Application of interior-point methods to model predictive control
Journal of Optimization Theory and Applications
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Stability of Gauss–Radau Pseudospectral Approximations of the One-Dimensional Wave Equation
Journal of Scientific Computing
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
An efficient overloaded implementation of forward mode automatic differentiation in MATLAB
ACM Transactions on Mathematical Software (TOMS)
Automatica (Journal of IFAC)
Computational Optimization and Applications
Computational Optimization and Applications
A simulated annealing driven multi-start algorithm for bound constrained global optimization
Journal of Computational and Applied Mathematics
Firefly algorithm, stochastic test functions and design optimisation
International Journal of Bio-Inspired Computation
Fuzzy-GA-based trajectory planner for robot manipulators sharing a common workspace
IEEE Transactions on Robotics
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
B-spline-decomposition-based output tracking with preview for nonminimum-phase linear systems
Automatica (Journal of IFAC)
Hi-index | 22.15 |
Nonlinear constrained optimal trajectory planning is a challenging and fundamental area of research. This paper proposes bio-inspired fast-time approaches for this type of problems based on the inspiration drawn from the natural phenomenon known as the motion camouflage. Two algorithms are proposed: the virtual motion camouflage (VMC) subspace method and the sequential VMC method. As a hybrid approach, the sequential VMC method works through a two-step structure in each iteration. First, the VMC subspace method will solve for an optimal solution over a selected subspace. Second, an algorithm consisting of a linear programming and a line search will vary the subspace so that the next VMC subspace result will be guaranteed not to be worse than that of the current step. The dimension and time complexities of the algorithms will be analyzed, and the optimality of the solution via the sequential VMC approach will be studied. Through the VMC approaches, the state and control variables in the kinematics or dynamics models of vehicles in the selected subspace can be represented by a single degree-of-freedom vector, called the path control parameter vector. The reduction in dimension and no involvement of equality constraints will in practice make the convergence faster and easier, and a much smaller computational cost is expected. Two simulation examples, the Breakwell problem and a minimum time robot obstacle avoidance problem with different numbers of obstacles, are used to demonstrate the capabilities of the algorithms.