| dc.description.abstract | The early work in robotics and Artificial Intelligence showed great promise, but because of the challenges and difficulties met in the early phases, the two fields drifted apart. Artificial Intelligence focused more on algorithms and the abstract method of approaching problems, while the robotics aspect focused more on electrical and mechanical engineering. Now with the recent developments in Machine Learning, big data, computing power, sensors, software etc., the two paths these to fields have been on are getting closer.
The goal of this thesis is to try to combine Artificial Intelligence and robotics. The author has the most experience with robotics, and will therefore try to focus on the Artificial Intelligence part by making a fully usable planner. A planner, in this case, means a program that will find a sequence of actions that will lead to the desired goal. All the code for the planner has been written from scratch including a parser that will read the problem description files which the planner will utilize to find a solution to the planning problem.
To test the planner on a robotics system, the robot named KUKA YouBot is used to solve different planning problems such as Tower of Hanoi, a stack/restack problem of blocks and moving around in a domain where the robot must interact with the environment to complete its goal.
As mentioned above, the AI community has focused much on algorithms and the abstract thinking around it. The problems that the planning algorithms have been based on have been in a deterministic matter where everything is known, which is not a realistic assumption of the real world where uncertainty plays a big part. The planner is based on a deterministic model where everything is known. This thesis will therefore make an attempt to adapt the planner such that it also can handle cases where not everything is known from before. | |
