A real-time streamline-based obstacle avoidance system for curvature-constrained nonholonomic mobile robots

Kuo, Pei-Li; Wang, Chung-Hsun; Chou, Han-Jung; Liu, Jing‐Sin

doi:10.1109/adconip.2017.7983851

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…In order to comply with the kinematic constraints of the used robot, a conversion is performed of the directional forces present on y axis to a moment based on the radius of the robot and the vector direction of the rotational movement. The authors in [28] used the Hector SLAM to generate the map of the environment. The obstacles shape and location are supposed to be known in order to generate a curvature-based path for the obstacle avoidance scheme of a non-holonomic platform.…”

Section: Proposed Schemementioning

confidence: 99%

Combining Hector SLAM and Artificial Potential Field for Autonomous Navigation Inside a Greenhouse

Harik

Korsæth

2018

Robotics

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Abstract:The key factor for autonomous navigation is efficient perception of the surroundings, while being able to move safely from an initial to a final point. We deal in this paper with a wheeled mobile robot working in a GPS-denied environment typical for a greenhouse. The Hector Simultaneous Localization and Mapping (SLAM) approach is used in order to estimate the robots' pose using a LIght Detection And Ranging (LIDAR) sensor. Waypoint following and obstacle avoidance are ensured by means of a new artificial potential field (APF) controller presented in this paper. The combination of the Hector SLAM and the APF controller allows the mobile robot to perform periodic tasks that require autonomous navigation between predefined waypoints. It also provides the mobile robot with a robustness to changing conditions that may occur inside the greenhouse, caused by the dynamic of plant development through the season. In this study, we show that the robot is safe to operate autonomously with a human presence, and that in contrast to classical odometry methods, no calibration is needed for repositioning the robot over repetitive runs. We include here both hardware and software descriptions, as well as simulation and experimental results.

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Section: Proposed Schemementioning

confidence: 99%