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GENERAL INFORMATION

1. Title of Dataset:
	Video for pick&place study in AFRC project. [Dataset]
 
2. Contributor information:
	Yijun Yan			(University of Strathclyde)
	Jaime Zabalza		(University of Strathclyde)
	Carmelo Mineo		(University of Strathclyde)
	Erfu Yang  			(University of Strathclyde)
	Zixiang Fei			(University of Strathclyde)
	Cuebong Wong		(University of Strathclyde)

3. Date of data collection:
	2018-03-31 

4. Funding:
	University of Strathclyde [Advanced Forming Research Centre (AFRC) under its Route to Impact 
	Funding Programme 2017-2018]  

ACCESS INFORMATION

1. License:
	https://creativecommons.org/licenses/by/4.0/
		 

2. Links to publications that cite or use the data:
	https://doi.org/10.3390/s19061354
	https://tinyurl.com/y5phtv58
	

3. Recommended citation: 
	YAN, Y., ZABALZA, J., MINEO, C., YANG, E., FEI, Z. and WANG, C. 2019. Video for pick&place
	study in AFRC project. [Dataset]. Hosted on Pureportal (University Strathclyde) [online].
	Available from: https://doi.org/10.15129/4df22803-2cc4-4cce-9cea-509f88f1b504
	
DATA AND FILE OVERVIEW

1. File List:
	Pick_place1(MP4) - Video clip shows the robot adapting to the sudden appearance of the obstacle
						(16 seconds).
	Pick_ place2(MP4) - Video clip shows the robot adapting to the sudden appearance of the obstacle
						(13 seconds).
		
METHODOLOGICAL INFORMATION

1. Summary: 
	The video data was created for the demo of Pick&place study in AFRC (Advanced Forming Research Centre)
	project. It quite clearly shows the robot adapting to the sudden appearance of the obstacle.			

2. Detailed methodology: 
	In order to extend the abilities of current robots in industrial applications towards more
	autonomous and flexible manufacturing, this work presents an integrated system comprising
	real-time sensing, path-planning and control of industrial robots to provide them with adaptive
	reasoning, autonomous thinking and environment interaction under dynamic and challenging
	conditions. The developed system consists of an intelligent motion planner for a 6
	degrees-of-freedom robotic manipulator, which performs pick-and-place tasks according to an
	optimized path computed in real-time while avoiding a moving obstacle in the workspace. This
	moving obstacle is tracked by a sensing strategy based on machine vision, working on the HSV
	space for color detection in order to deal with changing conditions including non-uniform
	background, lighting reflections and shadows projection. The proposed machine vision is
	implemented by an off-board scheme with two low-cost cameras, where the second camera is aimed
	at solving the problem of vision obstruction when the robot invades the field of view of the
	main sensor. Real-time performance of the overall system has been experimentally tested, using
	a KUKA KR90 R3100 robot. A machine vision module based on low-cost cameras and color detection
	in the hue, saturation, value (HSV) space is developed to make the robot aware of its changing
	environment. Therefore, this vision allows the detection and localization of a randomly moving
	obstacle. Path correction to avoid collision avoidance for such obstacles with robotic
	manipulator is achieved by exploiting an adaptive path planning module along with a dedicated
	robot control module, where the three modules run simultaneously. These sensing/smart
	capabilities allow the smooth interactions between the robot and its dynamic environment, where
	the robot needs to react to dynamic changes through autonomous thinking and reasoning with the
	reaction times below the average human reaction time. The experimental results demonstrate that
	effective human-robot and robot-robot interactions can be realized through the innovative
	integration of emerging sensing techniques, efficient planning algorithms and systematic designs.