Multi-robot olfactory search in structured environments

Abstract

This paper presents a cooperative distributed approach for searching odor sources in unknown structured environments with multiple mobile robots . While searching and exploring the environment, the robots independently generate on-line local topological maps and by sharing them with each other they construct a global map. The proposed method is a decentralized frontier based algorithm enhanced by a cost/utility evaluation function that considers the odor concentration and airflow at each frontier. Therefore, frontiers with higher probability of containing an odor source will be searched and explored first. The method also improves path planning of the robots for exploration process by presenting a priority policy. Since there is no global positioning system and each robot has its own coordinate reference system for its localization, this paper uses topological graph matching techniques for map merging. The proposed method was tested in both simulation and real world environments with different number of robots and different scenarios. The search time, exploration time, complexity of the environment and number of double-visited map nodes were investigated in the tests. The experimental results validate the functionality of the method in different configurations.

Robotic clusters: Multi-robot systems as computer clusters

“…While most research on multi-robot mapping has addressed the problem by creation of occupancy grid maps ( [63], [64], [65]), some research has been done on feature based or topological maps ( [3], [8], [66], [67], [68]). Jennings et al [69] used individual robots to create topological partial maps, and then used a simple distance metric to merge the maps considering a global reference frame for all robots.…”

“…For example, multiple robots can localize themselves more efficiently [1], fulfill search and exploration missions faster [2], [3], [4], [5], and generate maps of unknown environments more accurately [6]. As argued in [7], performance/cost ratio is the main advantage of multi-robot systems over singlerobot systems.…”

“…Clusters are usually deployed to improve performance and availability, while typically being much more cost-efficient than single computers of comparable speed or availability. The advantage of clusters over other traditional platforms were proved by several academic projects such as Beowulf 2 , Berkeley NOW [42], NetSolve [43] and HPVM 3 . These advantages include low-entry costs to access supercomputing-level performance, an incrementally upgradeable system, an open source development platform, and not being locked into particular vendor products.…”

Towards 3-D distributed odor source localization: An extended graph-based formation control algorithm for plume tracking

“…Leveraging multiple robots for distributed search has its own peculiarities, many of which have been explored during the last decade. Multirobot search algorithms proposed in the literature include biasing expansion swarm approaches [11], biased random walk [12], particle swarm optimization [13], gradient climbing techniques [14], infotaxis [15], probabilistic reasoning [16], search through exploration [17], physics-based swarming [18], attraction-repulsion swarming [19], and formation-based algorithms [20], [21].…”

Optimal Swarm Formation for Odor Plume Finding

“…Bio-inspired [16], [17], concentration gradient climbing (chemotaxis) and up-wind directed search (anemotaxis [19], [24], [25]) are the most common approaches to track odor plumes by mobile robots. Several other methods have been proposed for plume tracking using swarm robotic concepts, namely, biasing expansion swarm approaches (BESA) [26], biased random walk (BRW), evolutionary strategies [27], particle swarm optimization (PSO) [28]- [30], glowworm swarm optimization (GSO) [31], gradient climbing techniques, swarm spiral surge [32], physics-based swarming approach [33], and attraction/repulsion forces [15].…”