Human-like motion planning of robotic arms based on human arm motion patterns

Abstract

Robots with human-like appearances and structures are usually well accepted in the human–robot interaction. However, compared with human-like appearances and structures, the human-like motion plays a much more critical role in improving the efficiency and safety of the human–robot interaction. This paper develops a human-like motion planner based on human arm motion patterns (HAMPs) to fulfill the human–robot object handover tasks. First, a handover task is divided into two sub-tasks, that is, pick-up and delivery, and HAMPs are extracted for these two sub-tasks separately. The resulting HAMPs are analyzed, and a method is proposed to select HAMPs that can represent the characteristics of the human arm motion. Then the factors affecting the duration of the movement primitives are analyzed, and the relationship between the duration of the movement primitives and these factors is determined. Based on the selected HAMP and the computed duration of the movement primitives, a human-like motion planning framework is developed to generate the human-like motion for the robotic arms. Finally, this motion planner is verified by the human–robot handover experiments using a KUKA IIWA robot. It shows that the resulting trajectories can correctly reflect the relative relationship between the joints in the human arm motion and are very close to the recorded human arm trajectories. Furthermore, the proposed motion planning method is compared with the motion planning method based on minimum total potential energy. The results show that the proposed method can generate more human-like motion.

Based on human-like variable admittance control for human–robot collaborative motion

“…This paper selected the smoothness J of the motion trajectory and the average energy P ave as the motion performance assessment indexes to quantitatively assess the human-robot collaborative It can also be seen from Fig. 7 that the human-robot collaborative motion velocity curves based on these two admittance parameter regulators show "bell-shaped" distribution rule, which conforms to the characteristics of the minimum jerk model [39]. Therefore, the minimum jerk model was selected to represent the ideal motion velocity curve.…”

“…It can also be seen from Fig. 7 that the human–robot collaborative motion velocity curves based on these two admittance parameter regulators show “bell-shaped” distribution rule, which conforms to the characteristics of the minimum jerk model [39]. Therefore, the minimum jerk model was selected to represent the ideal motion velocity curve.…”

Humanoid Robot Motion Planning Approaches: a Survey

“…Zhao et al [77] pivot towards robots imitating human actions, which are undeniably useful in human-robot physical interactions. However, it is not just the appearance that counts; human-like movement is quintessential in boosting efficiency and ensuring safety.…”

“…In [4], the high-level controller makes real-time decisions influenced by complex inputs such as terrain maps. Zhao et al [77] focuses on robots that look human and move like one, particularly in handover tasks. By breaking down the handover into phases and studying motion patterns, the research ensures that the robot's actions closely emulate natural human movements in real-time scenarios.…”

“…• [4]: Demonstrates high efficiency and robustness in teaching robots environment-aware locomotion skills using a two-tiered deep reinforcement learning system. Its medium scalability suggests potential limitations in application breadth without further adaptation; • [60]: Exhibits high efficiency in training robotic arms for tasks requiring human-like dexterity, using motion capture data to learn from human arm movements; • [1]: Offers a unique approach to obstacle traversal with medium efficiency, highlighting challenges in scaling and adapting the method for various obstacles; • [61] and [77]: Both achieve high marks across efficiency, robustness, and adaptability, underscoring their success This evaluation provides insights into behavior cloning approaches' current state and potential in humanoid robotics. While the research exhibits significant advancements in mimicking human behavior, varying degrees of scalability highlight the need for ongoing efforts to generalize these methods across various tasks and environments.…”