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

Abstract

Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.

Xiroi II, an Evolved ASV Platform for Marine Multirobot Operations

“…Coordination and Cooperation of Multirobot Systems (MRS) are widely studied topics in the field of robotics [ 50 , 51 , 52 , 53 ]. As can be seen from the literature cited above, the MRS coordination strategies share many challenges.…”

Non-Monotone Energy-Aware Information Gathering for Heterogeneous Robot Teams

“…Methods in the second category adopt a hierarchical structure that separates planning and control, where safety is only handled in low-level controller (e.g., [40], [41]). As a result, existing safe multi-robot controllers can be utilized (see [42], [43] for comprehensive surveys) without affecting the problem structure in planning. The last category involves the joint optimization of information gathering objectives and safety, by capturing collision avoidance as constraints or penalties, without necessarily assuming submodularity and monotonicity, where typical examples include [44]- [47].…”

Hybrid Path Planning Model for Multiple Robots Considering Obstacle Avoidance

“…Parallel working groups for mobile robots offer a large number of benefits compared to single robot systems. To perform a large number of various tasks with sufficient robustness, teams of robots are used instead of a single highly specialized robot [14]. In order to improve the autonomy and intelligence of the robot, most algorithms focus on improving the autonomous learning capability of the robot [15].…”