Toward a Multi-Robot Coordination Formalism

Abstract

Coordination is an essential characteristic of any system, either natural or artificial, that is composed of multiple interacting agents. The mechanism by which the coordination is achieved determines such properties as how robust the system is to environmental perturbations, how efficient it is in performing a given task, and, more generally, what types of coordinated tasks can be achieved. A formal understanding of these issues will permit more focused analysis of coordinated natural systems and more effective approaches to the design of coordinated artificial systems. Toward this end, we are systematically investigating coordination in the context of multi-robot systems. We present a formal framework for multi-robot system (MRS) coordination and use it to discuss and reason about coordination. Using this principled foundation, we are developing a suite of general methods by which to automatically synthesize controllers for robots constituting a MRS such that a given task is performed in a coordinated fashion. This paper presents a method for the automatic synthesis of a specific type of controller, one that retains some internal state (e.g., memory of past occurrences) but is not capable of communication. Understanding the capabilities and limitations of a coordinated system composed of individual agents with such properties contributes to the understanding of when memory alone is sufficient to achieve the desired coordination and when other capabilities, such as communication, may be necessary. We validate our formal approach to the study of coordination in a multi-robot construction task domain through the use of both physically-realistic simulations and real-robot demonstrations.

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

“…To attain desired collective performance, implicit coordination approaches [93][94][95][96][97][98][99] use dynamics of interactions between robots and environment (i.e., implicit communication), mostly in the form of devised emergent behaviors [100]. Different sensors or devices are used with robots to perceive environmental changes.…”

“…In this section, we present some other coordination approaches used for other prominent applications of MRS. Such as, [95] based on State transition automata for construction task, [92] for fixed-obstacle avoidance, goal-seeking, and mobile-robot avoidance, [290,291] for Multi-robot SLAM, [292] based on "game-theoretic learning algorithms" using "fictitious play" and extended Kalman filter for cooperation among patrolling and material handling robots in a warehouses, [77,105] soccer, [202] collective construction, [259] cooperatively carrying mass by multiple UAV, [293] presents approach for coordination with limited communication ranges and communication failures i.e., rendezvous algorithm via proximity graph, [294] cooperatively performs a few of servicing tasks in a hospital environment, [89] can send information related to task in minimum time with a local communication scheme, [295] based on "mixed-integer linear programming" for Autonomous Intersection Management, [107] Localization and navigation of salve robots, [78] based on idea of setplay, i.e., to organize a robotic soccer team behavior for any "RoboCup cooperative league" and similar domains. Authors in [296] present a decentralized approach for coordination where robots use explicit communication during planning.…”

Automatic synthesis of communication-based coordinated multi-robot systems

“…Comm(x, c) (7) execute a random walk (8) else if obstacle nearby then (9) execute obstacle avoidance (10) else if ∃a(Act(x, cr, a) > 0) then (11) execute action a with prob. Act(x, cr, a) (12) execute a random walk (13) else (14) execute a random walk (15) endif (16) action and communication functions are integrated into the controller.…”

“…Given their formal grounding, they also provide a means to systematically determine the fundamental limitations of each type of controller, to understand the inherent relationships between different controller types, to contribute methods to systematically reduce one controller type to another, and to provide insight into the general requirements necessary for achieving different forms of coordination. In our previous work, we presented a method for the synthesis of DAct-IS-NoComm controllers and defined situations in which internal state is useful to achieve the necessary coordination [10] and a macroscopic MRS modeling approach directed to the analysis of homogeneous MRS composed of robots executing DAct-IS-NoComm controllers [11]. In this paper, we present a new method for the synthesis of a different type of controller, a DActNoIS-Comm.…”