Graph based distributed control of non-holonomic vehicles endowed with local positioning information engaged in escorting missions

Abstract

Abstract-Using graph theory, this paper investigates how a group of robots, endowed with local positioning (range and bearing from other robots), can be engaged in a leaderfollowing mission whilst keeping a predefined configuration. The possibility to locally change the behaviors of the follower team to accomodate both tasks is explored. In particular, a methodology to automatically adjust the parameters of the inter-robot interactions and a nonlinear PI controller are explained and implemented. Our approach is supported by a mathematical analysis as well as real robot experiments.

Graph-based distributed control for adaptive multi-robot patrolling through local formation transformation

“…In this work, weights have been tuned according to the distance c iL between the follower R i and the leader L using functions w ff, i (c), ∀e i ∈ E f and w f l,j (c), ∀e j ∈ E f l , as proposed in [13]. If c iL is large, the weight of the follower-follower edge w ff is smaller than on the leader-follower edge w f l and the followers have a higher potential to reach the leader first.…”

A Graph-Based Formation Algorithm for Odor Plume Tracing

“…As stated above, the law is only applicable under the assumptions of holonomicity, absolute positioning, and full connectivity maintenance. The same approach can, however, be implemented in a decentralized fashion and using only relative positioning information, assuming a connected but not necessarily complete graph, and accounting for nonholonomicity either natively or by offloading to a lower level controller [6]. As previously discussed, we want the formation to be oriented with respect to the wind, requiring the rotation of the bias vectors by the measured wind speed θ .…”

“…It is based on the principle of Laplacian feedback, which has been used extensively in other contexts [22,6]. The only information required by each robot is the relative position of its neighbors, and the formation may take an arbitrary shape and dynamically change over time.…”

Edge-weighted consensus-based formation control strategy with collision avoidance

“…where K ij > 0 is a constant, α ij > 0 is a value used to define the intensity of the inter-robot influence, 20 and V min ij > 0 is defined such that…”