Allocation of Proximity Sensors for Obstacle Detection of a Robot Manipulator

1997 ◽  
Vol 9 (6) ◽  
pp. 482-489
Author(s):  
Takahiro Tsuchiya ◽  
◽  
Ryosuke Masuda ◽  
Keyword(s):  
Obstacle Avoidance ◽  
Robot Control ◽  
Robot Manipulator ◽  
Obstacle Detection ◽  
The Other ◽  
Work Space ◽  
Work Area ◽  
Joint Coordinates ◽  
Avoidance Control ◽  
Allocation Methods

In this paper, we discuss the sensor allocation problem in detecting obstacles in robot manipulators. The detection of obstacles in a work area is important for safety purposes and for the efficiency of robot control. Therefore, it is necessary to allocate the sensors properly on the links in a robot manipulator. Here, we propose two types of effective sensor allocation methods. One is based on the joint coordinates of the robot, and the other is based on the orthogonal work space. In addition, we show the allocation of additional sensors based on the quantitative conditions of the robot and its obstacles. The optical proximity sensor, which was developed by the authors, is used, and the proposed allocation methods are applied using a SCARA-type robot. It is proved, by experiments on obstacle avoidance control, that effective sensor allocations can be found.

Obstacle avoidance inverse kinematics solution of redundant robots by neural networks

Robotica ◽  
1997 ◽  
Vol 15 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Ziqiang Mao ◽  
T. C. Hsia
Keyword(s):  
Neural Network ◽  
Obstacle Avoidance ◽  
Inverse Kinematics ◽  
Robot Manipulator ◽  
Solution Technique ◽  
Training Algorithms ◽  
Neural Network Approach ◽  
Work Space ◽  
The Neural Network ◽  
Free Case

This paper investigates the neural network approach to solve the inverse kinematics problem of redundant robot manipulators in an environment with obstacles. The solution technique proposed requires only the knowledge of the robot forward kinematics functions and the neural network is trained in the inverse modeling manner. Training algorithms for both the obstacle free case and the obstacle avoidance case are developed. For the obstacle free case, sample points can be selected in the work space as training patterns for the neural network. For the obstacle avoidance case, the training algorithm is augmented with a distance penalty function. A ball-covering object modeling technique is employed to calculate the distances between the robot links and the objects in the work space. It is shown that this technique is very computationally efficient. Extensive simulation results are presented to illustrate the success of the proposed solution schemes. Experimental results performed on a PUMA 560 robot manipulator is also presented.

scholarly journals Method for Robot Manipulator Joint Wear Reduction by Finding the Optimal Robot Placement in a Robotic Cell

2021 ◽  
Vol 11 (12) ◽  
pp. 5398
Author(s):  
Tomáš Kot ◽  
Zdenko Bobovský ◽  
Aleš Vysocký ◽  
Václav Krys ◽  
Jakub Šafařík ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Dynamic Simulation ◽  
Robot Manipulator ◽  
The Other ◽  
Robotic Arm ◽  
Robotic Cell ◽  
Wear Reduction ◽  
Fixed End ◽  
Robot Placement ◽  
Collaborative Robot

We describe a method for robotic cell optimization by changing the placement of the robot manipulator within the cell in applications with a fixed end-point trajectory. The goal is to reduce the overall robot joint wear and to prevent uneven joint wear when one or several joints are stressed more than the other joints. Joint wear is approximated by calculating the integral of the mechanical work of each joint during the whole trajectory, which depends on the joint angular velocity and torque. The method relies on using a dynamic simulation for the evaluation of the torques and velocities in robot joints for individual robot positions. Verification of the method was performed using CoppeliaSim and a laboratory robotic cell with the collaborative robot UR3. The results confirmed that, with proper robot base placement, the overall wear of the joints of a robotic arm could be reduced from 22% to 53% depending on the trajectory.

scholarly journals A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1069
Author(s):  
Shibbir Ahmed ◽  
Baijing Qiu ◽  
Fiaz Ahmad ◽  
Chun-Wei Kong ◽  
Huang Xin
Keyword(s):  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
State Of The Art ◽  
Obstacle Detection ◽  
Crop Monitoring ◽  
Data Intensive ◽  
Structural Details ◽  
Dynamic Operating ◽  
New Generation ◽  
The Internet Of Things

Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of Things (IoT) UAV systems, especially when used as sprayer UAVs, where data needs to be captured and preprocessed for robust real-time obstacle detection and collision avoidance. Moreover, because of the objective and operational difference between general UAVs and sprayer UAVs, not every obstacle detection and collision avoidance method will be sufficient for sprayer UAVs. In this regard, this article seeks to review the most relevant developments on all correlated branches of the obstacle avoidance scenarios for agricultural sprayer UAVs, including a UAV sprayer’s structural details. Furthermore, the most relevant open challenges for current UAV sprayer solutions are enumerated, thus paving the way for future researchers to define a roadmap for devising new-generation, affordable autonomous sprayer UAV solutions. Agricultural UAV sprayers require data-intensive algorithms for the processing of the images acquired, and expertise in the field of autonomous flight is usually needed. The present study concludes that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions.

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