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See detailAnt-Air self-learning algorithm for path planning in a cluttered environment
Ahmad, Rafiq UL; Plapper, Peter UL

in Applied Mechanics and Materials Journal (2014, December)

Path planning in unstructured area while dealing with narrow spaces is an area of research which is receiving extensive interest. Many existing algorithms are able to produce safe paths but the presented ... [more ▼]

Path planning in unstructured area while dealing with narrow spaces is an area of research which is receiving extensive interest. Many existing algorithms are able to produce safe paths but the presented concepts are either not adapted to narrow spaces or they are unable to learn from the past experience to improve repeated movements from the same agent or followed trajectories by other agents. This paper introduces an original concept based on Ant-Air phenomenon for safe path planning in a cluttered environment where narrow passages are treated. The algorithm presented is able to learn from the past experience and hence improve the already generated trajectory further by using some lessons learned from the past experience. The concept is applicable in various domains such as mobile robot path planning, manipulator trajectory generation and part movement in narrow passages in real or virtual assembly/disassembly process. [less ▲]

Detailed reference viewed: 159 (16 UL)
Full Text
Peer Reviewed
See detailHuman-Robot Collaboration: Twofold strategy algorithm to avoid collisions using ToF sensor
Ahmad, Rafiq UL; Plapper, Peter UL

in Applied Mechanics and Materials Journal (2014, December)

The importance of Human Robot Interaction to complement human skills in a manufacturing environment with industrial robots increases the concerns over safety of human and the robot. It is necessary to ... [more ▼]

The importance of Human Robot Interaction to complement human skills in a manufacturing environment with industrial robots increases the concerns over safety of human and the robot. It is necessary to identify collision risks and avoid them otherwise production stops may cost a huge amount to the industry. A robot working at manufacturing facility should be able to predict potential collisions and must be able to prevent i.e. react automatically for safe detour around obstacle/human. Currently, industrial robots are able to detect collisions after a real contact but the existing proposals for avoiding collisions are either computationally expensive or not very well adapted to human safety. The objective of this paper is to provide intelligence to the industrial robot to predict collision risks and react automatically without stopping the production in a static environment. The proposed approach using Time of Flight (TOF) camera, provides decision regarding trajectory correction and improvement by shifting robot to a secure position. The application presented in this paper is for safe KUKA robot trajectory generation in peg-in-hole assembly process in the laboratory context. [less ▲]

Detailed reference viewed: 205 (11 UL)