Optimal dynamic formation control of multi-agent systems in constrained environments

Abstract

We address the optimal dynamic formation problem in mobile leader-follower networks where an optimal formation is generated to maximize a given objective function while continuously preserving connectivity. We show that in a convex mission space, the connectivity constraints can be satisfied by any feasible solution to a mixed integer nonlinear optimization problem (MINLP). For the class of optimal formation problems where the objective is to maximize coverage, we show that the optimal formation is a tree which can be efficiently constructed without solving a MINLP. In a mission space constrained by obstacles, we separate the formation process into intervals with no obstacles detected and intervals where one or more obstacles are detected. In the latter case, we propose a minimum-effort reconfiguration approach for the formation which still optimizes the objective function while avoiding the obstacles and ensuring connectivity. We include simulation results illustrating this dynamic formation process.

Analysis of path following and obstacle avoidance for multiple wheeled robots in a shared workspace

“…that yields, for each given σ , a lower bound on A j . Notice that, if we make the a priori choice A j < 0, it is convenient to focus on (13), which guarantees the properties (1) and (2), whereas (12) does not. After some computations, this finally requires to satisfy a condition similar to (16), but with the opposite inequality.…”

“…However, in real world scenarios, assuming that the environment is static is unrealistic in most cases, and the environment should more realistically be modelled as dynamic: therefore, in the field of mobile robotics, the problem of safe navigation in dynamic environments is one of the most important challenges to be addressed [9,10]. The challenge becomes more complex and tough when the information about the dynamic obstacles and the environment is not available, even if such information is often assumed to be present in many research works [11,12,13,14,15]. This information may include the complete map of the environments, the position and the orientation of the obstacles in the map, the nature of the obstacles (whether the shape of the obstacles is constant or varies over time) and the motion of the obstacles (whether the obstacle is moving with a constant or time-varying velocity).…”

Multi-Agent Trajectory-Tracking Flexible Formation via Generalized Flocking and Leader-Average Sliding Mode Control

“…are numerous papers related to formation control, regarding various types of networked plants [3], [7], [8], [11], [14], [23]- [25], [33]- [35], [40], [41], [47], [52], [53], [58]. Formation control with targeting and/or trajectory tracking is discussed in [26].…”

Fuzzy adaptive leader‐following consensus of second‐order multiagent systems with imprecise communication topology structure

“…Multiagent systems (MAS), or networked cooperative systems, have become an attractive and active research topic in the past decade or so. Nowadays, MAS have wide applications in various fields such as formation control, flocking, and complex networks . In multiagent control community, the consensus is one of the most fundamental research topics, whose goal is to make multiple agents ultimately achieve common values by designing appropriate distributed controllers .…”