Collision Avoidance of Multi Modal Moving Objects for Mobile Robot Using Hybrid Velocity Obstacles

Fuad, Muhammad; Agustinah, Trihastuti; Purwanto, Djoko

doi:10.22266/ijies2020.0630.37

Cited by 11 publications

(14 citation statements)

References 14 publications

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“…Holonomic drive and non-holonomic drive [5] refer to the relationship between the controllable degree of freedom and total degrees of freedom of a robot. The controllable degree of freedom is specified.…”

Section: Background and Related Workmentioning

confidence: 99%

“…By simplifying assumption based on the nonholonomic constraint that each wheel of the spherical magnetic robot can roll without slipping [5,16]. Kinematic model is described in the state space of transition equations as [5] ̇=…”

Section: Inner Drive Unitmentioning

confidence: 99%

“…By following the unicycle model [5], the differential drive of spherical magnetic robot is described a model followed the inputs from the speed of right wheel and of the left wheel as shown in Fig. 3 as where is the radius of wheel and is the wheel base of robot.…”

Section: Inner Drive Unitmentioning

confidence: 99%

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Differential Drive Analysis of Spherical Magnetic Robot Using Multi-Single Board Computer

Prongnuch¹,

Sitjongsataporn²

2021

IJIES

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This paper presents a spherical magnetic robot named 'CE-R1' propelled by an inner drive unit and a magnetic balancing unit rolling without slipping on a plane. A spherical magnetic robot 'CE-R1' consists of two parts as a domed head and a ball-shaped body with contactless and controlled via Bluetooth. A non-holonomic model and wheel-drive locomotion are interested in a spherical mobile robot. Sphere movement is controlled by a differential drive from the inner drive unit inside the sphere. Firstly, the magnetic interaction in a magnetic balancing unit is applied to the connection of a domed head and a ball-shaped body with contactless. Secondly, a differential drive of proposed spherical magnetic robot 'CE-R1' with an inner drive unit inside is derived and deployed with the multi-single computer board controller. Hardware of prototype 'CE-R1' is designed including the multi-single board computer for wireless charger, voice recognition system and controlling the 'CE-R1'. The explicit experiments of proposed 'CE-R1' spherical magnetic robot are tested as a faculty-assistive guide robot controlled through application at the faculty lobby area in the Suan Sunandha Rajabhat University. This work proposed here is a step towards the overall goal of an autonomous spherical mobile robot.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Inner Drive Unitmentioning

confidence: 99%

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Differential Drive Analysis of Spherical Magnetic Robot Using Multi-Single Board Computer

Prongnuch¹,

Sitjongsataporn²

2021

IJIES

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“…Reciprocal Velocity Obstacles (RVO) was used to avoid collisions between agents in crowd navigation in research [8]. To help mobile robots that navigate close to humans by avoiding multi-modality moving objects, Hybrid Velocity Obstacles (HVO) was proposed in [9]. Although this algorithm can maintain human safety around the robot, additional capabilities are still needed to maintain human comfort so that the robot can operate in the human environment properly.…”

Section: Navigation Can Maintain Both Of These Factors With the Support Of Collision-avoidancementioning

confidence: 99%

Evading of Pedestrian Pursuer and Avoiding Obstacles Using Path-Velocity Planner

et al. 2021

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The ability to navigate mobile robot from initial to target position while maintaining the safety and comfort of pedestrian have to be supported by a collision-avoidance system. In the case of pursuit-evasion when a pedestrian pursues a robot, navigation must be able to evade capture from the pursuer. This paper aims to improve the collision-avoidance method based on Hybrid Velocity Obstacles (HVO) concerning the pursuer. This paper proposes a path-velocity planner that consists of a global path planner, and two velocity planners. These velocity planners combine pure pursuit path following and proposed collision-avoidance method. This research presents a modified version of the Headed Social Force Model (MHSFM) based on modified HVO (MHVO). The linear velocity of MHVO is employed to generate the MHSFM target force. The interaction force of MHSFM is influenced by static and moving objects, and pursuers. The angular velocity of MHVO is used to steer the robot away from the pursuer. This proposed method was evaluated with two simulation scenarios. MHVO-based MHSFM was implemented into a two-wheeled differentialsteering mobile robot that navigated in an indoor human environment. The results show that the proposed method is capable to evade a pedestrian pursuer by maintaining the safety and comfort of other pedestrians with an average value of 0.25 on the Threat Level Index (TLI).INDEX TERMS Collision-avoidance, differential-steering mobile robot, modified headed social force model, modified hybrid velocity obstacles, pedestrian pursuer, path-velocity planner.

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“…In addition, the key element of a mobile robot to be successfully operate is the navigation such as localization and collision avoidance. A method of collision avoidance based on velocity control has been proposed in [6]. Localization in outdoor environment often employs Global Positioning Systems (GPS) such as mobile robot for gas level mapping [7].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

Dikairono¹,

Setiawardhana²,

Purwanto³

et al. 2020

IJIES

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The Convolutional Neural Network (CNN) is an object classification method that has been widely used in recent research. In this paper, we propose CNN for use in the self-localization of wheeled soccer robots on a soccer field. If the soccer field is divided into equally sized quadrants with imaginary vertical and horizontal lines intersecting in the middle of the field, then the soccer field has an identical shape for each quadrant. Every quadrant is a reflection of the other quadrants. Superficially similar images appearing in different positions may result in positioning mistakes. This paper proposes a solution to this problem by using a visual modelling of the gyrocompass line mark and omni-vision image for the CNN-based self-localization system. A gyrocompass is used to obtain the angle of the robot on the soccer field. A 360° omni-vision camera is used to capture images that cover all parts of the soccer field wherever the robot is located. The angle of the robot is added to the omni-vision image using the visual modelling method. The implementation of self-localization without visual modelling gives accuracy rates of 0.3262, and this result is increased to 0.6827 with the proposed methods. The experiment was carried out in the robotics laboratory of the Institut Teknologi Sepuluh Nopember (ITS) with the ITS Robot with Intelligent System (IRIS) robot.

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Collision Avoidance of Multi Modal Moving Objects for Mobile Robot Using Hybrid Velocity Obstacles

Cited by 11 publications

References 14 publications

Differential Drive Analysis of Spherical Magnetic Robot Using Multi-Single Board Computer

Differential Drive Analysis of Spherical Magnetic Robot Using Multi-Single Board Computer

Evading of Pedestrian Pursuer and Avoiding Obstacles Using Path-Velocity Planner

CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

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