The fleet size of a multi-robot system is an important parameter to be considered for real robotics applications since it will determine the cost and the time of execution of any given task. Unfortunately, it is a topic that has received little attention in the robotics literature. The study of the fleet size will allow for the design and implementation of more effective techniques and coordination methods for multi-robot systems. In this paper we study the effects of the fleet size on the time of exploration of a structured environment. We present an analysis that allows us to specify the maximum fleet size that provides the maximum reduction on the exploration time when the structured environment is modeled as a tree. The analysis is applied to the Multi-Robot Depth First Search (MR-DFS) algorithm that allows for maximum parallelism when an exploration process starts from a single point. The analysis provides an expression for the average time of exploration of a tree and for the maximum number of robots that produces a significant reduction on the exploration time.

In this paper, we present a multirobot exploration algorithm that aims at reducing the exploration time and to minimize the overall traverse distance of the robots by coordinating the movement of the robots performing the exploration. Modeling the environment as a tree, we consider a coordination model that restricts the number of robots allowed to traverse an edge and to enter a vertex during each step. This coordination is achieved in a decentralized manner by the robots using a set of active landmarks that are dropped by them at explored vertices. We mathematically analyze the algorithm on trees, obtaining its main properties and specifying its bounds on the exploration time. We also define three metrics of performance for multirobot algorithms. We simulate and compare the performance of this new algorithm with those of our multirobot depth first search (MR-DFS) approach presented in our recent paper and classic single-robot DFS.

The searching for a location-unknown radio transmitter is a challenging task for autonomous robot. We propose an adaptive searching algorithm named theseus gradient guide (TGG) which is designed for solving the searching problem in indoor environments using received signal strength (RSS). While the RSS gradient serves as the main guide, the robot prefers to move to the places which have never been traveled. Thus the robot will not get stuck in the local maxima. Moreover, unlike the commonly used random kick strategy the TGG drives the robot escaping the local maxima with low cost in terms of travel distance. Meanwhile, TGG is not sensitive to motion errors. Simulation results show that the searches using TGG cost much less compared with those using other gradient based methods in our testing indoor environment. Guided by TGG, the robot can successfully reach the location-unknown radio transmitter with a ratio over 97% when the standard deviation of motion error is up to 20% of the step length.

In this paper, we present an algorithm for the exploration of an unknown graph by multiple robots, which is never worse than depth-first search with a single robot. On trees, we prove that the algorithm is optimal for two robots. For k robots, the algorithm has an optimal dependence on the size of the tree but not on its radius. We believe that the algorithm performs well on any tree, and this is substantiated by simulations. For trees with e edges and radius r, the exploration time is less than 2e/k + (1 + (k/r))k-1(2/k!)rk-1= (2e/k) + O((k + r)k-1) (for r >; k,k-1), thereby improving a recent method with time O((e/logk) + r) [2], and almost reaching the lower bound max((2e/k), 2r). The model underlying undirected-graph exploration is a set of rooms connected by opaque passages; thus, the algorithm is appropriate for scenarios like indoor navigation or cave exploration. In this framework, communication can be realized by bookkeeping devices being dropped by the robots at explored vertices, the states of which are read and changed by further visiting robots. Simulations have been performed in both tree and graph explorations to corroborate the mathematical results.

In this paper we study the problem of multi-robot exploration of unknown indoor environments that are modeled as trees. Specifically, our approach consider that robots deploy and communicate with active landmarks in every intersection they encounter. We present a novel algorithm that is guaranteed to completely explore any tree with m edges and diameter D, by allowing k robots to be fed into the tree one at a time. We prove that the exploration time of the algorithm grows in linear proportion with the size of the tree and is not bigger than D+m. Simulation results are presented that corroborate the theoretical analysis.

In this paper we present an algorithm for the exploration of an unknown graph with k robots, which is guaranteed to succeed on any graph, and which on trees we prove to be near-optimal for two robots, having optimal dependence on the size of the tree but not on its radius. We believe that the algorithm performs well on any graph, and this is substantiated by simulations. For trees with n edges and radius r, the exploration time is 2n/k + O(rk-1), improving a recent method with O(n/log k + r) [1], and almost reaching the lower bound max (2n/k, 2r). The algorithm is meant to be used in indoor navigation or cave search scenarios where the environment can be modeled as a graph. In this scenario, communication is realized by the devices being dropped by the robots at explored vertices, and the states of which are read and changed by further visiting robots. Simulations on Player/Stage platform have been performed in both tree and graph exploration which corroborate the mathematical results.

Biologically-inspired swarm of robots with collaboration towards a common mission has a broad range of applications. However, the required dynamic localization among autonomous robots for such swarm collaboration, though usually implicitly assumed, has not been properly studied. In this paper, we address the roles of multiple antennas in localization and energy-efficient wireless communications for a swarm of robots. Following the gradient of signal powers along a trajectory, a robot can track the direction of a source robot. With three or more properly placed antennas that sense different phase shifts of carrier, a robot can localize a source. By lateration, three collaborative robots can localize a source with known distances to it. Via angulation technique, three robots can determine their geometric relationship with knowing two angles and one distance between them. The techniques can be extended from the 2-D to the 3-D space for application of wall-climbing robots. On the basis of knowledge of robot locations, beamforming techniques can be employed to receive and transmit signal towards the desired robot therefore improving energy efficiency and prolonging robot lifetime.

In this paper, we propose a novel approach of signal power gradient by which a robot adaptively searches a location-unknown sensor. While moving, the robot measures signal strength and estimates the direction of power gradient along which the robot moves in the next step. The correctness of estimated direction is analyzed and the probability of correct direction is obtained. Since the robot continuously measures signal strength while moving, it can effectively overcome the motion errors. Simulation results demonstrate that the robot can successfully reach the location-unknown sensor with probability close to one when the signal to noise ratio at the initial location is as low as 0 dB and the standard deviation of motion error is 10% step size.

There is a real need in the robotics and wireless sensor network (WSN) communities for the estimation of the geolocation of wireless agents. The received signal strength indicator (RSSI), a common metric in most networking hardware, has been reputed as a very unreliable method for doing the job, due to its vulnerability to environmental factors. Nevertheless, it still remains as the most prevalent estimator of distance between agents on many research projects. Multipath fading, shadowing and other effects that the environment exerts over a signal while propagating are regarded as the main cause of such vulnerability. Although some success has been obtained using RSSI outdoors where the effects are less noticeable, indoor settings remain an unconquered territory. The main motivation of this paper is to establish whether, in real time applications, the use of preprocessing techniques over partial raw collected data helps the RSSI to be a suitable estimator of distance. We propose one such technique and the results suggest that its use may indeed assist the obtainment of more accurate distance estimations while using RSSI.

The vague assumption of a fixed communication range has facilitated the development of major areas of study in mobile sensor networks such as connectivity, coverage, and self-deployment algorithms by providing a stable element as a foundation for their analysis, but more precise communication model is required in order to convert theoretical simulations into realistic applications. This paper studies the effects of more realistic communication models on mobile sensor networks/ad hoc networks taking into consideration multi-access interference and noise and their impact on the communication range of the nodes. The study shows that the communication range changes with the characteristics of the network (network parameters and node distribution). Simulation results justify our findings and future research directions are discussed