Wheeled Mobile Robot Navigation Research Articles

Obstacle Avoidance and Path Planning of a Wheeled Mobile Robot Using Hybrid Algorithm

In the mobile robot workplace, the path planning problem is crucial. Robotic systems employ intelligence algorithms to plan the robot's path from one point to another. This paper proposes the fastest and optimal path planning of the wheeled mobile robot with collision avoidance to find the optimal route during wheeled mobile robot navigation from the start point to the target point. It is done using a modern meta-heuristic hybrid algorithm called IPSOGWO by combining Improved Particle Swarm Optimization (IPSO) with Grey Wolf Optimizer (GWO). The principal idea is based on boosting the ability to exploit in PSO with the exploration ability in GWO to the better-automated alignment between local and global search capabilities towards a targeted, optimized solution. The proposed hybrid algorithm tackles two objectives: the protection of the path and the length of the path. During, Simulation tests of the route planning by the hybrid algorithm are compared with individual results PSO, IPSO, and GWO concepts about the minimum length of the path, execution time, and the minimum number of iterations required to achieve the best route. This work's effective proposed navigation algorithm was evaluated in a MATLAB environment. The simulation results indicated that the developed algorithm reduced the average path length and the average computation time, less than PSO by (1%, 1.7%), less than GWO by (1%, 1.9%), and less than IPSO by (0.05%, 0.4%), respectively. Furthermore, the superiority of the proposed algorithm was proved through comparisons with other famous path planning algorithms with different static environments.

Turning Point and Free Segments Strategies for Navigation of Wheeled Mobile Robot

The basic idea of the developed work is to solve the problem of mobile robot navigation with obstacle avoidance and the trajectory tracking problem in simple and complex environments. The research contribution aims to develop a strategy of navigation based on the turning point and the free segments algorithms. Indeed, a turning point method is developed in order to solve the problem of navigation in a simple environment. Then, the free segments approach is applied in order to solve the problem of obstacle avoidance in a complex environment. The second part of this paper aims to solve the problem of trajectory tracking. For this reason, a sliding mode controller is proposed as a solution to control the stability of the mobile robot. Finally, some simulation results which are developed using Matlab software are given to prove the validity of the developed work.

Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

An intelligent approach for autonomous mobile robots path planning based on adaptive neuro-fuzzy inference system

This paper proposes an efficient path planning technique for the autonomous collision-free navigation of wheeled mobile robots with simple hardware based on an adaptive neuro-fuzzy inference system (ANFIS). The distance between the robot and obstacles is measured using three ultrasonic sensors that are installed on the left, front, and right of the robot. These distances from the sensors form the inputs to the ANFIS-utility function block that calculates an obstacle avoidance steering angle for the robot. The obstacle avoidance behavior of the robot is modeled under six scenarios of facing an obstacle. The instantaneous position of the robot and the target are available from Global Positioning System (GPS) modules. A simulation mobile robot in V-REP has been integrated into the ANFIS controller coded in MATLAB. The simulation results show that the proposed ANFIS-utility function-based path planning technique surpasses some of the related algorithms in terms of finding near-optimal paths.

Simpsons 1/3 base fuzzy neural network with passive controller of wheeled mobile robot system

The paper investigates Passivity of Simpson 1/3 rule fuzzy neural network controller by teleoperation-based wheeled mobile robot navigation and wheel slippage. To render the obstacle avoidance for wheeled mobile robot and ensuring incident-free navigation particularly unlimited workspace and surface slippage of coordination for master robot position and slave robot velocity, a passivity controller mode is employed. Effective control strategies are formulated to achieve system stability in which soft-computing methodology is accessed to bypass robot wheel slippage and skidding. Soft-computing based fuzzy neural network system is framed through Simpson 1/3 rule for reducing master/slave robot position error on behalf of unavoidable and acceptable forces, which is confirmed by teleoperation system. We concluded mathematically, the system stability which has been shown via its passivity of the fuzzy neural network controller. The forces which can be handled by the human operator are approximately equal to the forces applied by the environmental force and sensor predictor of slave robot whichever comes under the unlimited workspace. Simulation results are verified with the effect of the proposed controller. Index Terms: Autonomous wheeled mobile robot, Passivity, adaptive network fuzzy inference system, Simpson’s 1/3 rule, teleoperation system.

Safe and Optimum Navigation of Wheeled Mobile Robot using Grey Wolf Optimization Algorithm

This paper discusses our research in developing a track planner for a mobile robot using a swarm intelligence technique for optimal track planning in a short computational time to achieve better results in track planning. Through this technique, we proposed grey wolf optimization (GWO) for generating fastest and optimal path planning. this paper introduces an algorithm for rapid and global motion preparation for a mobile robot in a complex environment with static obstacles. the performing analysis for GWO algorithm was evaluated in two different maps. Finally, A comparative study was evaluated between the algorithm built and the other algorithm exist, the simulation results, which carried out using Matlab 2018a showed that the GWO algorithm made results for generating optimal path planning and efficiently in terms of path distance, execution-time.

Metric-Cauchy sequence for wheeled mobile robot navigation

In this paper, the path planning approach for the wheeled mobile robot has been presented using Metric-Cauchy Sequence for static environment. The Euclidean metric associated with the distance measurement and the Cauchy sequence optimizes the navigational decisions. This dual approach applies to study navigational challenges such as path complexity, path safety, path optimization, obstacle avoidance, map generation, and goal tracking. The developed navigational model has been tested in a simulation environment using MATLAB software, and real-time environment using the Khepera robot and in-house developed robot on a defined arena for single-robot and multi-robot system. The observations made from both the platforms are compared in terms of navigational time and path length, and obtained results are found to be in close proximity to each other. Furthermore, the developed controller is also compared with the other existing work for validating the performance.

Posture Stabilization of Wheeled Mobile Robot Based on Passivity-Based Robust Switching Control with Model Uncertainty Compensation

Thisstudy presents apassivity-based robust switching control for the posture stabilization of wheeled mobile robots (WMRs) with model uncertainty. Essentially, this proposed strategy is switching between (1) passivity-based robust control to lead the robot to the neighborhood of local minima with a finite time and (2) another robust control to perturb the w-rotational motion of the WMR before the v-kinetic energy of the WMR become meaningless, thereby, eventually converging to the desired posture. Thus, combining two switching control laws ensures the global convergence of (x,y)-navigation of WMRs from any initial position to desired set. Especially, the inter-switching time is intentionallyselected before the WMR completely loses its mobility, which ensures a strict decrease in (x,y)-navigation potential energy and a better global convergence rate. In addition, this control architecture also includes model uncertainty compensation, often neglected in practice, and analytical study of rotational perturbation was also conducted. The Lyapunov technique and energetic passivity wereutilized to derive this control law. Simulation results are presented to illustrate the effectiveness of the proposed technique. It wasfound from the results that the WMR wasquickly converged to the desired posture even under the presence of model uncertainty.

A New Mobile Robot Toolbox for Matlab

In this study, a wheeled mobile robot navigation toolbox for Matlab is presented. The toolbox includes algorithms for 3D map design, static and dynamic path planning, point stabilization, localizat...

Neural Based Autonomous Navigation of Wheeled Mobile Robots

Neural Based Autonomous Navigation of Wheeled Mobile Robots

Longitudinal and lateral slip control of autonomous wheeled mobile robot for trajectory tracking

This research formulates a path-following control problem subjected to wheel slippage and skid and solves it using a logic-based control scheme for a wheeled mobile robot (WMR). The novelty of the proposed scheme lies in its methodology that considers both longitudinal and lateral slip components. Based on the derived slip model, the controller for longitudinal motion slip has been synthesized. Various control parameters have been studied to investigate their effects on the performance of the controller resulting in selection of their optimum values. The designed controller for lateral slip or skid is based on the proposed side friction model and skid check condition. Considering a car-like WMR, simulation results demonstrate the effectiveness of the proposed control scheme. The robot successfully followed the desired circular trajectory in the presence of wheel slippage and skid. This research finds its potential in various applications involving WMR navigation and control.

Visual navigation of wheeled mobile robots using direct feedback of a geometric constraint

Many applications of wheeled mobile robots demand a good solution for the autonomous mobility problem, i.e., the navigation with large displacement. A promising approach to solve this problem is the following of a visual path extracted from a visual memory. In this paper, we propose an image-based control scheme for driving wheeled mobile robots along visual paths. Our approach is based on the feedback of information given by geometric constraints: the epipolar geometry or the trifocal tensor. The proposed control law only requires one measurement easily computed from the image data through the geometric constraint. The proposed approach has two main advantages: explicit pose parameters decomposition is not required and the rotational velocity is smooth or eventually piece-wise constant avoiding discontinuities that generally appear in previous works when the target image changes. The translational velocity is adapted as demanded for the path and the resultant motion is independent of this velocity. Furthermore, our approach is valid for all cameras with approximated central projection, including conventional, catadioptric and some fisheye cameras. Simulations and real-world experiments illustrate the validity of the proposal.

Exploiting wheel slips of mobile robots to improve navigation performance

Improving navigation performance of autonomous wheeled mobile robot (WMR) in a dynamic unstructured environment requires improved maneuverability. In such cases, the dynamics of wheel slip may violate the ideal no-slip kinematic constraints generally used to model nonholonomic WMR. In this paper, a new method is proposed to tackle the modeling inadequacy that arises when slip is neglected by including both longitudinal and lateral slip dynamics into the overall dynamics of the WMR. This new model of the WMR provides a realistic simulation environment that can be utilized to develop model-based controllers to improve WMR navigation. In this paper, a dynamic planner with a path-following controller is designed to allow the WMR to navigate efficiently by autonomously regulating the generated traction force due to wheel slip. Extensive simulation results demonstrate the importance of the proposed modeling technique to capture slip phenomenon and the efficacy of the presented control technique to exploit such slip for better navigation performance.

1P1-O07 自律型移動ロボットにおける障害物回避とナビゲーション(作業移動ロボット)

This paper presents sensor-based navigation of wheeled mobile robots in outdoor environment. In order to sense its surrounding environment and own location, the mobile robot is assumed to be equipped with a laser range sensor and GPS. A potential field method is utilized to create force fields around obstacles (repulsion force) and goal (attraction force). Look-ahead control is adopted to steer the mobile robot in which a reference point located in front of the robots is dynamically changed such that the mobile robots able to success fully maneuver among the obstacles and reach the goal. This approach was adopted to participate in Tsukuba Challenge 2010.

20707 車輪型移動ロボットの屋外環境におけるナビゲーションに関する研究(OS7 ロボティックス・メカトロニクス(5),オーガナイズドセッション)

20707 車輪型移動ロボットの屋外環境におけるナビゲーションに関する研究(OS7 ロボティックス・メカトロニクス(5),オーガナイズドセッション)

Equiangular navigation and guidance of a wheeled mobile robot based on range-only measurements

We consider the problems of a wheeled mobile robot navigation and guidance towards an unknown stationary or maneuvering target using range-only measurements. We propose and study several methods for navigation and guidance termed Equiangular Navigation Guidance (ENG) laws. We give mathematically rigorous analysis of the proposed guidance laws. The performance is confirmed with computer simulations and experiments with ActivMedia Pioneer 3-DX wheeled robots.

이동로봇의 장애물 회피를 위한 복소 포텐셜 항법의 개선

This paper deals with the enhancement of the complex potential navigation for wheeled mobile robots. The circle theorem from complex function theory is used to avoid an obstacle, and the enhancement to avoid multiple obstacles is proposed. The limit cycle navigation can be combined for robot to kick the ball to the intentioned direction. Avoiding step and superposing twin vortices can be applied to adjust the direction of robot's trajectory. The proposed method is verified through a set of simulation works, and the feasibilities for the enhancement of complex potential theory are successful.

Wheeled mobile robot navigation using proportional navigation

We present a method for wheeled mobile robot navigation based on the proportional navigation law. This method integrates the robot's kinematics equations and geometric rules. According to the control strategy, the robot's angular velocity is proportional to the rate of turn of the angle of the line of sight that joins the robot and the goal. We derive a relative kinematics system which models the navigation problem of the robot in polar coordinates. The kinematics model captures the robot path as a function of the control law parameters. It turns out that different paths are obtained for different control parameters. Since the control parameters are real, the number of possible paths is infinite. Results concerning the navigation using our control law are rigorously proven. An extensive simulation confirms our theoretical results.

Contour tracking of an unknown planar object by regulating force for mobile robot navigation

SUMMARYIn this paper, a novel contour-tracking control method of an unknown planar object by lateral force regulation for wheeled mobile robot navigation is presented. The robot is required to follow the contour of an unknown object toward the goal position. Based on mobile robot dynamic equations, a force-control algorithm is proposed to maintain constant contact with a planar object. Measured contact force from an object is used not only to regulate a contact force in a lateral direction, but also to control the orientation angle of the robot to avoid collision with an object. Simulation and experiment of contour-tracking tasks of a wheeled mobile robot are conducted. Experimental results show that the contact force is well-regulated, and the robot arrives at the goal position successfully.

신경망을 이용한 복도에서의 구륜이동로봇의 위치추정

본 논문에서는 비전 기반 구륜이동로봇이 복도에 설치된 조명을 표식으로 사용하여 복도를 주행하기 위해 필요한 벽면으로부터의 거리와 방향각을 신경망을 이용하여 추정하는 알고리즘에 대해 기술하였다. 복도의 천정에 설치된 조명은 구륜이동로봇의 위치에 따라 조명 배열선의 기울기가 변하며, 구륜이동로봇의 방향각에 따라 정의된 소멸점의 위치가 변한다는 특징을 이용하였다. 획득된 영상에서 조명은 크기가 제한되어 있으며, 모양이 원에 가깝다는 특징을 이용하여 단순한 알고리즘에 의해 추출하였다. 기지의 구륜이동로봇의 위치와 방향각에서 복도 영상을 획득하여 조명 배열선의 기울기와 소멸점의 위치를 계산하여 이들 사이의 관계를 확인하였다. 주행 중 구륜이동로봇의 위치와 방향각을 추정하기 위해 신경망을 구성하고, 획득된 데이터를 이용하여 역 전파 알고리즘(back propagation algorithm)에 의해 학습을 수행하였다. 구륜이동로봇의 제작하고, 학습결과를 이용하여 실제 복도 주행 실험을 수행하였다. This paper describes position estimation algorithm using neural network for the navigation of the vision-based Wheeled Mobile Robot (WMR) in a corridor with taking ceiling lamps as landmark. From images of a corridor the lamp's line on the ceiling in corridor has a specific slope to the lateral position of the WMR. The vanishing point produced by the lamp's line also has a specific position to the orientation of WMR. The ceiling lamps has a limited size and shape like a circle in image. Simple image processing algorithms are used to extract lamps from the corridor image. Then the lamp's line and vanishing point's position are defined and calculated at known position of WMR in a corridor To estimate the lateral position and orientation of WMR from an image, the relationship between the position of WMR and the features of ceiling lamps have to be defined. Data set between position of WMR and features of lamps are configured. Neural network are composed and teamed with data set. Back propagation algorithm(BPN) is used for learning. And it is applied in navigation of WMR in a corridor.