Engineering of Space Exploration Robots: From Design to Operation in Hazardous Environments

Subject Areas : Robotics

MohammadAmin Alaei ¹ , MohammadReza Majma ²

1 - Department of Computer Engineering, Pardis Branch, Islamic Azad University, Tehran, Iran
2 -

Received: 2025-11-08 Accepted : 2025-11-10 Published : 2025-11-14

Keywords: Autonomous robots, Adaptive predictive control, Machine Learning, Moving target tracking, Multi-objective optimization,

Abstract :

This paper presents and evaluates a novel hybrid control framework for robots operating in dynamic environments. Addressing the challenge of maintaining reliable performance amid continuously changing target positions, moving obstacles, and dynamic uncertainties, the proposed approach combines adaptive predictive techniques with machine-learning–based components to enhance robustness and efficiency. We first develop an advanced dynamic model that captures variable inertia, nonlinear effects, and environmental disturbances. Building on this model, we implement a three-layer control architecture comprising: an adaptive trajectory-prediction module based on an extended Kalman filtering approach, a multi-objective evolutionary path-planning algorithm, and a robust adaptive controller for real-time execution. System stability is established through a Lyapunov-based theoretical analysis. Extensive experimental evaluations across diverse scenarios demonstrate that the proposed framework achieves a 30% improvement in target-tracking accuracy and a 20% reduction in energy consumption compared with conventional methods. The results indicate the approach is particularly well suited for time- and energy-constrained applications, including search-and-rescue robots, smart prosthetic systems, and space exploration rovers. Overall, the study offers a practical, theoretically grounded solution for improving robotic performance in uncertain, dynamic operational contexts.

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