A multiple working mode approach to robotic hammering: Analysis and experiments

Romanyuk, Vladyslav; Soleymanpour, Sina; Liu, Guangjun

doi:10.1017/s0263574721000680

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…The general performance index of hammering is the speed of the hammer head in contact with the object or landmark of interest [23]. Research on hammering has been validated with single joint [24], [25], serial manipulators [26] and humanoid robots [27], [28]. Tsujita et al [27] presented a model of contact dynamic and proposed an evaluation of the impulsive force prediction model.…”

Section: Related Work a Impact-aware Hammering Task And Tool-use Taskmentioning

confidence: 99%

Imitation of Manipulation Skills Using Multiple Geometries

Gao

Zhao

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Humans use tools to complete impact-aware tasks such as hammering a nail or playing tennis. The postures adopted to use these tools can significantly influence the performance of these tasks, where the force or velocity of the hand holding a tool plays a crucial role. The underlying motion planning challenge consists of grabbing the tool in preparation for the use of this tool with an optimal body posture. Directional manipulability describes the dexterity of force and velocity in a joint configuration along a specific direction. In order to take directional manipulability and tool affordances into account, we apply an optimal control method combining iterative linear quadratic regulator (iLQR) with the alternating direction method of multipliers (ADMM). Our approach considers the notion of tool affordances to solve motion planning problems, by introducing a cost based on directional velocity manipulability. The proposed approach is applied to impact tasks in simulation and on a real 7-axis robot, specifically in a nail-hammering task with the assistance of a pilot hole. Our comparison study demonstrates the importance of maximizing directional manipulability in impact-aware tasks.

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Section: Related Work a Impact-aware Hammering Task And Tool-use Taskmentioning

confidence: 99%

Imitation of Manipulation Skills Using Multiple Geometries

Gao

Zhao

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…This is another example of multiple working mode (MWM) actuation approaches under development at our laboratory. Recently, we applied online, active-passive MWM actuation methods successfully in sophisticated manipulation tasks [1,2,3]. For our springassisted MRR (SA-MRR), the secondary mechanical operating mode features a synergistic integration of MWM online control with the joint brake and the embedded spring [4].…”

Section: Introductionmentioning

confidence: 99%

Energy-aware redundant actuation for safe spring-assisted modular and reconfigurable robot

Singh

Liu

2022

Robotica

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A spring-assisted modular and reconfigurable robot (SA-MRR) has been recently developed at our laboratory to reinforce its performance and enable safe and dexterous operation in human–robot environments. Multiple working mode (MWM) control enables each SA-MRR joint module to switch independently between working in a primary actuation mode and a secondary, spring-assisted mode that may improve task-specific energy performance measures and safety in a variety of manipulation tasks. The spring-assisted mode is characterized by synergy of spring and motor energy and may be summoned to offset motor energy demands or to safeguard a reconfigurable set of secondary joint limits. In this research work, two spring-assisted working mode strategies are proposed, and their characteristics have been investigated for SA-MRR actuation energy advantages while safe robot segregation in collaboration tasks is maintained. One MWM strategy has been designed to safeguard task-specific joint limits and is able to decrease motor energy consumption in some tasks. Another MWM strategy has been designed for energy efficiency and was able to reduce motor energy per cycle by $ \text{72}$ % in a simulated manipulation task while maintaining spatial safety constraints. Numerical simulations have demonstrated the effectiveness of the proposed spring-assisted working mode strategies for energy-aware safe manipulation applications.

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