Implementing Robots in Defence Through Motion Capture with Mixed Reality

Our soldiers are fighting for us, risking their lives and people working in mines spoiling their health. In this paper we will see how we will implement the technology of mixed reality and motion capture will give solutions for replacing humans with robots. We can save a lot of lot of human lives and it will be more cost efficient. As on today we are implementing motion capture in analyzing the responses of military soldiers to test their capabilities and doing animations in movies. So let us extend the existing features to implement a remote robot control system that allows us to replace humans with robots.

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Development of Mixed Reality Robot Control System Based on HoloLens

Intelligent Robotics and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-030-27526-6_50 ◽

2019 ◽

pp. 571-581 ◽

Cited By ~ 1

Author(s):

Xuanmeng Sha ◽

Zixi Jia ◽

Weidong Sun ◽

Yida Hao ◽

Xingang Xiao ◽

...

Keyword(s):

Control System ◽

Robot Control ◽

Mixed Reality ◽

Robot Control System

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Control system of 4-DOF palletizing robot based on improved R control multi-objective trajectory planning

Advances in Mechanical Engineering ◽

10.1177/16878140211002705 ◽

2021 ◽

Vol 13 (4) ◽

pp. 168781402110027

Author(s):

Jianqiang Wang ◽

Yanmin Zhang ◽

Xintong Liu

Keyword(s):

Control System ◽

Trajectory Planning ◽

Robot Control ◽

Machining Process ◽

Planning System ◽

Positioning Accuracy ◽

Multi Objective ◽

Planning Algorithm ◽

Function Testing ◽

Robot Control System

To realize efficient palletizing robot trajectory planning and ensure ultimate robot control system universality and extensibility, the B-spline trajectory planning algorithm is used to establish a palletizing robot control system and the system is tested and analyzed. Simultaneously, to improve trajectory planning speeds, R control trajectory planning is used. Through improved algorithm design, a trajectory interpolation algorithm is established. The robot control system is based on R-dominated multi-objective trajectory planning. System stack function testing and system accuracy testing are conducted in a production environment. During palletizing function testing, the system’s single-step code packet time is stable at approximately 5.8 s and the average evolutionary algebra for each layer ranges between 32.49 and 45.66, which can save trajectory planning time. During system accuracy testing, the palletizing robot system’s repeated positioning accuracy is tested. The repeated positioning accuracy error is currently 10−1 mm and is mainly caused by friction and the machining process. By studying the control system of a four-degrees-of-freedom (4-DOF) palletizing robot based on the trajectory planning algorithm, the design predictions and effects are realized, thus providing a reference for more efficient future palletizing robot design. Although the working process still has some shortcomings, the research has major practical significance.

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