scholarly journals A Study on the Feasibility of Robotic Harvesting for Chile Pepper

Robotics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 94
Author(s):  
Muhammad Umar Masood ◽  
Mahdi Haghshenas-Jaryani
Keyword(s):  
Success Rate ◽  
Degrees Of Freedom ◽  
Maximum Error ◽  
Location Estimation ◽  
Chile Pepper ◽  
Planning Algorithm ◽  
Estimation System ◽  
Chile Peppers ◽  
Path Planning Algorithm ◽  
Robotic Harvesting

This paper presents a study on the robotic harvesting of New Mexico type chile pepper, in a laboratory setting, using a five degrees of freedom (DoF) serial manipulator. The end-effector of the manipulator, a scissor-type cutting mechanism, was devised and experimentally tested in a lab setup which cuts the chile stem to detach the fruit. Through a MATLAB™-based program, the location of the chile pepper is estimated in the robot’s reference frame, using Intel RealSense Depth Camera. The accuracy of the 3D location estimation system matches the maximum accuracy claimed by the manufacturer of the hardware, with a maximum error to be in Y-axis, which is 5.7 mm. The forward and inverse kinematics are developed, and the reachable and dexterous workspaces of the robot are studied. An application-based path planning algorithm is developed to minimize the travel for a specified harvesting task. The robotic harvesting system was able to cut the chile pepper from the plant based on 3D location estimated by MATLAB™ program. On the basis of harvesting operation, on 77 chile peppers, the following harvesting indicators were achieved: localization success rate of 37.7%, detachment success rate of 65.5%, harvest success rate of 24.7%, damage rate of 6.9%, and cycle time of 7 s.

A Hierarchical Distributed Path Planning for Redundant Manipulators Based on Virtual Arm

1992 ◽  
Vol 4 (3) ◽  
pp. 210-217
Author(s):  
Toshio Tsuji ◽  
◽  
Koji Ito ◽  
Keyword(s):  
Path Planning ◽  
Degrees Of Freedom ◽  
Local Level ◽  
Intermediate Level ◽  
Global Level ◽  
Task Space ◽  
End Point ◽  
Planning Algorithm ◽  
Global Representation ◽  
Path Planning Algorithm

This paper proposes a collision-free path planning algorithm in the task space based on virtual arms. The virtual arm has the same kinematic structure as the actual arm except that its end-point is located at the joint or link of the actual arm. Therefore, the configuration of the actual arm can be represented as a set of end-points of the virtual arms, and the path planning for multi-joint manipulators can be performed only in the task space. Our method adopts a hierarchical strategy which consists of the global level, the intermediate level, and the local level. The global level plans the collision-free endpoint trajectory of the actual arm based on the global representation of the task space. The intermediate level generates the subgoals for the actual and virtual endpoints based on the current positions and the actual endpoint trajectory specified by the global level. The local level moves each end-point to the corresponding subgoal, avoiding the close obstacles based on the local informations of the task space. The effectiveness of the method is verified by computer simulations using a planar manipulator with redundant joint degrees of freedom.

A genetic path planning algorithm for redundant articulated robots

Robotica ◽  
1997 ◽  
Vol 15 (2) ◽  
pp. 213-224 ◽  
Author(s):  
Andreas C. Nearchou ◽  
Nikos A. Aspragathos
Keyword(s):  
Path Planning ◽  
Degrees Of Freedom ◽  
Target Location ◽  
Planning Problem ◽  
Worst Case ◽  
Pick And Place ◽  
Planning Algorithm ◽  
A New Technique ◽  
Articulated Robots ◽  
Path Planning Algorithm

In some daily tasks, such as pick and place, the robot is requested to reach with its hand tip a desired target location while it is operating in its environment. Such tasks become more complex in environments cluttered with obstacles, since the constraint for collision-free movement must be also taken into account. This paper presents a new technique based on genetic algorithms (GAs) to solve the path planning problem of articulated redundant robot manipulators. The efficiency of the proposed GA is demonstrated through multiple experiments carried out on several robots with redundant degrees-of-freedom. Finally, the computational complexity of the proposed solution is estimated, in the worst case.

Design and Experiment on Chinese Prickly Ash Automatic Picking Device

2014 ◽  
Vol 635-637 ◽  
pp. 1303-1307
Author(s):  
Zhi Yang He ◽  
Xiao Peng Huang ◽  
Jing Feng Wu ◽  
Fang Xin Wan
Keyword(s):  
Machine Vision ◽  
Success Rate ◽  
Degrees Of Freedom ◽  
Maximum Error ◽  
Experimental Results ◽  
False Identification ◽  
Positioning Accuracy ◽  
Minimum Radius ◽  
Automatic Picking ◽  
Chinese Prickly Ash

In order to realize automatic picking Chinese prickly ash fruits, We designed a special picking device with a five degrees of freedom mechanical arm based on machine vision. The mechanical structure was designed and the mechanical arm working range was analyzed. The experimental results show that the recognized positioning accuracy is ±10mm, the ratio of the fruit string minimum radius and the recognition radius was 0.95.The picking success rate was 93% when picking range distance was 200-800 mm, and the empty rate caused by false identification goal was 5%. The maximum error of the 3d coordinates which were calculated was 15 mm.

scholarly journals A new method on motion planning for mobile robots using jump point search and Bezier curves

2021 ◽  
Vol 18 (4) ◽  
pp. 172988142110192
Author(s):  
Ben Zhang ◽  
Denglin Zhu
Keyword(s):  
Motion Planning ◽  
Initial Point ◽  
Target Point ◽  
Jump Point ◽  
Bezier Curves ◽  
Bézier Curves ◽  
Front End ◽  
Planning Algorithm ◽  
Point Search ◽  
Path Planning Algorithm

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on motion planning systems that meet the shortest path and obstacle avoidance requirements. This article proposes a novel path planning algorithm based on jump point search and Bezier curves. The proposed algorithm consists of two main steps. In the front end, the improved heuristic function based on distance and direction is used to reduce the cost, and the redundant turning points are trimmed. In the back end, a novel trajectory generation method based on Bezier curves and a straight line is proposed. Our experimental results indicate that the proposed algorithm provides a complete motion planning solution from the front end to the back end, which can realize an optimal trajectory from the initial point to the target point used for robot navigation.

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

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