Automated, Depth Sensor Based Object Detection and Path Planning for Robot-Aided 3D Scanning

Ziegler, Jakob; Gattringer, Hubert; Kaserer, Dominik; Müller, Andreas

doi:10.1007/978-3-319-61276-8_37

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…This class of path planning problems is called sensor-based or online path planning which in general mean path planning in unknown environment. There are a variety of works done in this field resulting in different approaches with their specific characteristics, advantages and drawbacks [9][10][11][12]. In this class of path planning, the motion decisions are made simultaneously while the robot moves and obtains new data from the environment.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Motion Planning for Nonholonomic Mobile Robot Navigation in Unknown Indoor Environments

Khaksar¹,

Uddin²,

Tørresen³

2018

IJMERR

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An important expectation from a mobile robot is to be able to navigate in unexplored areas in the presence of unknown obstacles. Despite the great deal of work in this field, this problem is still a challenge as the planning domain is highly complex. This challenge can be seen in the form of high-cost solutions, high computational cost or increasing rate of failure. In this paper, a fuzzy motion planning approach is presented for guiding a nonholonomic mobile robot in unknown environments. The fuzzy controller uses the readings of the robot's sensor(s) in terms of total travelled distance, distance to the goal and the presence of obstacles in the vicinity of the robot; and computes the linear and angular velocity of the robot with a pre-defined frequency. The proposed method was tested through simulation as well as experimental studies on a TurtleBot in indoor environments. The simulation and experimental studies show the effective performance of the proposed approach in terms of solution cost, computational cost and failure rate. 

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Section: Introductionmentioning

confidence: 99%

Fuzzy Motion Planning for Nonholonomic Mobile Robot Navigation in Unknown Indoor Environments

Khaksar¹,

Uddin²,

Tørresen³

2018

IJMERR

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“…The added depth data is an attempt to improve the 2D detection process to include 3D environment information in the object detection process. Ziegler et al (2018) go one step further to render an RGB-D image as a point cloud. The point cloud is matched to a CAD model of the object to be detected with the intent to improve the accuracy of the object detection process allowing for better robotic -object interaction.…”

Section: Object Detectionmentioning

confidence: 99%

Robotic Feedback Loops: Implementing two-way communication in architecturally focused robotic pick and place operations

Fevre¹

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<p>The use of robots in the fabrication of complex architectural structures is increasing in popularity. However, architectural robotic workflows still require convoluted and time-consuming programming in order to execute complex fabrication tasks. Additionally, an inability for robots to adapt to different environments further highlights concerns around the robotic manipulator as a primary construction tool. There are four key issues currently present in robotic fabrication for architectural applications. Firstly, an inability to adapt to unknown environments; Secondly, a lack of autonomous decision making; Thirdly, an inability to locate, recognise, and then manipulate objects in the operating environment; Fourthly a lack of error detection if a motion instruction conflicts with environmental constraints. This research begins to resolve these critical issues by seeking to integrate a feedback loop in a robotic system to improve perception, interaction and manipulation of objects in a robotic working environment. Attempts to achieve intelligence and autonomy in static robotic systems have seen limited success. Primarily, research into these issues has originated from the need to adapt existing robotic processes to architectural applications. The work of Gramazio and Kohler Research, specifically ‘on-site mobile fabrication’ and ‘autonomous robotic stone stacking’, present the current state of the art in intelligent architectural robotic systems and begin to develop solutions to the issues previously outlined. However, the limitations of Gramazio and Kohler’s research, specifically around a lack of perception-controlled grasping, offers an opportunity for this research to begin developing relevant solutions to the outlined issues. This research proposes a system where blocks, of consistent dimensions, are randomly distributed within the robotic working environment. The robot establishes the location and pose (position and orientation) of the blocks through an adaptive inclusion test. The test involves subsampling a point-cloud into a consistent grid; filtering points based on their height above the ground plane in order to establish block surfaces, and matching these surfaces to a CAD model for improved accuracy. The resulting matched surfaces are used to determine four points which define the object rotation plane and centre point. The robot uses the centre point, and the quaternion rotation angle to execute motion and grasping instructions. The robot is instructed to repeat the perception process until the collection of all the blocks within the camera frame is complete, and a preprogrammed wall is built. The implementation of a robotic feedback loop in this way demonstrates both the future potential and success of this research. The research begins to develop pathways through which to integrate new types of technologies such as machine learning and deep learning in order to improve the accuracy, speed and reliability of perception-controlled robotic systems through learned behaviours.</p>

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Determination of Object Location for Robotic Grasping Using Depth Vision Sensor

Yovchev

Chikurtev

Chivarov

2020

Advances in Service and Industrial Robotics

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Automated, Depth Sensor Based Object Detection and Path Planning for Robot-Aided 3D Scanning

Cited by 5 publications

References 12 publications

Fuzzy Motion Planning for Nonholonomic Mobile Robot Navigation in Unknown Indoor Environments

Fuzzy Motion Planning for Nonholonomic Mobile Robot Navigation in Unknown Indoor Environments

Robotic Feedback Loops: Implementing two-way communication in architecturally focused robotic pick and place operations

Determination of Object Location for Robotic Grasping Using Depth Vision Sensor

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