Multi-Sensor Fusion Based Effective Obstacle Avoidance and Path-Following Technology

Dorj, Byambaa; Tuvshinjargal, Doopalam; Chong, Kil-To; Hong, Dong Pyo; Lee, Deok Jin

doi:10.1166/asl.2014.5680

Cited by 5 publications

(3 citation statements)

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“…Our proposed method shows robustness against noise. Effective parameter estimation of a curve lane detection could be used to control the speed and heading angle of the self-driving car [26].…”

Section: Introductionmentioning

confidence: 99%

Highly Curved Lane Detection Algorithms Based on Kalman Filter

2020

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The purpose of the self-driving car is to minimize the number casualties of traffic accidents. One of the effects of traffic accidents is an improper speed of a car, especially at the road turn. If we can make the anticipation of the road turn, it is possible to avoid traffic accidents. This paper presents a cutting edge curve lane detection algorithm based on the Kalman filter for the self-driving car. It uses parabola equation and circle equation models inside the Kalman filter to estimate parameters of a using curve lane. The proposed algorithm was tested with a self-driving vehicle. Experiment results show that the curve lane detection algorithm has a high success rate. The paper also presents simulation results of the autonomous vehicle with the feature to control steering and speed using the results of the full curve lane detection algorithm.

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Section: Introductionmentioning

confidence: 99%

Highly Curved Lane Detection Algorithms Based on Kalman Filter

2020

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“…The key concept of velocity obstacles is to transform the dynamic problem into several static problems in order to increase the capability of avoiding dynamic obstacle within unexpected environment changes [23]. Meanwhile, sensor based motion planning techniques are also widely used for robot navigation applications in dynamic environments, where the pose estimates of the moving obstacles are obtained by using sensory systems [24][25][26]. These sensor based navigation approaches also require the knowledge of the obstacle's velocities for an accurate navigation solution.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Motion Planning Method for Autonomous Robots Using Kinect Based Sensor Fusion and Virtual Plane Approach in Dynamic Environments

2015

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A new reactive motion planning method for an autonomous vehicle in dynamic environments is proposed. The new dynamic motion planning method combines a virtual plane based reactive motion planning technique with a sensor fusion based obstacle detection approach, which results in improving robustness and autonomy of vehicle navigation within unpredictable dynamic environments. The key feature of the new reactive motion planning method is based on a local observer in the virtual plane which allows the effective transformation of complex dynamic planning problems into simple stationary in the virtual plane. In addition, a sensor fusion based obstacle detection technique provides the pose estimation of moving obstacles by using a Kinect sensor and a sonar sensor, which helps to improve the accuracy and robustness of the reactive motion planning approach in uncertain dynamic environments. The performance of the proposed method was demonstrated through not only simulation studies but also field experiments using multiple moving obstacles even in hostile environments where conventional method failed.

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“…The robot, integrated with mechanics, electronics, sensing, controlling, computer technology and artificial intelligence can clean the room on its way by using its small dust-vacuum component. To make cleaning robot work, R&D staff must monitor robot position, attitude, speed and system internal status, perceive static and dynamic information around the robot environment to make vacuum cleaning robot operate smoothly and dynamically adapt to the working environment changes, and use the multi-sensor information fusion technology to achieve the robot positioning, obstacle avoidance and environmental modeling [1]. The purpose of the robot location is to tell its position as well as a variety of objects, and navigation is intended to guide the robot to reach the target position.…”

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confidence: 99%

System Design and Obstacle Avoidance Algorithm Research of Vacuum Cleaning Robot

Li¹,

Pan²

2015

2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

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Designing multi-sensor hardware system based on Cortex-M0 controller, detecting the surrounding of vacuum cleaning robot by sensor combination of ultrasonic, infrared, collision, etc., we proposed an information fusion algorithm with Kernel PCA based on SFC neural network to process the multi-sensor data. And the output result of SFC neural network is used to control the localization and obstacle avoidance of vacuum cleaning robot. The experimental results demonstrate that the multi-sensor hardware system and obstacle avoidance algorithm based on SFC neural network proposed in this paper can improve the localization and obstacle avoidance accuracy of vacuum cleaning robot highly, which is robust for different working environment of vacuum cleaning robot.

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Multi-Sensor Fusion Based Effective Obstacle Avoidance and Path-Following Technology

Cited by 5 publications

References 0 publications

Highly Curved Lane Detection Algorithms Based on Kalman Filter

Highly Curved Lane Detection Algorithms Based on Kalman Filter

Hybrid Motion Planning Method for Autonomous Robots Using Kinect Based Sensor Fusion and Virtual Plane Approach in Dynamic Environments

System Design and Obstacle Avoidance Algorithm Research of Vacuum Cleaning Robot

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