Agents in teamwork may be highly interdependent on each other, the awareness of interdependence relationships is an important requirement for designing and consequently implementing a multi-agent system. In this work, we propose a formal graphical and domain-independent language that can facilitate the identification of comprehensive interdependences among the agents in teamwork. Moreover, a formal semantics is also introduced to precisely express and explain the properties of a graphical structure. The novel feature of the graphical language is that it complements the Interdependence Analysis Color Scheme in a way that explicitly models negative influences and, in addition, provides a visual-communication aid for developers. To demonstrate the applicability and sufficiency of the graphical language in a variety of domains, our case studies include a multi-robot scenario and a human-robot scenario.
We propose a new cognitive robot control architecture in which the cognitive layer can be programmed by means of the agent pro-gramming language Goal. The architecture exploits the support that agent-oriented programming offers for creating cognitive robotic agents, including symbolic knowledge representation, deliberation via modular, high-level action selection, and support for multiple, declarative goals. The benefits of the architecture are that it provides a flexible approach to develop cognitive robots and support for a clean and clear separation of concerns about symbolic reasoning and sub-symbolic processing. We discuss the design of our architecture and discuss the issue of translating sub-symbolic information and behaviour control into symbolic represen-tations needed at the cognitive layer. An interactive navigation task is presented as a proof of concept.
We investigate the role of communication in the coordination of cooperative robot teams and its impact on performance in search and retrieval tasks. We first discuss a baseline without communication and analyse various kinds of coordination strategies for exploration and exploitation. We then discuss how the robots construct a shared mental model by communicating beliefs and/or goals with one another, as well as the coordination protocols with regard to subtask allocation and destination selection. Moreover, we also study the influence of various factors on performance including the size of robot teams, the size of the environment that needs to be explored and ordering constraints on the team goal. We use the Blocks World for Teams as an abstract testbed for simulating such tasks, where the team goal of the robots is to search and retrieve a number of target blocks in an initially unknown environment. In our experiments we have studied two main variations: a variant where all blocks to be retrieved have the same color (no ordering constraints on the team goal) and a variant where blocks of various colors need to be retrieved in a particular order (with ordering constraints). Our findings show that communication increases performance but significantly more so for the second variant and that exchanging more messages does not always yield a better team performance.
When robots perform teamwork in a shared workspace, they might be confronted with the risk of blocking each other's ways, which will result in conflicts or interference among the robots. How to plan collision-free paths for all the robots is the major challenge issue in the multi-robot cooperative pathfinding problem, in which each robot has to navigate from its starting location to the destination while keeping avoiding stationary obstacles as well as its teammates. In this paper, we present a novel fully decentralized approach to this problem. Our approach allows the robots to make real-time responses to the dynamic environment and can resolve a set of benchmark deadlock situations subject to complex spatial constraints in the robots' workspace. When confronted with conflicting situations, robots can employ waiting, dodging, retreating and turning-head strategies to make local adjustments. In addition, experimental results show that our proposed approach provides an efficient and competitive solution t this problem.