Performance based design optimization of an intrinsically compliant 6-dof parallel robot

Prashant K. Jamwal, Akim Kapsalyamov, Shahid Hussain, Mergen H. Ghayesh

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
3 Downloads (Pure)


Parallel robots are preferred over serial robots owing to their enhanced accuracy and rigidity which comes from their higher stiffness. However, there are applications both in industry and in healthcare where higher accuracy is required alongside high compliance (reduced stiffness). Accuracy and compliance being conflicting to each other are difficult to achieve simultaneously. To address this issue, an intrinsically compliant 6-dof parallel robot is proposed in this work. Kinematic and analytical modeling is performed for its conceptual design to obtain the Jacobian matrix and thereby map the joint and Cartesian spaces. Robot’s structure design is analyzed, and the wrench analysis is also carried out to estimate the link forces and stiffness. It is shown that by small changes in the proposed robot design; its compliance can be altered making it suitable for a range of applications. It is also shown mathematically that the robot design can be optimized to maintain higher accuracy together with higher compliance. To carry out design optimization, three important performance criteria, namely; global condition number (for higher accuracies), norm of link forces (to reduce actuator power requirement) and robot compliance (for response to an external wrench) are mathematically formulated. Later, a multi-criteria optimization is performed using an evolutionary algorithm to simultaneously optimize these performance criteria. From the final robot design selected, it is evident that a higher robot compliance with optimal condition number and link forces can be achieved.
Original languageEnglish
Pages (from-to)1237-1252
Number of pages16
JournalMechanics Based Design of Structures and Machines: an international journal
Issue number4
Publication statusPublished - 2022


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