The complexity of robot motion planning
The complexity of robot motion planning
Robot Motion Planning
Parallel Robots
An Interior Point Algorithm for Large-Scale Nonlinear Programming
SIAM Journal on Optimization
A globally convergent primal-dual interior-point filter method for nonlinear programming
Mathematical Programming: Series A and B
Line Search Filter Methods for Nonlinear Programming: Motivation and Global Convergence
SIAM Journal on Optimization
Mathematical Programming: Series A and B
Planning Algorithms
CHOMP: gradient optimization techniques for efficient motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
International Journal of Applied Mathematics and Computer Science
Motion Generation in the MRROC++ Robot Programming Framework
International Journal of Robotics Research
Modeling Mobility in Cooperative Ad Hoc Networks
Mobile Networks and Applications
Hi-index | 0.00 |
An application of advanced optimization techniques to solve the path planning problem for closed chain robot systems is proposed. The approach to path planning is formulated as a "quasi-dynamic" NonLinear Programming (NLP) problem with equality and inequality constraints in terms of the joint variables. The essence of the method is to find joint paths which satisfy the given constraints and minimize the proposed performance index. For numerical solution of the NLP problem, the IPOPT solver is used, which implements a nonlinear primal-dual interior-point method, one of the leading techniques for large-scale nonlinear optimization.