Cooperative Distributed Source Seeking by Multiple Robots: Algorithms and Experiments

Abstract

We consider the problem of source seeking using a group of mobile robots equipped with sensors for source concentration measurement. In the formulation, the robot team cooperatively estimates the gradient of the source field, moves to the source by tracing the gradient-ascending direction, and keeps a predefined formation in movement. We present two control algorithms with all-to-all and limited communications, respectively. For the case of all-to-all communication, rigorous analytic analysis proves that the formation center of the robots converges to the source in the presence of estimation errors with a bounded error, the upper bound of which is explicitly given. In the case of limited communication where centralized quantities are not available, distributed consensus filters are used to distributively estimate the centralized quantities, and then embedded in the distributed control laws. Numerical simulations are given to validate the effectiveness of the proposed approaches. Experimental results on the E-puck robot platform demonstrate satisfactory performances in a light source seeking application.

Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments

“…Assuming the current list objects' set , then the calculation time from the current location of mobile robot to each available landmark within SAR exploration area ( ) is represented in (7), and the measurement distances ( ) between mobile robot and the objects of interest are expressed as (8), respectively,…”

“…Multiple robots cooperatively acquired the surrounding's information in wide research work [1,[6][7][8][9], and an integration strategy of different kinds of measurements was used in multimode sense of 3D perception. Pfingsthorn et al [7] updated the local submaps through their graph-based structures, and made it be efficiently communicated among mobile robots' sensors.…”

“…Pfingsthorn et al [7] updated the local submaps through their graph-based structures, and made it be efficiently communicated among mobile robots' sensors. Using a group of mobile robots equipped with sensors for source concentration measurement, the mobile robots team cooperatively estimates the gradient of the exploration field, moves to the objects of interest, and keeps a predefined formation in movement [8]. Atanasov et al [9] developed an acquisition method of multisensors active information capturing the common characteristics of these scenarios and controlling mobile sensing systems to improve the accuracy and efficiency of collecting information autonomously.…”

Circular Formation Control of Multiagent Systems with Any Preset Phase Arrangement

“…Imitating the collective behaviors that occur in nature, the distributed control of multiagent systems (MAS), such as multiple autonomous underwater vehicles (AUVs) and unmanned aerial vehicles (UAVs) [1][2][3], has attracted a great deal of attention in control and robotic communities [4,5] and has been extensively explored with different settings, including consensus [6], formation control [7,8], flocking [9], distributed sensor networks [10], rendezvous [11], and source seeking [12,13], through coordinating multiple autonomous mobile agents.…”

Distributed Source Seeking via a Circular Formation of Agents Under Communication Constraints

“…In [15] a distributed source seeking algorithm is proposed using optimization techniques. A source seeking algorithm based on consensus filters is presented in [16] to deal with limited communication. The main drawback of these works is that the spatial propagation of the signal is assumed to be quadratic or concave.…”