COORDINATION OF LEADER-FOLLOWER MULTI-AGENT SYSTEM WITH TIME-VARYING OBJECTIVE FUNCTION

This thesis aims to introduce a new framework for the distributed control of multi-agent systems with adjustable swarm control objectives. Our goal is twofold: 1) to provide an overview to how time-varying objectives in the control of autonomous systems may be applied to the distributed control of multi-agent systems with variable autonomy level, and 2) to introduce a framework to incorporate the proposed concept to fundamental swarm behaviors such as aggregation and leader tracking. Leader-follower multi-agent systems are considered in this study, and a general form of time-dependent artificial potential function is proposed to describe the varying objectives of the system in the case of complete information exchange. Using Lyapunov methods, the stability and boundedness of the agents\u27 trajectories under single order and higher order dynamics are analyzed. Illustrative numerical simulations are presented to demonstrate the validity of our results. Then, we extend these results for multi-agent systems with limited information exchange and switching communication topology. The first steps of the realization of an experimental framework have been made with the ultimate goal of verifying the simulation results in practice

Probabilistic Human-Robot Information Fusion

This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability

Designing for dynamic task allocation

Future platforms are envisioned in which human-machine teams are able to share and trade tasks as demands in situations change. It seems that human-machine coordination has not received the attention it deserves by past and present approaches to task allocation. In this paper a simple way to make coordination requirements explicit is proposed and for dynamic task allocation a dual-route approach is suggested. Advantages of adaptable automation, in which the human adjusts the way tasks are divided and shared, are complemented with those of adaptive automation, in which the machine allocates tasks. To be able to support design for dynamic task allocation, a theory about task allocation decision making by means of modeling of trust is proposed. It is suggested that dynamic task allocation is improved when information about situational abilities of agents is provided and the cost of observing and re-directing agents is reduced

Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue

Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion

The European Union's Institutional Design

The paper offers a theoretical appraisal of the various steps follow by the countries involved in the EU integration process. It shows that the extent to which the leaps to higher cohesion level in common policies have been achieved by the linkages between different games such that the common policies prove to be mutually advantageous across countries

Quantitative evaluation of Human-Robot options for maintenance tasks during analogue surface operations

Due to the scarcity of human labour plus the harsh conditions at any human Mars base of the foreseeable future, robots are likely to be employed in to assist with at least some assembly, deployment, transportation, inspection, servicing or repair tasks. By the first human landing, robotic technology is expected to have made possible the use of robot teams already on the surface to prepare the landing site, ensure the functioning of ISRU equipment and survey the local area for the arriving astronauts. Robots are also likely to assist them during their stay and after their departure. Today’s researchers are increasingly interested in the question of how to systematically choose the best combination of robots and/or humans for particular tasks, and how to actually demonstrate and measure teams performing these tasks in realistic simulations. This paper critically examines a quantitative method developed by Roderiguez and Weisbin of JPL for computing performance/resource scores for a range of human-machine systems on a variety of tasks. It then proposes a practical experiment, to be conducted at a future Mars Society surface operations simulation, that will apply the method to quantitatively compare human maintenance task scores with those of a hexapodal service robot that the author is currently building