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Goal-directed Behaviour-based Planning for Multi-Robot Systems


Goal-directed Behaviour-based Planning for Multi-Robot Systems


Higher-level control of multi-robot systems requires appropriate decision-making and planning in highly dynamic (uncertain) environments. The different existing concepts of reactive decision-making and goal-directed symbolic planning have advantages and disadvantages in such environments. An advantageous solution can be the fusion in a hybrid approach incorporating the advantages of both concepts.

As part of a Bachelor or Master's Thesis you will further improve or extend an already existing hybrid solution that is part of a larger concept. The existing systems consists of a reactive behaviour-network layer and symbolic planner that uses PDDL.
Depending on the particular interests it is possible to focus on specific aspects or to address a wider ranger of smaller issues as well as to integrate your own ideas. The existing solution is tightly integrated into the Robot Operating System (ROS) and consists of modules written in Python as well as C++.

Supervision (Betreuung) is possible in English or German (Deutsch).

Possible topics:

1. Further development of the behaviour-network layer
- Improve concurrency (Planner)
- Allow for parametrised behaviours
- Other minor extensions → Developing additional activation function kernels, sensors, ...
2. Incorporating reinforcement learning (RL) into the behaviour-network layer
3. Integration of task decomposition by making use of another abstraction level for meta-level goals
- centralised vs. decentralised task decomposition (auction)
- Automated task decomposition
4. Integrating a probability concept
- Sensors and effects might not be accurate, how to incorporate noise and inaccuracy
5. Runtime monitoring and software engineering
- Developing external tools and visualisations that improve monitoring and analysing of the planning system: rqt, rviz integration of knowledge base, self-organisation data structures, behaviour states and many more
- Developing a graphical notation (inspiration UML) for simplified modelling, interaction and monitoring of the behaviour system
6. Integrating alternative planners
- Temporal planners as OPTIC or POPF2
- Mixed discrete/continuous domains planner DiNo
7. Further improving the self-organization integration
- Intelligent, for instance experience-based, self-organisation mechanisms selection (related to RL). Continuous evaluation of decisions during operation.

- Python/C++ depending on the particular topic
- Foundation in AI (e.g. Planning, Decision-Making) and Machine Learning depending on the particular topic
- Experience with ROS and Linux are an advantage
- Experience with Qt for UI related topics

Related Literature:
- Christopher-Eyk Hrabia, Stephan Wypler, Sahin Albayrak, “Towards Goal-driven Behaviour Control of Multi-Robot Systems”, IEEE 3nd International Conference on Control, Automation and Robotics (ICCAR); 2017
- Philipp Allgeuer and Sven Behnke, “Hierarchical and State-Based Architectures for Robot Behavior Planning and Control,” in Proceedings of 8th Workshop on Humanoid Soccer Robots, IEEE-RAS Int. Conf. on Humanoid Robots, Atlanta, USA, 2013, http://www.humanoidsoccer.org/ws13/papers/HSR13_Allgeuer_Behavior.pdf.
- Young-Seol Lee and Sung-Bae Cho, “A Hybrid System of Hierarchical Planning of Behaviour Selection Networks for Mobile Robot Control,” International Journal of Advanced Robotic Systems, 2014, 1, doi:10.5772/56088.
- David Jung and Alexander Zelinsky, “An Architecture for Distributed Cooperative Planning in a Behaviour-Based Multi-Robot System,” Robotics and Autonomous Systems, Field and Service Robotics, 26, no. 2–3 (February 28, 1999): 149–74, doi:10.1016/S0921-8890(98)00066-9.

Supervisor: Christopher-Eyk Hrabia
Mail: christopher-eyk.hrabia@tu-berlin.de

If you are interested in this research field please contact me by mail and include a CV as well as your current transcript of records (Notenübersicht).


Betreuer:  Christopher-Eyk Hrabia
Email:   christopher-eyk.hrabiatu-berlin.de


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