Trajectory Planning and Control System for Hyper-Redundant Manipulator in Semi-Structured Environments

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dc.contributor.advisorAkmeliawati, Rini
dc.contributor.advisorGrainger, Steven
dc.contributor.advisorLu, Tien-Fu (De Facto Principal Supervisor)
dc.contributor.authorAl-Khulaidi, Rami Ali Ghaleb Ahmed
dc.contributor.schoolSchool of Electrical and Mechanical Engineering
dc.date.issued2025
dc.description.abstractThis thesis investigates the development of integrated trajectory planning and control systems for hyper-redundant manipulators operating in semi-structured environments. The research objectives include developing and optimising a hyper-redundant manipulator for semi-structured environments, developing and implementing an integrated trajectory planning and passivity-based control (PBC) system utilising the Port-Controlled Hamiltonian (PCH) framework, and developing a collision-free trajectory planning method using Non-Linear Model Predictive Control (NLMPC) combined with an artificial bee colony optimisation algorithm. These objectives are addressed through theoretical development, simulation-based validation, and integration of innovative strategies into manipulator design and control frameworks. The research begins with the structural optimisation of a hyper-redundant manipulator featuring rigid short links, designed to enhance dexterity and manipulability. This design proves particularly effective in confined semi-structured environments, such as agricultural greenhouses. Building upon this foundation, an integrated trajectory planning and control system is developed using the Port-Controlled Hamiltonian (PCH) framework. This system employs a novel passivity-based control approach to achieve precise trajectory tracking and improved joint stability in semi-structured environments. To address the challenge of collision-free navigation, the thesis presents a hybrid method combining Non-Linear Model Predictive Control (NLMPC) with artificial bee colony optimisation. This approach demonstrates robust performance in navigating both static and dynamic obstacles, while effectively avoiding self-collisions. Additionally, the research explores an emotion-driven reinforcement learning model, which integrates human-like emotions into the decision-making process. This novel framework significantly enhances manipulators’ adaptability in decision-making, enabling efficient navigation and real-time adjustments in semi-structured and unknown environments. By addressing these research objectives, this thesis makes significant contributions to the field of robotics, offering innovative solutions to the challenges faced by hyper-redundant manipulators in dynamic and semi-structured settings. The findings provide a foundation for advancing manipulator design, trajectory planning, and control strategies, with implications for a wide range of applications in robotics and automation.
dc.description.dissertationThesis (Ph.D.) -- University of Adelaide, School of Electrical and Mechanical Engineering, 2025en
dc.identifier.urihttps://hdl.handle.net/2440/146402
dc.language.isoen
dc.provenanceThis electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legalsen
dc.subjecthyper-redundant manipulators
dc.subjectsemi-structured environments
dc.subjecttrajectory planning
dc.subjectpassivity-based control
dc.subjectNLMPC
dc.subjecthuman learning behaviour
dc.subjectemotion-based reinforcement learning
dc.titleTrajectory Planning and Control System for Hyper-Redundant Manipulator in Semi-Structured Environments
dc.typeThesisen

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