1,720,993 research outputs found
Adaptive Strategies for Team Formation in Minimalist Robot Swarms
No full textThe formation of collaborative robotic teams for task execution often requires coordination in both space and time, with robots gathering in close vicinity and concurrently executing the task. For decentralised robotic swarms constituted of minimalist agents unable to communicate and plan ahead, a probabilistic approach might ensure that tasks are executed at the maximum possible rate by means of opportunistic team formation. We consider here the case of strictly-collaborative tasks of two typeseasy and hardeach requiring a specific number of agents to concurrently work in the same area. We show how task execution can be improved by adaptive behavioural strategies that (i) change the random movements of robots to bias their distribution towards areas where hard tasks are present, and (ii) specialise the robots behaviour to facilitate the formation of teams tailored to the one or the other task type. Experiments with simulated and real swarms of Kilobotsdemonstrate the suitability of the proposed approach, opening to future applications in micro/nano-robotics
Self-organizing strategy design for heterogeneous coexistence in the Sub-6 GHz
No full textDue to the worldwide ongoing pressure to massively exploit the Sub-6 GHz spectrum for the deployment of independently-operated and heterogeneous networks, innovative solutions for network coexistence are deeply required. Hence, in this paper, we design a self-organizing strategy with the aim of minimizing the coexistence interference among heterogeneous networks sharing the Sub-6 GHz spectrum. The design is performed under the constraints of promoting selfless network utilization and avoiding any direct communication among the heterogeneous networks. For this, we develop an analytical framework, grounded on the nest-site selection behavior observed in honeybee swarms, to model the coexistence problem among multiple heterogeneous networks. Specifically, first, different heterogeneous networks are mapped into different populations and the allocation of a Sub-6 GHz band to a network is mapped into the population commitment. Then, the evolution of the commitment process is described through a multi-dimensional differential system. We analytically study the stability of such a system at the equilibrium, and we derive the conditions that assure the optimal allocation of the available Sub-6 GHz bands among the different heterogeneous networks. Finally, the proposed strategy is validated through an extensive performance evaluation
Best-of-N Collective Decisions on a Hierarchy
No full textThe best-of-N problem in collective decision making is complex especially when the number of available alternatives is larger than a few, and no alternative distinctly shines over the others. Additionally, if the quality of the available alternatives is not a priori known and noisy, errors in the quality estimation may lead to the premature selection of sub-optimal alternatives. A typical speed-accuracy trade-off must be faced, which is hardened by the presence of several alternatives to be analyzed in parallel. In this study, we transform a one-shot best-of-N decision problem in a sequence of simpler decisions between a small number of alternatives, by organizing the decision problem in a hierarchy of choices. To this end, we construct an m-ary tree where the leaves represent the available alternatives, and high-level nodes group the low-level ones to present a low-dimension decision problem. Results from multi-agent simulations in both a fully-connected topology and in a spatial decision problem demonstrate that the sequential collective decisions can be parameterized to maximize speed and accuracy against different decision problems. A further improvement relies on an adaptive approach that automatically tunes the system parameters
Field coverage and weed mapping by UAV swarms
Full text linkThe demands from precision agriculture (PA) for high-quality information at the individual plant level require to re-think the approaches exploited to date for remote sensing as performed by unmanned aerial vehicles (UAVs). A swarm of collaborating UAVs may prove more efficient and economically viable compared to other solutions. To identify the merits and limitations of a swarm intelligence approach to remote sensing, we propose here a decentralised multi-agent system for a field coverage and weed mapping problem, which is efficient, intrinsically robust and scalable to different group sizes. The proposed solution is based on a reinforced random walk with inhibition of return, where the information available from other agents (UAVs) is exploited to bias the individual motion pattern. Experiments are performed to demonstrate the efficiency and scalability of the proposed approach under a variety of experimental conditions, accounting also for limited communication range and different routing protocols. © 2017 IEEE
Summary: Distributed task assignment and path planning with limited communication for robot teams
Full text linkWe consider multi-robot service scenarios, where tasks appear at any time and in any location of the working area. A solution to such a service task problem requires finding a suitable task assignment and a collision-free trajectory for each robot of a multi-robot team. In cluttered environments, such as indoor spaces with hallways, those two problems are tightly coupled. We propose a decentralized algorithm for simultaneously solving both problems, called Hierarchical Task Assignment and Path Finding (HTAPF). HTAPF extends a previous bio-inspired Multi-Robot Task Allocation (MRTA) framework [1], In this work, task allocation is performed on a arbitrarily deep hierarchy of work areas and is tightly coupled with a fully distributed version of the priority-based planning paradigm [12], using only broadcast communication. Specifically, priorities are assigned implicitly by the order in which data is received from nearby robots. No token passing procedure or specific schedule is in place ensuring robust execution also in the presence of limited probabilistic communication and robot failures
Hierarchical task assignment and path finding with limited communication for robot swarms
Full text linkComplex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures
Dynamic UAV swarm deployment for non-uniform coverage: Robotics track
No full textIn many monitoring and mapping applications, high-resolution data are required only in certain areas while others can receive lower attention. To this end, unmanned aerial vehicles (UAVs) can adjust the flight altitude to increase the resolution only where needed, making non-uniform coverage strategies efficient both in time and energy expenditure. In a multi-UAV monitoring context, it is nece ssary to deploy UAVs to inspect in parallel those areas where a higher resolution is required. To address this problem, we propose a decentralised deployment strategy inspired by the collective beh aviour of honeybees. This strategy dynamically assigns UAVs to different areas to be monitored, and suitably re-assigns them to other areas when needed. We introduce an analytical macroscopic model of area monitoring from UAVs. and we propose a paramet erisation that leads to an efficient allocation of UAVs to the areas to be monitored. We exploit abstract multi-agent simulations to study the dynamics of the deployment of UAVs to multiple areas, and we present results with simulations of a UAV swarm engaged in a weed monitoring and mapping task. © 2018 International Foundation for Autonomous Agents and Multiagent Systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Field coverage for weed mapping: Toward experiments with a UAV swarm
No full textPrecision agriculture represents a very promising domain for swarm robotics, as it deals with expansive fields and tasks that can be parallelised and executed with a collaborative approach. Weed monitoring and mapping is one such problem, and solutions have been proposed that exploit swarms of unmanned aerial vehicles (UAVs). With this paper, we move one step forward towards the deployment of UAV swarms in the field. We present the implementation of a collective behaviour for weed monitoring and mapping, which takes into account all the processes to be run onboard, including machine vision and collision avoidance. We present simulation results to evaluate the efficiency of the proposed system once that such processes are considered, and we also run hardware-in-the-loop simulations which provide a precise profiling of all the system components, a necessary step before final deployment in the field
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