Reactive Trajectory Tracking for Mobile Robots based on Non Linear Model Predictive Control

Vougioukas, Stavros

doi:10.1109/robot.2007.363939

Cited by 37 publications

(20 citation statements)

References 14 publications

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“…Some of them are as follows: Vougioukas [26] presented a reactive path tracking controller based on nonlinear MPC, along with an iterative gradient descent algorithm for its real-time implementation. In the presence of obstacles, the controller deviates from the reference trajectory by incorporating obstacle-distance information from range sensors into the optimization problem.…”

Section: The Virtual Structure Strategy With Mpcmentioning

confidence: 99%

Coordinated Path Following for Mobile Robots Using a Virtual Structure Strategy with Model Predictive Control

Kanjanawanishkul

2014

Automatika

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In this paper, we propose a novel coordinated path following controller based on model predictive control (MPC) for mobile robots. The strategy is based on a virtual structure approach where the entire formation is considered as a rigid body and the control laws for a virtual leader vehicle and for actual follower robots are optimized by considering the dynamics of the virtual structure and the desired motion of each vehicle. Besides, we also fulfill time convergence for trajectory tracking by integrating an additional penalty term into our model predictive control scheme. However, the major concern in the use of model predictive control is whether such an open-loop control scheme can guarantee system stability. In this case, we apply the idea of a contractive constraint to guarantee the stability of our MPC framework. Although our approach is centralized, numerous simulation scenarios have been conducted to illustrate its effectiveness and its superior performance for a small group of mobile robots. Furthermore, we show that path following control can offer a number of advantages over its trajectory tracking counterpart. Koordinirano slije enje putanje za mobilne robote zasnovano na strategiji virtualne strukture i modelskom prediktivnom upravljanju. U ovomečlanku predlaže se novi algoritam upravljanja koordiniranim slije enjem putanje za mobilne robote zasnovan na modelskom prediktivnom upravljanju. Strategija se zasniva na pristupu s virtualnom strukturom gdje se cijela formacija robota smatra krutim tijelom, dok se za virtualno vodeće vozilo i za slijedeće robote optimiziraju zakoni upravljanja vodeći računa o dinamici virtualne strukture i željenom gibanju svakog od vozila. Tako er, algoritam ispunjava uvjet vremena konvergencije radi praćenja trajektorija na način da integrira dodataničlan unutar algoritma modelskog prediktivnog upravljanja. Me utim, koristeći modelsko prediktivno upravljanje postavlja se pitanje može li ovakav pristup u otvorenom upravljačkom krugu jamčiti stabilnost. Radi toga, primijenjuje se ideja sužavajućeg ograničenja radi jamčenja stabilnosti predloženog riješenja. Iako je predloženi pristup centraliziran, provedeni su brojni simulacijski eksperimenti kako bi se ilustrirala učinkovitost i superiorno vladanje na primjeru male grupe mobilnih robota. Nadalje, pokazuje se da upravljanje slije enjem putanje pruža brojne prednosti u usporedbi s praćenjem trajektorije.Ključne riječi: upravljanje koordiniranim slije enjem putanje, modelsko prediktivno upravljanje, mobilni roboti, virtualna struktura, upravljanje gibanjem robota

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Section: The Virtual Structure Strategy With Mpcmentioning

confidence: 99%

Coordinated Path Following for Mobile Robots Using a Virtual Structure Strategy with Model Predictive Control

Kanjanawanishkul

2014

Automatika

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“…Obstacle avoidance control methods for mobile robots have been studied widely. For example, the artificial potential field method (5) , the global dynamics window approach (6) , the trajectory generation algorithm using a Voronoi's diagram and a Bézier curve (7) and nonlinear model predictive control method (8) have been reported. Additionally, the obstacle avoidance control method considering the robot's shape has been proposed such as in Ref.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Obstacle Avoidance and Wheel Allocation Control of Mobile Robots Using Embedded CPU

Takahashi

Nonaka

2012

JSDD

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In this study, we propose a real-time model predictive control method for leg/wheel mobile robots which simultaneously achieves both obstacle avoidance and wheel allocation at a flexible position. The proposed method generates both obstacle avoidance path and dynamical wheel positions, and controls the heading angle depending on the slope of the predicted path so that the robot can keep a good balance between stability and mobility in narrow and complex spaces like indoor environments. Moreover, we reduce the computational effort of the algorithm by deleting usage of mathematical function in the repetitive numerical computation. Thus the proposed real-time optimization method can be applied to low speed on-board CPUs used in commerciallyproduced vehicles. We conducted experiments to verify efficacy and feasibility of the real-time implementation of the proposed method. We used a leg/wheel mobile robot which is equipped with two laser range finders to detect obstacles and an embedded CPU whose clock speed is only 80 MHz. Experiments indicate that the proposed method achieves improved obstacle avoidance comparing with the previous method in the sense that it generates an avoidance path with balanced allocation of right and left side wheels.

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“…Recently, the performance of the computers have been improved and fast numerical algorithms for MPC have been proposed [14]- [16] to apply MPC to the practical problems. Thus, the real-time implementation of MPC becomes feasible and is applied to the robot controls [9], [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

Model predictive obstacle avoidance control for leg/wheel mobile robots with optimized articulated leg configuration

Takahashi

Nonaka

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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In this study, we propose a model predictive obstacle avoidance control for leg/wheel mobile robots with optimized articulated leg configuration. Using the proposed method, the robot can avoid obstacles and keep stability in narrow and complex spaces like indoor environments while the robot allocates wheels adapted to the surrounding environment. We optimize the angular velocities of joints and wheel driving speeds using model predictive control. Although our past related researches optimize the joint angles and wheel speeds separately, the proposed method optimizes these simultaneously. Thus, enhanced optimality of the entire robot system is expected to be achieved. We conduct numerical simulations of an actual mobile robot to verify efficacy and feasibility of the proposed method, which demonstrate that the proposed method is effective to avoid obstacles.

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Reactive Trajectory Tracking for Mobile Robots based on Non Linear Model Predictive Control

Cited by 37 publications

References 14 publications

Coordinated Path Following for Mobile Robots Using a Virtual Structure Strategy with Model Predictive Control

Coordinated Path Following for Mobile Robots Using a Virtual Structure Strategy with Model Predictive Control

Model Predictive Obstacle Avoidance and Wheel Allocation Control of Mobile Robots Using Embedded CPU

Model predictive obstacle avoidance control for leg/wheel mobile robots with optimized articulated leg configuration

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