Robotic Grasping of Large Objects for Collaborative Manipulation

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Muthusamy, Rajkumar Tariq, Usama 2017-10-30T08:03:33Z 2017-10-30T08:03:33Z 2017-10-23
dc.description.abstract In near future, robots are envisioned to work alongside humans in professional and domestic environments without significant restructuring of workspace. Robotic systems in such setups must be adept at observation, analysis and rational decision making. To coexist in an environment, humans and robots will need to interact and cooperate for multiple tasks. A fundamental such task is the manipulation of large objects in work environments which requires cooperation between multiple manipulating agents for load sharing. Collaborative manipulation has been studied in the literature with the focus on multi-agent planning and control strategies. However, for a collaborative manipulation task, grasp planning also plays a pivotal role in cooperation and task completion. In this work, a novel approach is proposed for collaborative grasping and manipulation of large unknown objects. The manipulation task was defined as a sequence of poses and expected external wrench acting on the target object. In a two-agent manipulation task, the proposed approach selects a grasp for the second agent after observing the grasp location of the first agent. The solution is computed in a way that it minimizes the grasp wrenches by load sharing between both agents. To verify the proposed methodology, an online system for human-robot manipulation of unknown objects was developed. The system utilized depth information from a fixed Kinect sensor for perception and decision making for a human-robot collaborative lift-up. Experiments with multiple objects substantiated that the proposed method results in an optimal load sharing despite limited information and partial observability. en
dc.format.extent 61
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Robotic Grasping of Large Objects for Collaborative Manipulation en
dc.type G2 Pro gradu, diplomityö fi Sähkötekniikan korkeakoulu fi
dc.subject.keyword grasp planning en
dc.subject.keyword multi-agent grasping en
dc.subject.keyword collaborative manipulation en
dc.subject.keyword load sharing en
dc.identifier.urn URN:NBN:fi:aalto-201710307401
dc.programme.major Space Robotics and Automation fi
dc.programme.mcode ELEC3047 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Kyrki, Ville
dc.programme Erasmus Mundus Space Master fi
dc.ethesisid Aalto 9796
dc.location P1 fi

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