Model-Based Development of QoS-Aware Reconfigurable Autonomous Robotic Systems

2018 ◽  
Author(s):  
Davide Brugali ◽  
Rafael Capilla ◽  
Raffaela Mirandola ◽  
Catia Trubiani
Keyword(s):  
Robotic Systems ◽  
Model Based ◽  
Autonomous Robotic Systems

Backlash Robotic Systems Compensation by Inverse Model-Based PID Control

2019 ◽  
Author(s):  
Fernando da Fonseca Schneider ◽  
Cesar Bastos da Silva ◽  
Paulo Jefferson Dias de Oliveira Evald ◽  
Rodrigo Sousa e Silva ◽  
Rodrigo Zelir Azzolin
Keyword(s):  
Pid Control ◽  
Inverse Model ◽  
Robotic Systems ◽  
Model Based

Hand closure model for planning top grasps with soft robotic hands

2020 ◽  
Vol 39 (14) ◽  
pp. 1706-1723
Author(s):  
Maria Pozzi ◽  
Sara Marullo ◽  
Gionata Salvietti ◽  
Joao Bimbo ◽  
Monica Malvezzi ◽  
...  
Keyword(s):  
Robotic Systems ◽  
Closure Model ◽  
Robotic Hands ◽  
Grasp Planning ◽  
Model Based ◽  
Major Trend ◽  
Different Shapes ◽  
Planning Strategies ◽  
Experimental Trials ◽  
Soft Manipulation

Automating the act of grasping is one of the most compelling challenges in robotics. In recent times, a major trend has gained the attention of the robotic grasping community: soft manipulation. Along with the design of intrinsically soft robotic hands, it is important to devise grasp planning strategies that can take into account the hand characteristics, but are general enough to be applied to different robotic systems. In this article, we investigate how to perform top grasps with soft hands according to a model-based approach, using both power and precision grasps. The so-called closure signature (CS) is used to model closure motions of soft hands by associating to them a preferred grasping direction. This direction can be aligned to a suitable direction over the object to achieve successful top grasps. The CS-alignment is here combined with a recently developed AI-driven grasp planner for rigid grippers that is adjusted and used to retrieve an estimate of the optimal grasp to be performed on the object. The resulting grasp planner is tested with multiple experimental trials with two different robotic hands. A wide set of objects with different shapes was grasped successfully.

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