Collaborative motion planning of humanoid robots
For a matter of efficiency and robustness, it is often better to use a team of robots instead of a single agent to solve a given problem. A key challenge with multi-robot systems is the collaboration in order to accomplish complex tasks. To coordinate them, we can pre-compute their behavior. However, this method might not be robust to some events such as modification of environment or robots team. To overcome this issue, an adaptive decentralized coordination framework is needed for heterogeneous multiple robot systems.
We consider a team of two robots NAOs which can only exchange information when they are close to each other, or via symbols grounded to each embodiment. They are initially in a room a few meters away from each other. The goal is to make them meet and then perform an action such as exchanging an object or some information.
In this thesis, we study first robots specifications and adopt tools used for robot control. A tracking method in a simple situation is then described. The robots’ strategy is structured and improved adding obstacles limiting the two agents’ motion.
The achieved robust framework allows two humanoid robots to meet, even if one has a problem and can not move.
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