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Robotic grasping has been a prevailing problem ever since
humans began creating robots to execute human-like tasks. The problems
are usually due to the involvement of moving parts and sensors. Inaccuracy in sensor data usually leads to unexpected results. Researchers have
used a variety of sensors for improving manipulation tasks in robots.
We focus specifically on grasping unknown objects using mobile service
robots. An approach using convolutional neural networks to generate
grasp points in a scene using RGBD sensor data is proposed. Two convolutional neural networks that perform grasp detection in a top down
scenario are evaluated, enhanced and compared in a more general scenario. Experiments are performed in a simulated environment as well as
the real world. The results are used to understand how the difference in
sensor data can affect grasping and enhancements are made to overcome
these effects and to optimize the solution.
In this thesis a swarm intelligent approach for controlling and coordinating a multi- agent system is studied. Considering an example where bodyguard agents have to protect one or more VIP agents, it is shown that the swarm intelligent approach can be a flexible and robust way to control a multi-agent system.