Towards a more controllable sensorised soft gripper: a
data-driven approach</p

Khaled Elgeneidy (1251450)

Robotic grippers have been constantly improving over the years to become more dextrous and adaptable in handing difficult objects with variations in their shape or uncertainty in their positioning. A challenge that remains difficult until now is the ability to handle delicate objects that can be easily considered as defective due to their interaction with the gripper, such as the case for food products or finely machined parts. An interesting emerging approach to tackle this challenge is to rethink the origin of the problem, which is the fact that all conventional grippers are made of hard and rigid components that can easily damage objects during grasping if not precisely controlled based on reliable sensory feedback. Hence, creating gripper fingers from soft materials makes them inherently safe and relaxes the need for sophisticated sensing and complex control. However, several open research challenges exist that are hindering the full utilisation of soft robotic components. In the context of soft grippers, relying primarily on the soft nature of the fingers to passively and gently adapt to its targets although highly desirable, consequently means that no sensory feedback is available to have better control over the grasping process or confirm its success.In this research, a low-cost soft gripper was developed based on the ribbed pneumatic bending actuators with embedded bend sensing, in order to investigate the potential for sensor-guided control of soft gripper fingers. [Continues.]</p

Towards a more controllable sensorised soft gripper: a data-driven approach

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