A comprehensive study of a predictive fatigue aware adaptive control framework for mobile robots using low cost sensing

Mobile robots deployed in real-world environments are exposed to variable terrain conditions, payload changes, and repeated acceleration and deceleration cycles that induce fluctuating mechanical loads. These operating conditions lead to cumulative mechanical fatigue, which is a major cause of actuator and structural component failure in robotic systems. Conventional robot controllers primarily focus on trajectory tracking and stability and do not explicitly account for mechanical health or fatigue accumulation. This paper proposes a predictive fatigue-aware adaptive control framework for mobile robots that integrates low-cost load estimation, real-time fatigue accumulation modeling, lightweight prediction, and supervisory adaptive motion control. The proposed approach estimates mechanical load using motor current, inertial measurements, and encoder data, and computes a fatigue index that serves as a proxy indicator of cumulative operational mechanical stress (not a direct measurement of material fatigue damage). A prediction module anticipates future load peaks and enables proactive adaptation of motion constraints, such as velocity and acceleration limits. The framework is validated through extensive simulation and experimental implementation on a differential-drive mobile robot platform. Results demonstrate a significant reduction in peak motor current and vibration levels, indicating reduced mechanical stress. This mechanical protection is achieved at the cost of increased task completion time—rising from approximately 45–55 s under rough terrain and from 60 to 74 s under high-payload conditions—and a modest increase in trajectory tracking RMSE of 6–13 mm across all tested conditions. This explicit trade-off between mechanical protection and task performance is a direct consequence of the imposed velocity and acceleration limits and is discussed in full in the results section. Experimental validation under rough-terrain and high-payload conditions demonstrated that the proposed predictive fatigue-aware adaptive controller reduced peak motor current by approximately 35%, decreased vibration RMS by 44%, and lowered the final fatigue index (a proxy indicator of operational stress accumulation) by 47% compared with the baseline controller under rough-terrain conditions; averaged across all three evaluated operating conditions (smooth terrain, rough terrain, and high payload), the reductions were approximately 26%, 34%, and 41%, respectively. Trajectory tracking RMSE increased modestly (by 6–13 mm in absolute terms) as a consequence of the deliberately imposed velocity and acceleration limits, while stable real-time operation was maintained on the ESP32 embedded platform. The proposed framework provides a practical and low-cost solution for health-aware robotic control and can be integrated as a supervisory layer in existing robotic systems.