Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Continuous methods for motion planning
Continuous methods for motion planning
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
A fuzzy clustering neural networks for motion equations of synchro-drive robot
Expert Systems with Applications: An International Journal
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This study describes a generation of globally time optimal trajectories for a mobile robot in predefined environment. The primary task in the study is to apply Differential Evolution (DE) method for definition of globally time-optimal trajectories under environmental and dynamical constraints. The planned trajectories are composed of line segments and curve segments. The structures of the curve segments are determined by using only two parameters such as a turn angle θ and a translation velocity on the curve vt_start. All possible curve segments in parameters range θ ∈(0, π]°, vt_start [0,40] inch and at_turn ∈ [-at_max, at_max] inch/sec2 form a curve segments set. Then DE, is used to find time optimal trajectory from this set. Experimental results are given and the results are shown successfully.