Other auction-based techniques have also been applied in [6]. Such thereby methods are likely to show similar performance to the most popular and efficient utilities approach.The utilities approach was introduced by Burgard et al. in [9]. The essence is to compute a distance cost towards each frontier and then update the utilities of neighboring frontiers according to greedy assignments of the teammates. This work has been enhanced by using machine learning such as clustering frontiers [11] and learning typical indoor environments [2,14] for more certain mapping and better goal point assignment. The best work in terms of efficient exploration and algorithm complexity reports an O(n2T) for greedy frontier allocation for n robots and T frontiers, and optimal allocation with up to O(T!(T?n)!), without considering any additional processing [1].
On the other hand, some works have focused on determining costs that try to minimize the uncertainty of the process. In [16] the idea is to keep the robots in line of sight so that they can help each other to accurately localize while each one navigates in a round-robin process. If robots tend to go out of range, then a rendezvous is scheduled. In [15], the goal is to assign targets that are estimated to reduce the odometry error. Balch and Arkin in [19] propose an anchored random exploration such that every robot remains inside communication range. Nevertheless, even when these works have similar results to the utilities technique and have the advantage of improving the overall accuracy of the map, they have the disadvantage of keeping the robots in close proximity, which can increase the exploration time by preventing the robots from efficiently spreading.
Other works include [8,24] in which they leverage the work in [9] by including communication limits in the cost Entinostat function and allocating frontiers within a bidding process. Also, in [17], an inside-communication technique demonstrates similar results to the work in [19]. Moreover, with the same rendezvous idea of [16], researchers propose in [18] a role-based technique for allowing robots to explore distant areas
Methane is the main component of coal mine gas and natural gas, and it is closely connected with the people’s daily activities and life. Since methane gas is inflammable and explosive, it is important to accurately detect the concentration of methane gas. Methane detectors may be classified into two categories based on their applications. One is mainly used to alarm for the explosion of methane. Since the low explosive level (LEL) concentration of methane is 5%, detectors for this purpose usually do not require a high Wortmannin side effects resolution or a very low detection limit. The other is focused on detecting very low concentrations of methane (ppm level).