AbstractThis thesis presents, critical compares and develops new methods to control and optimise locomotion for a range of systems. Jumping and running locomotion skills are examined in detail, and intelligent methods are discussed and adapted to optimise for correct form of motion, and performance outcomes. Existing control techniques are summarised and compared, including traditional analytical methods, central pattern generator oscillator systems, pattern generating neural networks, rule based systems and other specialist methods.
Optimisation and learning methods presented in the literature are also summarised, and while several methods exist, modern global search methods were limited to genetic algorithms. This thesis applies particle swarm optimisation and quantum inspired evolutionary algorithms to vertical jump and walking optimisation, comparing their performance to a genetic algorithm. Improvements were developed for both binary and real-value variants of quantum inspired evolutionary algorithms, to benefit performance on the real-value problems involved in locomotion control. These improvements consisted of modifications to their rotation gate operators, including a novel scheme to reduce premature convergence in the binary methods, based on limiting the range of less significant bits.
Methods were applied in simulated environments, although they can be adapted to real world robotic control, or for reference in optimising motion in humans. A discussion of the susceptibility of simulation runs to poor physical modelling was presented, as this was a significant problem during research. Results were generally mixed, to the extent that all tested methods may be usefully examined more in future work. The central pattern generators tested generated successful patterns more often than are current neural network, and the results of the optimisation algorithms did not show sufficient advantage of one over the others.
|Date of Award||Nov 2015|
|Supervisor||Ivan Jordanov (Supervisor), Mo Adda (Supervisor) & Mohamed Medhat Gaber (Supervisor)|