Robot navigation in dynamic environment using Navigation function APF with SLAM

Iizuka, Soh; Nakamura, Takahiko; Suzuki, Satoshi

doi:10.1109/mecatronics.2014.7018605

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…As a result, SLAM is associated with observation errors associated with sensors and caused by the changes in physical factors of the environment. Moreover, other researchers [14][15][16][17][18][19][20][21] conducted research works on SLAM for RN with real obstacles; however, there was no mention of VOs in their research. A need to investigate and explore possible navigation inefficiencies or inaccuracies from the point of view of sensor impairment cannot be overemphasised.…”

Section: Simultaneous Localisation and Mapping (Slam)mentioning

confidence: 99%

Virtual Obstacles for Sensors Incapacitation in Robot Navigation: A Systematic Review of 2D Path Planning

Ngwenya

Ayomoh

Yadavalli

2022

Sensors

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The field of mobile robot (MR) navigation with obstacle avoidance has largely focused on real, physical obstacles as the sole external causative agent for navigation impediment. This paper has explored the possible option of virtual obstacles (VOs) dominance in robot navigation impediment in certain navigation environments as a MR move from sone point in the workspace to a desired target point. The systematically explored literature presented reviews mostly between the years 2000 and 2021; however, some outlier reviews from earlier years were also covered. An exploratory review approach was deployed to itemise and discuss different navigation environments and how VOs can impact the efficacy of both algorithms and sensors on a robotic vehicle. The associated limitations and the specific problem types addressed in the different literature sources were highlighted including whether or not a VO was considered in the path planning simulation or experiment. The discussion and conclusive sections further recommended some solutions as a measure towards addressing sensor performance incapacitation in a robot vehicle navigation problem.

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Section: Simultaneous Localisation and Mapping (Slam)mentioning

confidence: 99%

Virtual Obstacles for Sensors Incapacitation in Robot Navigation: A Systematic Review of 2D Path Planning

Ngwenya

Ayomoh

Yadavalli

2022

Sensors

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“…Consequently, mobile robot that coexists with people is required to adapt to such environment [1]. In this case, a map of the environment may be used by the mobile robot.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, mobile robot navigation has been studied as the basic functionality of a robot in living environments such as home or office. Consequently, mobile robot that coexists with people is required to adapt to such environment [1]. In this case, a map of the environment may be used by the mobile robot.…”

Section: Introductionmentioning

confidence: 99%

Grid-Edge-Depth Map Building Employing Sad With Sobel Edge Detector

Rahmani

Harjoko

Priyambodo

2017

International Journal on Smart Sensing and Intelligent Systems

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-This paper presents a new method of a map building which is suitable for a wheeled robot. The 2D map represents the obstacle's position and distance in the environment. The information of the obstacles obtained from a calibrated stereo camera. The stereo images size were 320x240 pixels. Hereafter the images were rectified and the disparity map was built using a Sum of Absolute Difference (SAD) algorithm. The depth map was calculated using disparity map, focal length, and baseline parameter values. In order to detect the obstacles, Sobel edge detection was implemented. The edge detection image was compared and substituted with the depth map which is resulting edge-depth map. The edge-depth map was divided into 25 grids (5 grids horizontal and 5 grids vertical). Finally, the minimum depth of detected obstacles for each grid was calculated. This process was resulting in a grid-edge-depth map (GED map). The proposal has been tested with a mobile robot in 5x3 meters living environment. Finally, experimental results are presented. The average error of feature points in the previous study was 5.40 cm, whereas in this study is 3.82 cm. There has been a decrease in the measurement gap of 29.26% from the previous study.

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