Development of a Lightweight Two-Dimensional Simulator for Behavioural Analysis Under Environmental Stimulus Conditions
Authors
Fakulti Kecerdasan Buatan dan Keselamatan Siber, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Muhammad Noorazlan Shah Zainudin
Fakulti Kecerdasan Buatan dan Keselamatan Siber, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Fakulti Teknologi Dan Kejuruteraan Elektronik dan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Fakulti Sains Komputer dan Teknologi Maklumat, Universiti Putra Malaysia, 43400, Selangor (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2026.100500389
Subject Category: Physics
Volume/Issue: 10/5 | Page No: 5829-5840
Publication Timeline
Submitted: 2026-05-08
Accepted: 2026-05-13
Published: 2026-06-02
Abstract
Simulation platforms are widely used in behavioural and environmental interaction studies due to their ability to provide flexible, low-cost, and repeatable experimental environments. This paper presents the development of a lightweight two-dimensional behavioural simulator designed for movement visualization and stimulus-based interaction analysis within a simplified virtual environment. The simulator consists of several modular components including environment modelling, behavioural network connectivity, locomotion control, environmental sensing, and real-time visualization modules. In addition, the simulator supports configurable simulation environments through a simplified file-based configuration structure. Several simulations were conducted under both stimulus and non-stimulus environmental conditions to evaluate the functionality and behavioural capabilities of the simulator. The results demonstrated that the simulator is capable of producing reproducible as well as stochastic movement behaviours depending on the simulation configuration. The simulator also successfully represented exploratory movement patterns and adaptive directional movement toward environmental stimulus regions. The proposed simulator provides a flexible platform for behavioural visualization and environmental interaction analysis within a lightweight two-dimensional environment.
Keywords
Behavioural Simulation, Two-Dimensional Simulator, Environmental Interaction
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References
1. J. Crewe, A. Humnabadkar, Y. Liu, A. Ahmed, and A. Behera, "SLAV-Sim: A Framework for Self-Learning Autonomous Vehicle Simulation," Sensors, vol. 23, no. 20, p. 8649, Oct. 2023. [Google Scholar] [Crossref]
2. O. A. Dambri et al., "Design and Implementation of a Simulation Framework for a Bio–Neural Dust System," Modelling, vol. 6, no. 1, p. 8, Jan. 2025. [Google Scholar] [Crossref]
3. A. López-Incera, K. Ried, T. Müller, and H. J. Briegel, "Development of swarm behavior in artificial learning agents that adapt to different foraging environments," PLOS ONE, vol. 15, no. 12, p. e0243628, Dec. 2020. [Google Scholar] [Crossref]
4. H.-T. Dang, B. Gaudou, and N. Verstaevel, "HyPedSim: A Multi-Level Crowd-Simulation Framework—Methodology, Calibration, and Validation," Sensors, vol. 24, no. 5, p. 1639, Feb. 2024. [Google Scholar] [Crossref]
5. Z. Talebpour and A. Martinoli, "Adaptive Risk-Based Replanning For Human-Aware Multi-Robot Task Allocation With Local Perception," IEEE Robot. Autom. Lett., vol. 4, no. 4, pp. 3790-3797, Oct. 2019. [Google Scholar] [Crossref]
6. A. Yildiz et al., "Experience Filter: Using Past Experiences on Unseen Tasks or Environments," in Proc. IEEE Intell. Vehicles Symp. (IV), Anchorage, AK, USA, 2023, pp. 1-7. [Google Scholar] [Crossref]
7. A. Sadhwani et al., "Fleet2D: A Fast and Light Simulator for Home Robotics," in Proc. 7th IEEE Int. Conf. Robot. Comput. (IRC), Naples, Italy, 2023, pp. 102-109. [Google Scholar] [Crossref]
8. H. Zeng, L. Kästner, and J. Lambrecht, "Efficient 2D Simulators for Deep-Reinforcement-Learning-based Training of Navigation Approaches," in Proc. 20th Int. Conf. Ubiquitous Robots (UR), Honolulu, HI, USA, 2023, pp. 275-280. [Google Scholar] [Crossref]
9. M. Holen, K. M. Knausgård, and M. Goodwin, "Development of a Simulator for Prototyping Reinforcement Learning-Based Autonomous Cars," Informatics, vol. 9, no. 2, p. 33, Apr. 2022. [Google Scholar] [Crossref]
10. M. Sasaki, J. Muguro, F. Kitano, W. Njeri, and K. Matsushita, "Sim–Real Mapping of an Image-Based Robot Arm Controller Using Deep Reinforcement Learning," Appl. Sci., vol. 12, no. 20, p. 10277, Oct. 2022. [Google Scholar] [Crossref]
11. J. Xu, E. Huang, L. Hsieh, R. S. Lee, T. Jia, and C. Chen, "Simulation optimization in the era of Industrial 4.0 and the Industrial Internet," J. Simulation, vol. 10, no. 4, pp. 310-320, Nov. 2016. [Google Scholar] [Crossref]
12. E. Rohmer, S. P. N. Singh, and M. Freese, "V-REP: A versatile and scalable robot simulation framework," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Tokyo, Japan, 2013, pp. 1321-1326. [Google Scholar] [Crossref]
13. M. Savva et al., "Habitat: A Platform for Embodied AI Research," in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Seoul, Korea (South), 2019, pp. 9339-9347. [Google Scholar] [Crossref]
14. N. Koenig and A. Howard, "Design and use paradigms for Gazebo, an open-source multi-robot simulator," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Sendai, Japan, 2004, pp. 2149-2154. [Google Scholar] [Crossref]
15. O. Michel, "Cyberbotics Ltd. Webots™: professional mobile robot simulation," Int. J. Adv. Robot. Syst., vol. 1, no. 1, p. 5, 2004. [Google Scholar] [Crossref]
16. S. Garnier, J. Gautrais, and G. Theraulaz, "The biological principles of swarm intelligence," Swarm Intell., vol. 1, pp. 3-31, 2007. [Google Scholar] [Crossref]
17. E. Tuci, M. Alkilabi, and O. Akanyeti, "Cooperative object transport in multi-robot systems: A review of the state-of-the-art," Front. Robot. AI, vol. 5, p. 59, 2018. [Google Scholar] [Crossref]
18. J. Pitonakova, R. Crowder, and S. Bullock, "Feature and performance comparison of the V-REP, ARGoS and Gazebo simulators," Robot. Auton. Syst., vol. 105, pp. 43-57, 2018. [Google Scholar] [Crossref]
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