Swarm robotic odor localization

Hayes, A.T.; Martinoli, Alcherio; Goodman, R.M.

doi:10.1109/iros.2001.976311

Cited by 81 publications

(62 citation statements)

References 17 publications

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“…In the last few years, the swarm intelligence control principles have been successfully applied to a series of case studies in collective robotics: aggregation [2,20,21] and segregation [13], beacon and odor localization [11,12], collaborative mapping [4], collaborative transportation [15,17], work division and task allocation [1,16], and flocking and foraging [23]. All these works have been performed using groups of simple, autonomous robots or embodied simulated agents, exploiting local communication forms among teammates (implicit, through the environment, or explicit, wireless communication), and fully distributed control.…”

Section: Introductionmentioning

confidence: 99%

“…All these works have been performed using groups of simple, autonomous robots or embodied simulated agents, exploiting local communication forms among teammates (implicit, through the environment, or explicit, wireless communication), and fully distributed control. Sometimes, due to technical difficulties in experimentation with real robots, local explicit communication [4,12,23] or specific environmental information (e.g., nest energy in [16]) has been obtained with the help of absolute positioning systems and/or global communication. While global communication capabilities, if used extensively, represent a bottleneck for the scalability of the collective system, global positioning systems (GPSs), depending on their specific implementation, can achieve performances independent of the team size (e.g., GPS or the system used in [4]) and, therefore, represent suitable technical aids for applying the swarm intelligence approach to artificial systems.…”

Section: Introductionmentioning

confidence: 99%

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A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems

et al. 2001

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In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots,11,. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems

et al. 2001

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“…This embodied simulator has previously been shown to generate data that closely matches real robot experiments [7], [8], so there is reason to believe that the results are representative of a real system.…”

Section: Webotsmentioning

confidence: 81%

How Many Robots? Group Size and Efficiency in Collective Search Tasks

Hayes

2002

Distributed Autonomous Robotic Systems 5

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Abstract. This paper presents a quantitative analysis of the tradeoffs between group size and efficiency in collective search tasks that considers both the timesensitive nature of search completion and the system operating cost. First, the search task is defined and a performance metric is presented that can account for all of the costs associated with the task. Next, for both random and coordinated search strategies, analytical expressions are derived that can be used to predict optimal system performance bounds given a particular task description, and the performance benefit of using coordinated search is shown to be dependent on the relative values of the different cost components. Finally, an embodied computer simulation is used to support the analytical results, suggesting that the assumptions involved in their derivation are sound.

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“…Simultaneous distributed sensing delivers high flexibility in placing the sensors according to the configuration of the search space, thus improving search efficiency. A good example of this type of situation is given by Hayes et al (Hayes et al, 2001) where a very difficult search task, plume tracing, is performed using a swarm of robots equipped with odor sensors.…”

Section: Versatilitymentioning

confidence: 99%

Swarm-Bot: A New Distributed Robotic Concept

Pettinaro²,

et al. 2004

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Abstract. The swarm intelligence paradigm has proven to have very interesting properties such as robustness, flexibility and ability to solve complex problems exploiting parallelism and self-organization. Several robotics implementations of this paradigm confirm that these properties can be exploited for the control of a population of physically independent mobile robots.The work presented here introduces a new robotic concept called swarm-bot in which the collective interaction exploited by the swarm intelligence mechanism goes beyond the control layer and is extended to the physical level. This implies the addition of new mechanical functionalities on the single robot, together with new electronics and software to manage it. These new functionalities, even if not directly related to mobility and navigation, allow to address complex mobile robotics problems, such as extreme all-terrain exploration.The work shows also how this new concept is investigated using a simulation tool (swarmbot3d) specifically developed for quickly designing and evaluating new control algorithms. Experimental work shows how the simulated detailed representation of one s-bot has been calibrated to match the behaviour of the real robot.

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Swarm robotic odor localization

Cited by 81 publications

References 17 publications

A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems

A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems

How Many Robots? Group Size and Efficiency in Collective Search Tasks

Swarm-Bot: A New Distributed Robotic Concept

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