Obstacle Avoidance
This dataset contains simulated and real-world experimental data associated with the paper “Comprehensive Analysis of Optimization-Based Obstacle Avoidance for Agricultural Robotics in Greenhouse Environments.” The dataset from the simulated environment comprises multiple CSV files generated from the Gazebo simulation of a differential robot, the Stretch Robot. These files document the robot's movement, capturing data from the Gazebo model topic.
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This dataset contains simulated and real-world experimental data associated with the paper “Comprehensive Analysis of Optimization-Based Obstacle Avoidance for Agricultural Robotics in Greenhouse Environments.” The dataset from the simulated environment comprises multiple CSV files generated from the Gazebo simulation of a differential robot, the Stretch Robot. These files document the robot's movement, capturing data from the Gazebo model topic.
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This paper investigates the finite-time formation control problem for high-order nonlinear multiagent systems (MASs) with consideration of obstacle avoidance, unmeasurable states and dead-zone input. A neural networks k-filter observer is designed to estimate the unmeasurable states and cope with the problem of dead-zone input. Also, by using a tangent type Lyapunov barrier function (LBF), the obstacle avoidance mission can be completed for MASs without dynamic mismatching.
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Obstacle avoidance methodologies seldom work under assumption or limitation of other obstacel implying similar collision averting protocol. Furthermore, prerequisite of having perfect sensing and having central communciation are also needed in order to safely navigate witout collision. A novel obstacle avoidance method of FLC-ORCA is initiated in an attemp to fill in the void within obstacle avoidance. The proposed method is compared with other state-of-the-art tecniques such as Improved A-Star and Directional ORCA.
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198 Views