A decomposition approach to multi-vehicle cooperative control
Robotics and Autonomous Systems
Robust tracking control of mobile robot formation with obstacle avoidance
Journal of Control Science and Engineering
Proceedings of the 1st international conference on Robot communication and coordination
CHOMP: gradient optimization techniques for efficient motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A new LP-based obstacle-avoided model in path planning problem
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Game-theoretic learning algorithm for a spatial coverage problem
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Modeling and path planning of the city-climber robot part I: dynamic modeling
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Resource-driven mission-phasing techniques for constrained agents in stochastic environments
Journal of Artificial Intelligence Research
Path Planning for UAVs Under Communication Constraints Using SPLAT! and MILP
Journal of Intelligent and Robotic Systems
Safe distributed motion coordination for second-order systems with different planning cycles
International Journal of Robotics Research
Robotics and Autonomous Systems
Journal of Intelligent and Robotic Systems
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Mixed-integer linear programming (MILP) is a powerful tool for planning and control problems because of its modeling capability and the availability of good solvers. However, for large models, MILP methods suffer computationally. In this paper, we present iterative MILP algorithms that address this issue. We consider trajectory-generation problems with obstacle-avoidance requirements and minimum-time trajectory-generation problems. These problems involve vehicles that are described by mixed logical dynamical equations, a form of hybrid system. The algorithms use fewer binary variables than standard MILP methods, and require less computational effort.