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        <title>Frontiers in Robotics and AI | Multi-Robot Systems section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/robotics-and-ai/sections/multi-robot-systems</link>
        <description>RSS Feed for Multi-Robot Systems section in the Frontiers in Robotics and AI journal | New and Recent Articles</description>
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        <pubDate>2026-05-14T22:18:08.776+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2026.1731740</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2026.1731740</link>
        <title><![CDATA[Control flow graph based code optimization using graph neural networks]]></title>
        <pubdate>2026-03-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Melih Peker</author><author>Ozcan Ozturk</author>
        <description><![CDATA[Selecting a good set of optimization flags requires extensive effort and expert input. While most of the prior research considers using static, spatial, or dynamic features, some of the latest research directly applied deep neural networks to source code. We combined the static features, spatial features, and deep neural networks by representing source code as graphs and trained Graph Neural Network for automatically finding suitable optimization flags. We created a dataset of 12000 graphs using 256 optimization flag combinations on 47 benchmarks. We trained and tested our model using these benchmarks, and our results show that we can achieve a maximum of 48.6% speed-up compared to the case where all optimization flags are enabled.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2026.1770121</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2026.1770121</link>
        <title><![CDATA[When AI takes the wheel: AI-defined vehicles principles and pitfalls]]></title>
        <pubdate>2026-03-02T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Marco De Vincenzi</author><author>Chiara Bodei</author><author>Ilaria Matteucci</author>
        <description><![CDATA[As introduced by Asimov in “I, Robot”, intelligent machines are characterized as systems capable of performing tasks that traditionally require human intelligence, such as autonomous decision-making and driving. In this context, modern road vehicles can increasingly be understood as robotic systems endowed with progressively sophisticated functionalities, operational flexibility, and, crucially, the capacity to learn and evolve autonomously over time. Building on this perspective, AI-defined vehicles (AIDVs) are emerging in both the automotive industry and the research community as a next stage in vehicle evolution, where interaction capabilities, adaptability, sustainability, and ethical governance are embedded as core design principles rather than treated as auxiliary features. This work aims to introduce this new class of vehicles and provide an analysis of their defining principles, capabilities, and challenges. This article contributes a first conceptualization of AIDVs, outlines their defining principles, and distinguishes them from existing vehicle classes. Then, it identifies the risks introduced by adaptive AI and proposes a preliminary roadmap for their integration into Intelligent Transportation Systems (ITS).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2025.1671952</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2025.1671952</link>
        <title><![CDATA[Assessing the impact of feature communication in swarm perception for people re-identification]]></title>
        <pubdate>2025-12-02T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Miquel Kegeleirs</author><author>Ilyes Gharbi</author><author>Marios Kaplanis</author><author>Lorenzo Garattoni</author><author>Gianpiero Francesca</author><author>Mauro Birattari</author>
        <description><![CDATA[Swarm perception enables a robot swarm to collectively sense and interpret the environment by integrating sensory inputs from individual robots. In this study, we explore its application to people re-identification, a critical task in multi-camera tracking scenarios. We propose a decentralized, feature-based perception method that allows robots to re-identify people across different viewpoints. Our approach combines detection, tracking, re-identification, and clustering algorithms, enhanced by a model trained to refine extracted features. Robots dynamically share and fuse data in a decentralized manner, ensuring that collected information remains up to date. Simulation results, measured by the cumulative matching characteristics (CMC) curve, mean average precision (mAP), and average cluster purity, show that decentralized communication significantly improves performance, enabling robots to outperform static cameras without communication and, in some cases, even centralized communication. Furthermore, the findings suggest a trade-off between the amount of data shared and the consistency of the Re-ID.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2025.1648309</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2025.1648309</link>
        <title><![CDATA[GNV2-SLAM: vision SLAM system for cowshed inspection robots]]></title>
        <pubdate>2025-09-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xinwu Du</author><author>Tingting Li</author><author>Xin Jin</author><author>Xiufang Yu</author><author>Xiaolin Xie</author><author>Chenglin Zhang</author>
        <description><![CDATA[Simultaneous Localization and Mapping (SLAM) has emerged as one of the foundational technologies enabling mobile robots to achieve autonomous navigation, garnering significant attention in recent years. To address the limitations inherent in traditional SLAM systems when operating within dynamic environments, this paper proposes a new SLAM system named GNV2-SLAM based on ORB-SLAM2, offering an innovative solution for the scenario of cowshed inspection. This innovative system incorporates a lightweight object detection network called GNV2 based on YOLOv8. Additionally, it employs GhostNetv2 as backbone network. The CBAM attention mechanism and SCDown downsampling module were introduced to reduce the model complexity while ensuring detection accuracy. Experimental results indicate that the GNV2 network achieves excellent model compression effects while maintaining high performance: mAP@0.5 increased by 1.04%, reaching a total of 95.19%; model parameters were decreased by 41.95%, computational cost reduced by 36.71%, and the model size shrunk by 40.44%. Moreover, the GNV2-SLAM system incorporates point and line feature extraction techniques, effectively mitigate issues reduced feature point extraction caused by excessive dynamic targets or blurred images. Testing on the TUM dataset demonstrate that GNV2-SLAM significantly outperforms the traditional ORB-SLAM2 system in terms of positioning accuracy and robustness within dynamic environments. Specifically, there was a remarkable reduction of 96.13% in root mean square error (RMSE) for absolute trajectory error (ATE), alongside decreases of 88.36% and 86.19% for translation and rotation drift in relative pose error (RPE), respectively. In terms of tracking evaluation, GNV2-SLAM successfully completes the tracking processing of a single frame image within 30 ms, demonstrating expressive real-time performance and competitiveness. Following the deployment of this system on inspection robots and subsequent experimental trials conducted in the cowshed environment, the results indicate that when the robot operates at speeds of 0.4 m/s and 0.6 m/s, the pose trajectory output by GNV2-SLAM is more consistent with the robot's actual movement trajectory. This study systematically validated the system's significant advantages in target recognition and positioning accuracy through experimental verification, thereby providing a new technical solution for the comprehensive automation of cattle barn inspection tasks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2025.1607978</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2025.1607978</link>
        <title><![CDATA[Towards applied swarm robotics: current limitations and enablers]]></title>
        <pubdate>2025-06-13T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Miquel Kegeleirs</author><author>Mauro Birattari</author>
        <description><![CDATA[Swarm robotics addresses the design, deployment, and analysis of large groups of robots that collaborate to perform tasks in a decentralized manner. Research in this field has predominantly relied on simulations or small-scale robots with limited sensing, actuation, and computational capabilities. Consequently, despite significant advancements, swarm robotics has yet to see widespread commercial or industrial application. A major barrier to practical deployment is the lack of affordable, modern, and robust platforms suitable for real-world scenarios. Moreover, a narrow definition of what swarm robotics should be has restricted the scope of potential applications. In this paper, we argue that the development of more advanced robotic platforms—incorporating state-of-the-art technologies such as SLAM, computer vision, and reliable communication systems—and the adoption of a broader interpretation of swarm robotics could significantly expand its range of applicability. This would enable robot swarms to tackle a wider variety of real-world tasks and integrate more effectively with existing systems, ultimately paving the way for successful deployment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2025.1537101</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2025.1537101</link>
        <title><![CDATA[AcoustoBots: A swarm of robots for acoustophoretic multimodal interactions]]></title>
        <pubdate>2025-05-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Narsimlu Kemsaram</author><author>James Hardwick</author><author>Jincheng Wang</author><author>Bonot Gautam</author><author>Ceylan Besevli</author><author>Giorgos Christopoulos</author><author>Sourabh Dogra</author><author>Lei Gao</author><author>Akin Delibasi</author><author>Diego Martinez Plasencia</author><author>Orestis Georgiou</author><author>Marianna Obrist</author><author>Ryuji Hirayama</author><author>Sriram Subramanian</author>
        <description><![CDATA[IntroductionAcoustophoresis has enabled novel interaction capabilities, such as levitation, volumetric displays, mid-air haptic feedback, and directional sound generation, to open new forms of multimodal interactions. However, its traditional implementation as a singular static unit limits its dynamic range and application versatility.MethodsThis paper introduces “AcoustoBots” — a novel convergence of acoustophoresis with a movable and reconfigurable phased array of transducers for enhanced application versatility. We mount a phased array of transducers on a swarm of robots to harness the benefits of multiple mobile acoustophoretic units. This offers a more flexible and interactive platform that enables a swarm of acoustophoretic multimodal interactions. Our novel AcoustoBots design includes a hinge actuation system that controls the orientation of the mounted phased array of transducers to achieve high flexibility in a swarm of acoustophoretic multimodal interactions. In addition, we designed a BeadDispenserBot that can deliver particles to trapping locations, which automates the acoustic levitation interaction.ResultsThese attributes allow AcoustoBots to independently work for a common cause and interchange between modalities, allowing for novel augmentations (e.g., a swarm of haptics, audio, and levitation) and bilateral interactions with users in an expanded interaction area.DiscussionWe detail our design considerations, challenges, and methodological approach to extend acoustophoretic central control in distributed settings. This work demonstrates a scalable acoustic control framework with two mobile robots, laying the groundwork for future deployment in larger robotic swarms. Finally, we characterize the performance of our AcoustoBots and explore the potential interactive scenarios they can enable.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2025.1499215</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2025.1499215</link>
        <title><![CDATA[A comprehensive perspective on electric vehicles as evolutionary robots]]></title>
        <pubdate>2025-02-17T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Haoyang Che</author><author>Shaolin Wang</author><author>Lei Yao</author><author>Ying Gu</author>
        <description><![CDATA[Multi-robot systems exhibit different application forms in human life, among these, electric vehicles (EVs) at rest and in motion can be perceived as a specialized category of multi-robot systems with increasingly sophisticated vehicle functions and a certain degree of flexibility, and most notably, the ability to iteratively evolve. However, for EVs to evolve into the next-generation of multi-robot systems, more complex technical and operational mechanisms shall be fully cultivated in EVs to develop their evolutionary capabilities, including, but not limited to multimodal environmental sensing techniques, advanced telematics communication protocols such as 5G, Over-The-Air (OTA) upgrade functions, real-time backend data lake analytics, and user-centric marketing initiatives. As it stands, these mechanisms are evidently insufficient for realizing genuine evolutionary robots (ER), especially in unstructured environments. The overarching perspective of conceptualizing EV as ER is not always prominently featured in academic literature. This manuscript provides a succinct overview of the ongoing transition from Software-Defined Vehicles (SDV) to Artificial Intelligence-Defined Vehicles (AIDV), and examines the ongoing research focused on the utilization of electric vehicles as mobile edge computing platforms. Furthermore, it discusses the fundamental evolutionary competencies that define modern electric vehicles, establishing the core tenets upon which our analysis is predicated. To transcend the status quo, we underscore the imperative and pressing need for profound transformations across a spectrum of pivotal domains within the field. Furthermore, this endeavor aims to amplify the reach and influence of research on EVs as ERs, potentially catalyzing the emergence of several niche research areas.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2024.1426282</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2024.1426282</link>
        <title><![CDATA[Heterogeneous foraging swarms can be better]]></title>
        <pubdate>2025-01-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gal A. Kaminka</author><author>Yinon Douchan</author>
        <description><![CDATA[IntroductionInspired by natural phenomena, generations of researchers have been investigating how a swarm of robots can act coherently and purposefully, when individual robots can only sense and communicate with nearby peers, with no means of global communications and coordination. In this paper, we will show that swarms can perform better, when they self-adapt to admit heterogeneous behavior roles.MethodsWe model a foraging swarm task as an extensive-form fully-cooperative game, in which the swarm reward is an additive function of individual contributions (the sum of collected items). To maximize the swarm reward, previous work proposed using distributed reinforcement learning, where each robot adapts its own collision-avoidance decisions based on the Effectiveness Index reward (EI). EI uses information about the time between their own collisions (information readily available even to simple physical robots). While promising, the use of EI is brittle (as we show), since robots that selfishly seek to optimize their own EI (minimizing time spent on collisions) can actually cause swarm-wide performance to degrade.ResultsTo address this, we derive a reward function from a game-theoretic view of swarm foraging as a fully-cooperative, unknown horizon repeating game. We demonstrate analytically that the total coordination overhead of the swarm (total time spent on collision-avoidance, rather than foraging per-se) is directly tied to the total utility of the swarm: less overhead, more items collected. Treating every collision as a stage in the repeating game, the overhead is bounded by the total EI of all robots. We then use a marginal-contribution (difference-reward) formulation to derive individual rewards from the total EI. The resulting Aligned Effective Index (AEI) reward has the property that each individual can estimate the impact of its decisions on the swarm: individual improvements translate to swarm improvements. We show that AEI provably generalizes previous work, adding a component that computes the effect of counterfactual robot absence. Different assumptions on this counterfactual lead to bounds on AEI from above and below.DiscussionWhile the theoretical analysis clarifies both assumptions and gaps with respect to the reality of robots, experiments with real and simulated robots empirically demonstrate the efficacy of the approach in practice, and the importance of behavioral (decision-making) diversity in optimizing swarm goals.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2024.1394209</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2024.1394209</link>
        <title><![CDATA[MACRPO: Multi-agent cooperative recurrent policy optimization]]></title>
        <pubdate>2024-12-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Eshagh Kargar</author><author>Ville Kyrki</author>
        <description><![CDATA[This work considers the problem of learning cooperative policies in multi-agent settings with partially observable and non-stationary environments without a communication channel. We focus on improving information sharing between agents and propose a new multi-agent actor-critic method called Multi-Agent Cooperative Recurrent Proximal Policy Optimization (MACRPO). We propose two novel ways of integrating information across agents and time in MACRPO: First, we use a recurrent layer in the critic’s network architecture and propose a new framework to use the proposed meta-trajectory to train the recurrent layer. This allows the network to learn the cooperation and dynamics of interactions between agents, and also handle partial observability. Second, we propose a new advantage function that incorporates other agents’ rewards and value functions by controlling the level of cooperation between agents using a parameter. The use of this control parameter is suitable for environments in which the agents are unable to fully cooperate with each other. We evaluate our algorithm on three challenging multi-agent environments with continuous and discrete action spaces, Deepdrive-Zero, Multi-Walker, and Particle environment. We compare the results with several ablations and state-of-the-art multi-agent algorithms such as MAGIC, IC3Net, CommNet, GA-Comm, QMIX, MADDPG, and RMAPPO, and also single-agent methods with shared parameters between agents such as IMPALA and APEX. The results show superior performance against other algorithms. The code is available online at https://github.com/kargarisaac/macrpo.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2024.1375393</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2024.1375393</link>
        <title><![CDATA[Evolutionary optimization for risk-aware heterogeneous multi-agent path planning in uncertain environments]]></title>
        <pubdate>2024-08-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fatemeh Rekabi Bana</author><author>Tomáš Krajník</author><author>Farshad Arvin</author>
        <description><![CDATA[Cooperative multi-agent systems make it possible to employ miniature robots in order to perform different experiments for data collection in wide open areas to physical interactions with test subjects in confined environments such as a hive. This paper proposes a new multi-agent path-planning approach to determine a set of trajectories where the agents do not collide with each other or any obstacle. The proposed algorithm leverages a risk-aware probabilistic roadmap algorithm to generate a map, employs node classification to delineate exploration regions, and incorporates a customized genetic framework to address the combinatorial optimization, with the ultimate goal of computing safe trajectories for the team. Furthermore, the proposed planning algorithm makes the agents explore all subdomains in the workspace together as a formation to allow the team to perform different tasks or collect multiple datasets for reliable localization or hazard detection. The objective function for minimization includes two major parts, the traveling distance of all the agents in the entire mission and the probability of collisions between the agents or agents with obstacles. A sampling method is used to determine the objective function considering the agents’ dynamic behavior influenced by environmental disturbances and uncertainties. The algorithm’s performance is evaluated for different group sizes by using a simulation environment, and two different benchmark scenarios are introduced to compare the exploration behavior. The proposed optimization method establishes stable and convergent properties regardless of the group size.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2024.1422344</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2024.1422344</link>
        <title><![CDATA[Editorial: Decision-making and planning for multi-agent systems]]></title>
        <pubdate>2024-05-24T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Panagiotis Tsiotras</author><author>Matthew Gombolay</author><author>Jakob Foerster</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2024.1407421</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2024.1407421</link>
        <title><![CDATA[Editorial: Understanding and engineering cyber-physical collectives]]></title>
        <pubdate>2024-05-06T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Roberto Casadei</author><author>Lukas Esterle</author><author>Rose Gamble</author><author>Paul Harvey</author><author>Elizabeth F. Wanner</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2024.1172105</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2024.1172105</link>
        <title><![CDATA[Cooperative planning for physically interacting heterogeneous robots]]></title>
        <pubdate>2024-03-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Michael A. Sebok</author><author>Herbert G. Tanner</author>
        <description><![CDATA[Heterogeneous multi-agent systems can be deployed to complete a variety of tasks, including some that are impossible using a single generic modality. This paper introduces an approach to solving the problem of cooperative behavior planning in small heterogeneous robot teams where members can both function independently as well as physically interact with each other in ways that give rise to additional functionality. This approach enables, for the first time, the cooperative completion of tasks that are infeasible when using any single modality from those agents comprising the team.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2023.1190296</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2023.1190296</link>
        <title><![CDATA[Exploiting redundancy for UWB anomaly detection in infrastructure-free multi-robot relative localization]]></title>
        <pubdate>2023-12-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sahar Salimpour</author><author>Paola Torrico Morón</author><author>Xianjia Yu</author><author>Tomi Westerlund</author><author>Jorge Peña-Queralta</author>
        <description><![CDATA[Ultra-wideband (UWB) localization methods have emerged as a cost-effective and accurate solution for GNSS-denied environments. There is a significant amount of previous research in terms of resilience of UWB ranging, with non-line-of-sight and multipath detection methods. However, little attention has been paid to resilience against disturbances in relative localization systems involving multiple nodes. This paper presents an approach to detecting range anomalies in UWB ranging measurements from the perspective of multi-robot cooperative localization. We introduce an approach to exploiting redundancy for relative localization in multi-robot systems, where the position of each node is calculated using different subsets of available data. This enables us to effectively identify nodes that present ranging anomalies and eliminate their effect within the cooperative localization scheme. We analyze anomalies created by timing errors in the ranging process, e.g., owing to malfunctioning hardware. However, our method is generic and can be extended to other types of ranging anomalies. Our approach results in a more resilient cooperative localization framework with a negligible impact in terms of the computational workload.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2023.1285412</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2023.1285412</link>
        <title><![CDATA[End-to-end decentralized formation control using a graph neural network-based learning method]]></title>
        <pubdate>2023-11-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chao Jiang</author><author>Xinchi Huang</author><author>Yi Guo</author>
        <description><![CDATA[Multi-robot cooperative control has been extensively studied using model-based distributed control methods. However, such control methods rely on sensing and perception modules in a sequential pipeline design, and the separation of perception and controls may cause processing latencies and compounding errors that affect control performance. End-to-end learning overcomes this limitation by implementing direct learning from onboard sensing data, with control commands output to the robots. Challenges exist in end-to-end learning for multi-robot cooperative control, and previous results are not scalable. We propose in this article a novel decentralized cooperative control method for multi-robot formations using deep neural networks, in which inter-robot communication is modeled by a graph neural network (GNN). Our method takes LiDAR sensor data as input, and the control policy is learned from demonstrations that are provided by an expert controller for decentralized formation control. Although it is trained with a fixed number of robots, the learned control policy is scalable. Evaluation in a robot simulator demonstrates the triangular formation behavior of multi-robot teams of different sizes under the learned control policy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2023.1249586</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2023.1249586</link>
        <title><![CDATA[Terrain-aware semantic mapping for cooperative subterranean exploration]]></title>
        <pubdate>2023-10-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Michael J. Miles</author><author>Harel Biggie</author><author>Christoffer Heckman</author>
        <description><![CDATA[Navigation over torturous terrain such as those in natural subterranean environments presents a significant challenge to field robots. The diversity of hazards, from large boulders to muddy or even partially submerged Earth, eludes complete definition. The challenge is amplified if the presence and nature of these hazards must be shared among multiple agents that are operating in the same space. Furthermore, highly efficient mapping and robust navigation solutions are absolutely critical to operations such as semi-autonomous search and rescue. We propose an efficient and modular framework for semantic grid mapping of subterranean environments. Our approach encodes occupancy and traversability information, as well as the presence of stairways, into a grid map that is distributed amongst a robot fleet despite bandwidth constraints. We demonstrate that the mapping method enables safe and enduring exploration of subterranean environments. The performance of the system is showcased in high-fidelity simulations, physical experiments, and Team MARBLE’s entry in the DARPA Subterranean Challenge which received third place.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2023.1219931</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2023.1219931</link>
        <title><![CDATA[Distributed control for geometric pattern formation of large-scale multirobot systems]]></title>
        <pubdate>2023-09-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Andrea Giusti</author><author>Gian Carlo Maffettone</author><author>Davide Fiore</author><author>Marco Coraggio</author><author>Mario di Bernardo</author>
        <description><![CDATA[Introduction: Geometric pattern formation is crucial in many tasks involving large-scale multi-agent systems. Examples include mobile agents performing surveillance, swarms of drones or robots, and smart transportation systems. Currently, most control strategies proposed to achieve pattern formation in network systems either show good performance but require expensive sensors and communication devices, or have lesser sensor requirements but behave more poorly.Methods and result: In this paper, we provide a distributed displacement-based control law that allows large groups of agents to achieve triangular and square lattices, with low sensor requirements and without needing communication between the agents. Also, a simple, yet powerful, adaptation law is proposed to automatically tune the control gains in order to reduce the design effort, while improving robustness and flexibility.Results: We show the validity and robustness of our approach via numerical simulations and experiments, comparing it, where possible, with other approaches from the existing literature.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2023.1163185</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2023.1163185</link>
        <title><![CDATA[Adaptivity: a path towards general swarm intelligence?]]></title>
        <pubdate>2023-05-09T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Hian Lee Kwa</author><author>Jabez Leong Kit</author><author>Nikolaj Horsevad</author><author>Julien Philippot</author><author>Mohammad Savari</author><author>Roland Bouffanais</author>
        <description><![CDATA[The field of multi-robot systems (MRS) has recently been gaining increasing popularity among various research groups, practitioners, and a wide range of industries. Compared to single-robot systems, multi-robot systems are able to perform tasks more efficiently or accomplish objectives that are simply not feasible with a single unit. This makes such multi-robot systems ideal candidates for carrying out distributed tasks in large environments—e.g., performing object retrieval, mapping, or surveillance. However, the traditional approach to multi-robot systems using global planning and centralized operation is, in general, ill-suited for fulfilling tasks in unstructured and dynamic environments. Swarming multi-robot systems have been proposed to deal with such steep challenges, primarily owing to its adaptivity. These qualities are expressed by the system’s ability to learn or change its behavior in response to new and/or evolving operating conditions. Given its importance, in this perspective, we focus on the critical importance of adaptivity for effective multi-robot system swarming and use it as the basis for defining, and potentially quantifying, swarm intelligence. In addition, we highlight the importance of establishing a suite of benchmark tests to measure a swarm’s level of adaptivity. We believe that a focus on achieving increased levels of swarm intelligence through the focus on adaptivity will further be able to elevate the field of swarm robotics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2023.1089062</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2023.1089062</link>
        <title><![CDATA[On the role and opportunities in teamwork design for advanced multi-robot search systems]]></title>
        <pubdate>2023-04-13T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Roee M. Francos</author><author>Alfred M. Bruckstein</author>
        <description><![CDATA[Intelligent robotic systems are becoming ever more present in our lives across a multitude of domains such as industry, transportation, agriculture, security, healthcare and even education. Such systems enable humans to focus on the interesting and sophisticated tasks while robots accomplish tasks that are either too tedious, routine or potentially dangerous for humans to do. Recent advances in perception technologies and accompanying hardware, mainly attributed to rapid advancements in the deep-learning ecosystem, enable the deployment of robotic systems equipped with onboard sensors as well as the computational power to perform autonomous reasoning and decision making online. While there has been significant progress in expanding the capabilities of single and multi-robot systems during the last decades across a multitude of domains and applications, there are still many promising areas for research that can advance the state of cooperative searching systems that employ multiple robots. In this article, several prospective avenues of research in teamwork cooperation with considerable potential for advancement of multi-robot search systems will be visited and discussed. In previous works we have shown that multi-agent search tasks can greatly benefit from intelligent cooperation between team members and can achieve performance close to the theoretical optimum. The techniques applied can be used in a variety of domains including planning against adversarial opponents, control of forest fires and coordinating search-and-rescue missions. The state-of-the-art on methods of multi-robot search across several selected domains of application is explained, highlighting the pros and cons of each method, providing an up-to-date view on the current state of the domains and their future challenges.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frobt.2022.992716</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frobt.2022.992716</link>
        <title><![CDATA[Active-sensing-based decentralized control of autonomous mobile agents for quick and smooth collision avoidance]]></title>
        <pubdate>2022-11-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Takeshi Kano</author><author>Takeru Kanno</author><author>Taishi Mikami</author><author>Akio Ishiguro</author>
        <description><![CDATA[There is an increasing demand for multi-agent systems in which each mobile agent, such as a robot in a warehouse or a flying drone, moves toward its destination while avoiding other agents. Although several control schemes for collision avoidance have been proposed, they cannot achieve quick and safe movement with minimal acceleration and deceleration. To address this, we developed a decentralized control scheme that involves modifying the social force model, a model of pedestrian dynamics, and successfully realized quick, smooth, and safe movement. However, each agent had to observe many nearby agents and predict their future motion; that is, unnecessary sensing and calculations were required for each agent. In this study, we addressed this issue by introducing active sensing. In this control scheme, an index referred to as the “collision risk level” is defined, and the observation range of each agent is actively controlled on this basis. Through simulations, we demonstrated that the proposed control scheme works reasonably while reducing unnecessary sensing and calculations.]]></description>
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