Nonholonomic mobile robots' trajectory tracking model predictive control: a survey

Nascimento, Tiago P.; Dórea, Carlos Eduardo Trabuco; Gonçalves, Luiz Marcos Garcia

doi:10.1017/s0263574717000637

Cited by 83 publications

(32 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only a few works in literature uses outdoor WMRs with model predictive control that make this an open topic for researchers. In contrast, although indoor mobile robots are often studied, they are always in controllable environments [32].…”

Section: Discussionmentioning

confidence: 99%

A Review of Indoor Navigation Systems for a Bipedal Robot: Preliminary Results

Ortega-Palacios¹,

Guerrero-García²,

González-Calleros³

2019

RCS

View full text Add to dashboard Cite

The navigation process of human is quite different from robots. Semantic meaning is what we used to model the environment. One of the most important elements human uses to model the environment is the vision. The keyframes are the elements that human use to model the environment. Therefore, we do not need a specific coordinate to move around. We can only use vision to navigate to anywhere we want to go. To take advantage of the concept that human model the environment, we propose a method which imitates the mapping and navigation process of the human. We will use computer vision to do the mapping of the environment and wayfinding to navigate it. The environment model is created with data collected with an odometer that identifies the obstacles on the route of the robot. Our research proposal is to create an algorithm to avoid obstacles. In this paper, we focus on reviewing existing solutions to address our research problem which is a system mounted on a biped robot navigating indoor environments on its own, without having to use preset routines. We have reviewed existing literature and presented it to help future researchers who had the same goal and could be used as a starting point.

show abstract

Section: Discussionmentioning

confidence: 99%

A Review of Indoor Navigation Systems for a Bipedal Robot: Preliminary Results

Ortega-Palacios¹,

Guerrero-García²,

González-Calleros³

2019

RCS

View full text Add to dashboard Cite

show abstract

“…In 1994-1999, Gómez-Ortega et al proposed the NMPC controller and improved the real-time performance of the controller by using artificial neural networks or genetic algorithms [37][38][39][40][41]. After 2000, researchers have proposed more NMPC-based path tracking controllers [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. In these studies, Künhe et al [42], Oyelere et al [54], and Li et al [58] have compared NMPC and LMPC.…”

Section: Nmpcmentioning

confidence: 99%

Review and Comparison of Path Tracking Based on Model Predictive Control

et al. 2019

View full text Add to dashboard Cite

Recently, model predictive control (MPC) is increasingly applied to path tracking of mobile devices, such as mobile robots. The characteristics of these MPC-based controllers are not identical due to the different approaches taken during design. According to the differences in the prediction models, we believe that the existing MPC-based path tracking controllers can be divided into four categories. We named them linear model predictive control (LMPC), linear error model predictive control (LEMPC), nonlinear model predictive control (NMPC), and nonlinear error model predictive control (NEMPC). Subsequently, we built these four controllers for the same mobile robot and compared them. By comparison, we got some conclusions. The real-time performance of LMPC and LEMPC is good, but they are less robust to reference paths and positioning errors. NMPC performs well when the reference velocity is high and the radius of the reference path is small. It is also robust to positioning errors. However, the real-time performance of NMPC is slightly worse. NEMPC has many disadvantages. Like LMPC and LEMPC, it performs poorly when the reference velocity is high and the radius of the reference path is small. Its real-time performance is also not good enough.

show abstract

“…Nevertheless, a common approach in tracking control design is to apply a coordinate change as follows: 20,26 ⎡…”

Section: Open-loop Error Dynamicsmentioning

confidence: 99%

A Feedback Linearization-Based Motion Controller for a UWMR with Experimental Evaluations

2019

View full text Add to dashboard Cite

SummaryIn this paper, the feedback linearization approach is used to introduce a motion controller for unicycle-type wheeled mobile robots (UWMRs). The output function is defined as a linear combination of the error state. The novel scheme is firstly tested in numerical simulation and compared with its corresponding experimental result. Three controllers are taken from the literature and compared to the proposed approach by means of experiments. The gains of the experimentally tested controllers are selected to obtain identical energy consumption. The Optitrack commercial vision system and Pioneer P3-DX UWMR are used in real-time experimental tests. In addition, two sets of experimental results for different motion tasks are provided. The results show that the proposed controller presents the best tracking accuracy.

show abstract

Nonholonomic mobile robots' trajectory tracking model predictive control: a survey

Cited by 83 publications

References 100 publications

A Review of Indoor Navigation Systems for a Bipedal Robot: Preliminary Results

A Review of Indoor Navigation Systems for a Bipedal Robot: Preliminary Results

Review and Comparison of Path Tracking Based on Model Predictive Control

A Feedback Linearization-Based Motion Controller for a UWMR with Experimental Evaluations

Contact Info

Product

Resources

About