Tahirovic A., Magnani G. Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning

Springer London Heidelberg New York Dordrecht, 2013. XI, 56 p. 20 illus., 17 illus. in color. — ISBN: 978-1-4471-5048-0, ISBN: 978-1-4471-5049-7 (eBook), DOI 10.1007/978-1-4471-5049-7 — (SpringerBriefs in Electrical and Computer Engineering. Control, Automation and Robotics).Provides the reader with a means of guaranteeing task completion for path execution
Can accomodate the characteristics of a range of complex vehicle models during motion planning
Brings the flexibility of model predictive control to robot path planning
Passivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehicle navigation in both indoor and outdoor environments. The brief also introduces analysis of the worst-case scenario that might occur during the task execution. Some of the questions answered in the text include:
how to use an MPC optimization framework for the mobile vehicle navigation approach;
how to guarantee safe task completion even in complex environments including obstacle avoidance and sideslip and rollover avoidance; and
what to expect in the worst-case scenario in which the roughness of the terrain leads the algorithm to generate the longest possible path to the goal.
The passivity-based MPC approach provides a framework in which a wide range of complex vehicles can be accommodated to obtain a safer and more realizable tool during the path-planning stage. During task execution, the optimization step is continuously repeated to take into account new local sensor measurements. These ongoing changes make the path generated rather robust in comparison with techniques that fix the entire path prior to task execution. In addition to researchers working in MPC, engineers interested in vehicle path planning for a number of purposes: rescued mission in hazardous environments; humanitarian demining; agriculture; and even planetary exploration, will find this SpringerBrief to be instructive and helpful.Content Level » Research
Keywords » Autonomous Vehicle Motion Planning - Mobile Robotics - Model Predictive Control -Passivity-based Control
Related subjects » Control Engineering - Mechanical Engineering - Robotics.Motivation
Motion Planning Literature
Passivity-Based Control Overview
Passivity-Based Model Predictive Control
Scope of the WorkPB/MPC Navigation PlannerPB/MPC Optimization Framework
Cost Function
Optimization Constraints
Optimization Techniques
Design of the PB/MPC Motion Planner
General Model
Energy-Shaping Using a Navigation Function
Energy Storage Function
Passivity
Zero State Observability
StabilityExamplesFlat Terrain
Unicycle Vehicle
Car-Like Mobile Vehicle with Slippage
Simulations
Rough Terrains
General Model of a Vehicle in Rough Terrain
Energy-Shaping Using a Navigation Function
Passivity-Based Stability
SimulationSome Limitations and Real-Time ImplementationThe Worst Case Scenarios on Rough Terrains
Unknown Rough Terrain with Obstacles
Completely Known Rough Terrain with Obstacles
Unknown Rough Terrain Without Obstacles
Real Time Implementation of an MPC Based Motion Planner
Simulation ResultsEditors’ Biography