Task planning and allocation for multi-agent collaborative robot systems

In this thesis we address the problem of human robot interaction in industrial environments from collaboration perspective. This thesis, in particular, focuses on introducing novel frameworks for coordination of heterogeneous teams made of humans and robots that collaboratively aim to reach to a common goal. In the last decade, robots has received enormous attentions for being employed in both industrial environments and workplaces. Thin is mainly because of a number of reasons: (I) the shift of mass production industries toward autonomous industry units, (II) huge amount of financial and scientific investments on robotics, and (III) substitution of humans with robots to accomplish hazardous and stressful tasks. However Due to the limited cognitive knowledge and reasoning of the robots in accomplishing complex operations, they still are not able to operate in a fully autonomous fashion and independently from their human counterparts. Therefore presence of human operators, as a complementary counterparts, in workplaces becomes fundamental for robots to become utterly safe, reliable and operative. The goal of this thesis is to design and implement a framework whereby humans and/or robots can together play a complementary role, while applying their individual skills to accomplish a task. Human-robot collaboration (HRC) is defined as the purposeful interaction among humans and robots in a shared space, and it is aimed at a common goal. The design of such framework for HRC problems, requires to satisfy many requirements from which flexibility, adaptability and safety, are the primary characteristics of such framework. In this thesis we mainly focus on multi-agent robot systems task allocation and planning. We consider two main aspects in defining our objectives: on one hand we investigate on HRC, and implement alternative frameworks to model and study collaborations in industrial scenarios considering various roles of humans in coordination and collaboration with robots. On the other hand, the presence of humans is neglected and it is assumed that robots are able to fully precept the environment independently from human cognitive support, as this can be the case of future where Artificial intelligence might substitute the skills of humans. To model a HRC scenario, a smart framework is required to start, coordinate and terminate a collaborative process. This framework, in particular has to be aware of agents and their types, determine their responsibilities and roles, be aware of their physical structure, define the logical relationship among the agent and handle the collaboration process fluently. In this thesis, to address the framework described above, we propose different frameworks and evaluate their effectiveness in solving HRC problems. To formulate task planning and allocation problem , we introduce and implement three variants of AND/OR graphs, namely, c-layer AND/OR graphs, Branched AND/OR graphs, and Iteratively deepened AND/OR graphs. The first two variants aim at addressing the problem of task allocation among humans and collaborative robots in object defect inspection (ODI) scenarios in HRC context. Instead, the third variant targets Task and motion planning (TAMP) problems for heterogeneous robots. TAMP problems, compared to HRC problem, is not only responsible for allocating task among agents at higher-level, but also at lower level, it plans motions for agents and ultimately, interconnects higher levels of task planning to lower levels of motion planning and control to achieve a complete planning framework. To validate the applicability and scalability of our proposed frameworks, we design and implement various real-world and simulation experiments and we also evaluate their effectiveness in terms of achieving desired objectives, and quantitatively with other available methods in the literature.