RUL-Aware RRT*: Degradation-Balanced Motion Planning for Robotic Manipulators

Abstract

Industrial robotic manipulators operating over long durations may suffer from uneven joint degradation, causing the weakest actuator to fail prematurely and limiting the lifetime of the entire system. Although Prognostics and Health Management (PHM) techniques can estimate component health and remaining useful life (RUL), such information is rarely incorporated into online motion planning. To address this gap, this paper proposes an RUL-aware RRT* method that integrates joint RUL information into the trajectory generation process through a health-aware cost formulation and an adaptive joint weighting mechanism. A deterministic cumulative joint-usage surrogate is used to represent planning-level degradation in simulation. The method is evaluated in a Local Degradation Scenario, where one joint starts from a severely weakened condition and acts as the dominant lifetime bottleneck. Results show that the proposed method reduces the motion assigned to the weak joint, delays the first system failure, and maintains more balanced degradation than the baseline RRT*. These findings demonstrate that integrating prognostic information into motion planning provides a practical pathway toward health-aware robotic decision-making.