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2013 | Book

Introduction Read chapter Read first chapter

Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning

Authors: Adnan Tahirovic, Gianantonio Magnani

Publisher: Springer London

Book Series : SpringerBriefs in Electrical and Computer Engineering

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik"

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About this book

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.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
The popularity of the research on unmanned ground vehicles has increased recently due to a variety of operations and environments. Planetary explorations, search and rescue missions in hazard areas, surveillance, humanitarian de-mining, as well as agriculture applications such as pruning vine and fruit trees, represent possible fields of using autonomous vehicles in natural environments. Planetary exploration allows for understanding the planet surface geology, its present and past climate conditions, and for discovering potential signs of other lives.
Adnan Tahirovic, Gianantonio Magnani
Chapter 2. PB/MPC Navigation Planner
Abstract
In this chapter, a rather straightforward procedure is presented to obtain navigation algorithms for a broad class of vehicle models, based on an adapted version of the passivity-based nonlinear MPC examined in [1]. The proposed PB/MPC approach for navigation planning can be seen as a generalization of the well-known DWA developed in [2–4]. Similar to the navigation based on the MPC/CLF [5], the PB/MPC optimization setup guarantees the task completion, which means the vehicle is being able to reach the goal position. However, whereas in the MPC/CLF navigation framework a control action that decreases the Lyapunov function has to be found in advance, which is rather difficult if not impossible for complex vehicle models, the PB/MPC navigation framework gives directly the control action as a consequence of the passivity-based control. Therefore, the PB/MPC can be easily adapted to a variety of vehicle and terrain models providing a straightforward procedure for the navigation of wide range of vehicles.
Adnan Tahirovic, Gianantonio Magnani
Chapter 3. Examples
Abstract
This chapter demonstrates the design procedure of the PB/MPC motion planning framework. The first two examples consider the vehicle models that might be used on flat terrains, a unicycle, and a car-like mobile vehicle. The third example covers a rather general model that can be used for rough terrains.
Adnan Tahirovic, Gianantonio Magnani
Chapter 4. Some Limitations and Real-Time Implementation
Abstract
This chapter gives an analysis of the worst possible case which the vehicle might experience during the task execution on rough terrains while using the PB/MPC motion planner. Additionally, we present a possible real-time implementation of an MPC-like motion planner using algorithms developed for optimal control problems.
Adnan Tahirovic, Gianantonio Magnani
Chapter 5. Conclusion
Abstract
The presented PB/MPC motion planning approach is based both on the energy-shaping technique using a navigation function obtained from the terrain configuration and on the passivity-based MPC concept. The planner can be seen as a generalized DWA planning technique. The PB/MPC algorithm is a straightforward procedure that can be easily adapted to the navigation for a broad class of vehicles and terrains. It guarantees task completion under the assumption that the vehicle model is known and its states are obtainable through measurement and estimation at the end of each optimization interval.
Adnan Tahirovic, Gianantonio Magnani
Backmatter
Metadata
Title
Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning
Authors
Adnan Tahirovic
Gianantonio Magnani
Copyright Year
2013
Publisher
Springer London
Electronic ISBN
978-1-4471-5049-7
Print ISBN
978-1-4471-5048-0
DOI
https://doi.org/10.1007/978-1-4471-5049-7