foraging optimization in robot path planning. Journal of King Saud University Computer and Information Sciences, 38(6), 548. https://doi.org/10.1007/s44443-026-00955-5
- Arumugam, V., & Algumalai, V. (2024). Optimization and Design Parametric Analysis Mobile Robot Path Planning using Multi Objectives Reinforcement Learning Algorithm. . https://doi.org/10.22541/au.172793564.42790243/v1
- Chen, L., MartÃnez, D., Nakamura, A., & O'Connor, L. (2025). Multi-Robot Collaborative Path Planning and Control Optimization Based on Deep Reinforcement Learning. . https://doi.org/10.20944/preprints202511.1696.v1
- Cui, J. (2024). Optimization of Spinal Surgery Robot Path Planning Using Deep Reinforcement Learning. 2024 First International Conference on Software, Systems and Information Technology (SSITCON), 1-5. https://doi.org/10.1109/ssitcon62437.2024.10797118
- Gu, S. (2025). Intelligent Robot Path Planning Performance Optimization and Control Strategy Integrating Reinforcement Learning and AlphaEvolve. 2025 2nd International Conference on Electronic Circuits and Signaling Technologies (ICECST), 100-106. https://doi.org/10.1109/icecst66106.2025.11307633
- Igarashi, H. (2002). Path planning of a mobile robot by optimization and reinforcement learning. Artificial Life and Robotics, 6(1-2), 59-65. https://doi.org/10.1007/bf02481210
- Zhang, J., & Zhu, Z. (2025). Multi-Robot Collaborative Path Planning Algorithms: Optimization and Applications. Proceedings of the 3rd International Conference on Data Analysis and Machine Learning, 352-359. https://doi.org/10.5220/0014763900004818
- Gao, L., Wang, W., & Ke, D. (2026). Energy Optimization for Autonomous Mobile Robot Path Planning Based on Deep Reinforcement Learning. Computers, Materials & Continua, 86(1), 1-15. https://doi.org/10.32604/cmc.2025.068873
- Du, H. (2026). Research on Industrial Robot Path Planning Optimization Algorithm Based on IoT and Deep Reinforcement Learning. Journal of Advanced Manufacturing Systems, 1-21. https://doi.org/10.1142/s0219686728500175
- Zhang, H., Sun, L., Tan, W., Bao, S., He, X., & Chen, J. (2026). A substation robot path planning algorithm based on deep reinforcement learning enhanced by ant colony optimization. Frontiers in Robotics and AI, 12. https://doi.org/10.3389/frobt.2025.1759501
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