Nonlinear Model Predictive Control for Omnidirectional Robot Motion Planning and Tracking With Avoidance of Moving Obstacles

Teatro, Timothy A. V.; Eklund, Johan; Milman, Ruth

doi:10.1109/cjece.2014.2328973

Cited by 30 publications

(18 citation statements)

References 12 publications

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“…GA and its modified versions are frequently implemented to find the shortest path for mobile robot path planning in different environments [17], while path planning using neural networks was developed in [18]. Integrating a path planning algorithm with the motion controllers of mobile robots was achieved in [19][20][21][22], where several different motion control strategies were employed, including fuzzy logic controls, adaptive neuro-fuzzy inference systems, and model predictive controls. The Wind Driven Optimization (WDO) and Invasive Weed Optimization (IWO) algorithms were used to tune the parameters of the fuzzy logic controller and adaptive neuro-fuzzy inference systems in [20], [21], respectively, while ACO and PSO were used in the tuning of the fuzzy logic controller presented by [23].…”

Section: Related Workmentioning

confidence: 99%

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Ajeil

Ibraheem

Sahib

et al. 2020

Applied Soft Computing

164

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The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments. The problem is solved by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The proposed path planning algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed algorithm consists of three modules. The first module forms an optimized path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimizes distance and follows path smoothness criteria. The second module detects any infeasible points generated by the proposed PSO-MFB Algorithm by a novel Local Search (LS) algorithm integrated with the PSO-MFB algorithm to be converted into feasible solutions. The third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. The simulation results indicate that this method generates an optimal feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Moreover, compared to recent path planning techniques, simulation results show that the proposed PSO-MFB algorithm is highly competitive in terms of path optimality.

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Section: Related Workmentioning

confidence: 99%

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Ajeil

Ibraheem

Sahib

et al. 2020

Applied Soft Computing

164

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

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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.

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“…The works [6,7,12,22,24,28,31,32,43] concern two-wheeled robots, and work [6] concerns inverted pendulum robot. In turn, works [2,5,10,20,21,29,38] describe three-wheeled omnidirectional robots, whereas [1,4,18,35], three-wheeled robots with two fixed driven wheels and castor. Works [13,17,27,30] cover research involving fourwheeled robots of car-like steering type, the work [11] is concerned with differentially driven four-wheeled robot with two wheels driven and two castors, whereas works [9,45,46] describe four-wheeled omnidirectional robot.…”

Section: Wheeled Mobile Robots and Slippagementioning

confidence: 99%

“…Often model linearization is used in the neighborhood of the operating point, which ultimately boils down to use of algorithms relying on linear model. In contrast, the NMPC uses advanced methods of objective function optimization, which was discussed in the works:[3,7,10,19,28,29,31,34,38,44].…”

mentioning

confidence: 99%

State of the Art in Predictive Control of Wheeled Mobile Robots

Harasim

Trojnacki

2016

JAMRIS

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The paper is concerned with the problem of tracking control of wheeled mobile robots (WMRs) using predictive control systems. Various kinematic structures of WMRs important from the point of view of motion control are discussed. A hierarchical approach to the problem of motion control of this kind of robots is presented. The problems of trajectory tracking and path following control of WMRs are briefly discussed. The methods of predictive control of WMRs are described in detail and the following aspects relevant to predictive control are considered: kinematic structures of robots, slip of wheels and its compensation, assumed constraints, methods of optimization of the objective function, problems of model nonlinearity, linearization and discretization, stability of the control system and use of the state observers.

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Nonlinear Model Predictive Control for Omnidirectional Robot Motion Planning and Tracking With Avoidance of Moving Obstacles

Cited by 30 publications

References 12 publications

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Review and Comparison of Path Tracking Based on Model Predictive Control

State of the Art in Predictive Control of Wheeled Mobile Robots

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