Tracking multiple moving targets with swarms of mobile robots

Abstract

This paper presents a distributed approach to enable mobile robot swarms to track multiple targets moving unpredictably. The proposed approach consists of two constituent algorithms: local interaction and target tracking. When the robots are faster than the targets, Lyapunov theory can be applied to show that the robots converge asymptotically to each vertex of the desired equilateral triangular configurations while tracking the targets. Toward practical implementation of the algorithms, it is important to realize the observation capability of individual robots in an inexpensive and efficient way. A new proximity sensor that we call dual rotating infrared (DRIr) sensor is developed to meet these requirements. Both our simulation and experimental results employing the proposed algorithms and DRIr sensors confirm that the proposed distributed multi-target tracking method for a swarm of robots is effective and easy to implement.

Research on cooperative area search of multiple underwater robots based on the prediction of initial target information

“…https://doi.org/10.1016/j.oceaneng.2018.12.035 Received 14 September 2018; Received in revised form 12 November 2018; Accepted 13 December 2018 However, solving the search problem of the dynamic target is still immature, and further research is needed (Newstadt et al, 2015;Esmailifar and Saghafi, 2015;Li et al, 2017;Lee et al, 2010). In the search process of multiple UAVs (unmanned aerial vehicles), the rolling time domain method is applied to the target search in the literature (Lanillos et al, 2014;Polycarpout et al, 2001;Xiao et al, 2012;Hu et al, 2014;Bertuccelli and How, 2006;Trodden and Richards, 2008).…”

Self-organized Fission Control Algorithm for Flocking System with Time Delay

“…It is widely observed in nature, e.g., a flock of birds or a school of fish may split to evade the attack from predators [2], a herd of sheep can break up into smaller foraging groups for grass eating on the prairie [1] and so forth. In engineering applications, fission behavior is also of great significance in such occasions as obstacle/ danger avoidance for swarm UAVs (unmanned aerial vehicles) [3], multi-site surveillance for mobile sensor networks [4] and multi-target tracking for mobile robot groups [5].…”

“…Chen et al [11] studied the aggregation and splitting problem of flocking system via a medium range repulsion force. In addition, some multi-target tracking algorithms can also achieve the fission behavior by predefining the corresponding objectives to individuals [5][12] [13]. However, these works either depend on explicit identification [10] or intelligent coordination mechanisms such as negotiation, appointment or centralized control [12], which may not be well consistent with the characteristics of fission behavior.…”

Self-Organized Fission Control for Flocking System

“…At present, literatures that address the fission control problem seem diverse. By predefining the leaders/targets to different individuals, fission behavior emerged in the multiobjective tracking process [14][15][16]; in [17], Kumar et al assigned different coupling strength to heterogeneous robot swarm that leads weak coupling robots to separate and the strong coupling robots form clusters. In addition, a long range attractive, short range repulsive interaction as well as an intermediate range Gauss-shaped interaction was employed for flock aggregation and splitting in [18].…”