Static and Dynamic Path Planning of Humanoids using an Advanced Regression Controller

Kumar, Priyadarshi Biplab; Sahu, Chinmaya; Parhi, Dayal R.; Pandey, Krishna Kant; Chhotray, Animesh

doi:10.24200/sci.2018.5064.1071

Cited by 12 publications

(12 citation statements)

References 35 publications

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“…Kumar et al did initial research on static and dynamic path planners on humanoid robots [238]. They developed a novel controller that represents static path planner as a single robot encountering random static obstacles and dynamic planner as multiple robots encountering random static obstacles.…”

Section: Path Planningmentioning

confidence: 99%

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Kolar

Benavidez

Jamshidi

2020

Sensors

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This paper focuses on data fusion, which is fundamental to one of the most important modules in any autonomous system: perception. Over the past decade, there has been a surge in the usage of smart/autonomous mobility systems. Such systems can be used in various areas of life like safe mobility for the disabled, senior citizens, and so on and are dependent on accurate sensor information in order to function optimally. This information may be from a single sensor or a suite of sensors with the same or different modalities. We review various types of sensors, their data, and the need for fusion of the data with each other to output the best data for the task at hand, which in this case is autonomous navigation. In order to obtain such accurate data, we need to have optimal technology to read the sensor data, process the data, eliminate or at least reduce the noise and then use the data for the required tasks. We present a survey of the current data processing techniques that implement data fusion using different sensors like LiDAR that use light scan technology, stereo/depth cameras, Red Green Blue monocular (RGB) and Time-of-flight (TOF) cameras that use optical technology and review the efficiency of using fused data from multiple sensors rather than a single sensor in autonomous navigation tasks like mapping, obstacle detection, and avoidance or localization. This survey will provide sensor information to researchers who intend to accomplish the task of motion control of a robot and detail the use of LiDAR and cameras to accomplish robot navigation.

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Section: Path Planningmentioning

confidence: 99%

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Kolar

Benavidez

Jamshidi

2020

Sensors

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“…Based on the information about the obstacles, the working environment of a robot can be categorized as a completely known environment, a partially known environment, or a completely unknown environment. It can also be categorized as a static environment or a dynamic environment [50,51,52]. There are many path planning and navigation algorithms, such as PRM, RRT, EST, RRT*, APF, MPC, ANN, GA, PSO, ACO, and D* [53], compared to which the A* algorithm has advantages such as its simple principles, easy realization, and high efficiency.…”

Section: Improved A* Algorithmmentioning

confidence: 99%

Navigation Simulation of a Mecanum Wheel Mobile Robot Based on an Improved A* Algorithm in Unity3D

Dai

Shi

et al. 2019

Sensors

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Computer simulation is an effective means for the research of robot navigation algorithms. In order to implement real-time, three-dimensional, and visual navigation algorithm simulation, a method of algorithm simulation based on secondary development of Unity3D is proposed. With this method, a virtual robot prototype can be created quickly with the imported 3D robot model, virtual joints, and virtual sensors, and then the navigation simulation can be carried out using the virtual prototype with the algorithm script in the virtual environment. Firstly, the scripts of the virtual revolute joint, virtual LiDAR sensors, and terrain environment are written. Secondly, the A* algorithm is improved for navigation in unknown 3D space. Thirdly, taking the Mecanum wheel mobile robot as an example, the 3D robot model is imported into Unity3D, and the virtual joint, sensor, and navigation algorithm scripts are added to the model. Then, the navigation is simulated in static and dynamic environments using a virtual prototype. Finally, the navigation tests of the physical robot are carried out in the physical environment, and the test trajectory is compared with the simulation trajectory. The simulation and test results validate the algorithm simulation method based on the redevelopment of Unity3d, showing that it is feasible, efficient, and flexible.

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“…Dongre and Raikwal 13 used RA as a user web browsing prediction method based on previous training pattern. Kumar et al [14][15][16][17] have discussed regarding the use of various nature inspired algorithms for navigational analysis of humanoid robots. Al et al 18 used genetic algorithm (GA) to control the motion of a hybrid actuator by optimization of the parameters affecting the smooth movement.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

Kumar

Parhi

2019

Robotica

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SUMMARYIn the current investigation, a novel navigational controller has been designed and implemented for humanoids in cluttered environments. Here, regression analysis is hybridized with genetic algorithm (GA) for designing the controller. The obstacle distances collected in the form of sensor outputs are initially fed to the regression controller; and based on the previous training pattern data, an intermediate advancing angle (AA) is obtained as the first output. The intermediate AA obtained from the regression controller along with other inputs is again fed to the GA controller, which generates the final AA as the desired final output to avoid the obstacles present in a complex environment and reach the destination successfully. The working of the controller is tested on a V-REP simulation platform. In the current work, navigation of both single as well as multiple humanoids has been attempted. To avoid inter-collision among multiple humanoids during their navigation in a common platform, a Petri-Net model has been proposed. The simulation results are validated through a real-time experimental platform developed under laboratory conditions. The results obtained from both the simulation and experimental platforms are compared against each other and are found to be in good agreement with acceptable percentage of errors. Finally, the proposed controller is also compared with other existing navigational controller and an improvement in performance has been observed.

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Static and Dynamic Path Planning of Humanoids using an Advanced Regression Controller

Cited by 12 publications

References 35 publications

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Navigation Simulation of a Mecanum Wheel Mobile Robot Based on an Improved A* Algorithm in Unity3D

Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

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