2019
DOI: 10.3390/s19204384
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ITC: Infused Tangential Curves for Smooth 2D and 3D Navigation of Mobile Robots †

Abstract: Navigation is an indispensable component of ground and aerial mobile robots. Although there is a plethora of path planning algorithms, most of them generate paths that are not smooth and have angular turns. In many cases, it is not feasible for the robots to execute these sharp turns, and a smooth trajectory is desired. We present ‘ITC: Infused Tangential Curves’ which can generate smooth trajectories for mobile robots. The main characteristics of the proposed ITC algorithm are: (1) The curves are tangential t… Show more

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Cited by 8 publications
(5 citation statements)
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References 54 publications
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“…These search plans are fast and can work with maps of various sizes. By analyzing the mechanics of the robotic system, when the design has been generated on the graph, the path smoothing methods can generate a smoother path from start to goal positions [ 48 ] such as Infused Tangential Curves (ICT) method [ 49 ].…”
Section: Background and Related Workmentioning
confidence: 99%
“…These search plans are fast and can work with maps of various sizes. By analyzing the mechanics of the robotic system, when the design has been generated on the graph, the path smoothing methods can generate a smoother path from start to goal positions [ 48 ] such as Infused Tangential Curves (ICT) method [ 49 ].…”
Section: Background and Related Workmentioning
confidence: 99%
“…Obstacle avoidance incorporated with smoothing has been proposed in many previous works such as [34][35][36][37], and a review of all the works can be found in [38][39][40][41]. A modified algorithm [42] suited for vineyards in the context of robot navigation using extraction of pillar positions is briefly explained here. Path planning is basically divided into two stages of global and local planning.…”
Section: Obstacle Avoidance With Path Smoothingmentioning
confidence: 99%
“…A navigation architecture becomes efficient if the mobility of the robot is divided into specialized software modules. This architecture consists of software modularity (as a consequence of introducing a new sensor or maintaining some obstacle avoidance modules based on certain cinematics), robot location control based on the different functionalities and learning algorithms, techniques of time-domain analysis (response time of the sensors, temporal depth, space localization, and decision making based of the dynamics of the robot), and decoupled control [ 32 , 33 , 34 , 35 , 36 ]. Implementing machine learning and deep learning techniques requires precise information so that the path planning allows locating the robot in space and memorizing the position of obstacles.…”
Section: Configuration Of the Intervention Robotmentioning
confidence: 99%