Collaborative system and multi robots based on pneumatic muscle actuator

Designing a multi-robot system provides numerous advantages for many applications, such as low cost, multi-tasking and more efficient group work. While the rigidity of the robots used in industrial and medical application increase the probability of risk of injury. Therefore, many researches are done to increase the safety factor for robot-human interaction, as a result, either the separated between the human and robot is suggested or the force shutdown to robot system is applied. These solutions might be useful for industrial applications, nonetheless it is not for medical and the application require the direct interaction between the human and machine. To overcome the rigidity problem, a soft pneumatic muscle actuator PMA is used in this thesis to design a fully soft robot arm.The performances and the behaviours of these actuators are tested to enhance the force formula for the contraction and the extension PMAs. General length formulas are proposed in terms of the initial length in addition to the structure-based formulas for the tensile force and length. Three different novel actuators are proposed together with their kinematics. These actuators include: the self-bending contraction actuator SBCA, the double-bend pneumatic muscle actuator DB-PMA and the circular pneumatic muscle actuator CPMA. The presented actuators are used with the simple contraction and extension actuators to design different novel structures of continuum arms and end effectors. Then an efficient control system is proposed by using a parallel structure of the neural network NN and proportional P controller (PNNP controller). The presented continuum arms formed a multiple robot system to perform several tasks under the PNNP controller.

Level dc:type.qualificationlevel

Doctoral (Level 8)

Year dc:date.issued

2026

Author dc:creator

• Al-Ibadi, A

Language dc:language

en

Identifier

oai:salford-repository.worktribe.com:1367948

Author Identifier

0000-0002-0779-8217

OAI identifier oai:identifier

oai:salford-repository.worktribe.com:1367948