Swarm SLAM: Challenges and Perspectives

A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems—i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints

Automatic design of ant-miner mixed attributes for classification rule discovery

Ant-Miner Mixed Attributes (Ant-MinerMA) was inspired and built based on ACOMV. which uses an archive-based pheromone model to cope with mixed attribute types. On the one hand, the use of an archive-based pheromone model improved significantly the runtime of Ant-MinerMA and helped to eliminate the need for discretisation procedure when dealing with continuous attributes. On the other hand, the graph-based pheromone model showed superiority when dealing with datasets containing a large size of attributes, as the graph helps the algorithm to easily identify good attributes. In this paper, we propose an automatic design framework to incorporate the graph-based model along with the archive-based model in the rule creation process. We compared the automatically designed hybrid algorithm against existing ACO-based algorithms: one using a graph-based pheromone model and one using an archive-based pheromone model. Our results show that the hybrid algorithm improves the predictive quality over both the base archive-based and graph-based algorithms

MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework

International audienceAutomated algorithm configuration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, these configuration procedures were limited to optimising a single performance objective, such as the running time or solution quality achieved by the algorithm being configured. However, in many applications there is more than one performance objective of interest. This gives rise to the multi-objective automatic algorithm configuration problem, which involves finding a Pareto set of configurations of a given target algorithm that characterises trade-offs between multiple performance objectives. In this work, we introduce MO-ParamILS, a multi-objective extension of the state-of-the-art single-objective algorithm configuration framework ParamILS, and demonstrate that it produces good results on several challenging bi-objective algorithm configuration scenarios compared to a base-line obtained from using a state-of-the-art single-objective algorithm configurator

Comprehensive study of the reactions induced by 12C on 103Rh up to 33 MeV/nucleon

Abstract Fifty-three excitation functions for the production of radioactive residues in the interaction of 12C with 103Rh have been measured from the Coulomb barrier up to 400 MeV by means of the activation technique. These excitation functions have been analyzed considering complete fusion, incomplete fusion of 8Be and α-particle fragments and, above about 200 MeV, the transfer of either one proton or one neutron from 12C to 103Rh. The emission of pre-equilibrium particles during the thermalization of the excited composite nuclei formed in all these processes and, in the case of 8Be and α incomplete fusion, also the re-emission of α-particles after a mean-field interaction or a few interactions with the target nucleons have been taken into account

An Empirical Study of Off-line Configuration and On-line Adaptation in Operator Selection

Automating the process of finding good parameter settings is important in the design of high-performing algorithms. These automatic processes can generally be categorized into off-line and on-line methods. Off-line configuration consists in learning and selecting the best setting in a training phase, and usually fixes it while solving an instance. On-line adaptation methods on the contrary vary the parameter setting adaptively during each algorithm run. In this work, we provide an empirical study of both approaches on the operator selection problem, explore the possibility of varying parameter value by a non-adaptive distribution tuned off-line, and incorporate the off-line with on-line approaches. In particular, using an off-line tuned distribution to vary parameter values at runtime appears to be a promising idea for automatic configuration. © 2014 Springer International Publishing.SCOPUS: cp.kinfo:eu-repo/semantics/publishe