The study presents a novel approach to tackle the issues of stability and safety in robotic manipulators. The researchers propose an obstacle-free deep reinforcement learning (DRL) trajectory planner integrated with…
Continue readingThis comprehensive review focuses on the development and recent advancements in fault-tolerant control schemes for robotic manipulators. The authors trace the historical evolution of these schemes and discuss the significant…
Continue readingThe article introduces a new online model-predictive trajectory planner for robotic manipulators, called BoundMPC. This planner allows robots to follow collision-free Cartesian reference paths in dynamic environments. The unique feature…
Continue readingThe paper presents BoundMPC, a novel online model-predictive trajectory planner for robotic manipulators. This planner allows the collision-free following of Cartesian reference paths in the end-effector’s position and orientation, including…
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