Decentralized Strategy for Cooperative Multi-Robot Exploration and Mapping

Batinović, Ana; Oršulić, Juraj; Petrović, Tamara; Bogdan, Stjepan

doi:10.1016/j.ifacol.2020.12.2618

Cited by 12 publications

(12 citation statements)

References 19 publications

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“…CAMShift 11 [93], HOG 12 + SVM [129], Actor-Critic RL [140], YOLOv3-Tiny + KCF 13 [2], RRT [17], [143], [144], DDPG 19 [15], [145], [146], GFE 20 [147], A3C [148], Dyna-Q algorithm [11], [149], SARL* 22 [150], ROAMFREE [69], Two-stage stochastic programming [81], TEB 23 + A* [95], [127] 2 -Global Path Planning IDE 24 [16], Dijkstra's algorithm [119], [120], [151], SARSA [152], DQL [8], [153], [154],…”

Section: Householdmentioning

confidence: 99%

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

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Simulation plays a pivotal role in providing safely reproducible scenarios to evaluate the ever-advancing domain of computer science and robotics. It was an essential part of the pandemic when no access to physical spaces was available. The advent of AI-powered platforms in conjunction with enhanced graphics, physics and other sensory engines attracts a new breed of interdisciplinary researchers to enter the robotic field, most notably from computer science, engineering and social sciences. Integration of ROS as a uniform middleware to deploy achieved outcomes in real practice provides an opportunity to move one step closer to the sim-to-real experiences that enables researchers to test ideas beyond the close laboratory spaces. There is a lack of comprehensive evaluation of ROS-enabled simulators, and the integration of advanced AI techniques for realistic scenario replication. This paper addresses this challenge by evaluating ROSenabled simulators in the design and implementation of AI techniques through an in-depth systematic literature review (SLR). This SLR is guided by the research and commercial market demands, employing Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) frameworks with a major focus on Wheeled Mobile Robots (WMRs). We also highlight the increasing importance of game engines like Unity and Unreal in future of robotic simulations, especially under modelling close to real experiences. By comparing simulation platform features and capabilities, this paper offers guidance to developers and researchers, enabling them to select the most suitable platform for their projects efficiently that contradicts the commonly in use "one size fits all" approach. Finally, based on the thorough insights from the review, we identify and suggest some key future research directions in AI-enhanced realistic robotic simulations.

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Section: Householdmentioning

confidence: 99%

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

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“…Cooperative exploration of unknown environments using SRSs has been previously investigated for swarms equipped with "high-information" sensors (e.g., omnidirectional laser scanners), where each robot can explore and map their local environment individually [37][38][39][40][41].…”

Section: Mapping Using Robotic Systems With "High-information" Extero...mentioning

confidence: 99%

“…In the above context, maps can be built by storing one centralized map that each robot contributes information to or by having each robot store their own individual map of the environment. Centralized maps have been used with efficient decentralized frontierbased exploration methods to quickly disperse agents throughout the environment [37]. Alternatively, each swarm robot may store their own map while exchanging information with their neighbors to merge their neighbors map with their own and better plan their own trajectories [38][39][40].…”

Section: Mapping Using Robotic Systems With "High-information" Extero...mentioning

confidence: 99%

See 1 more Smart Citation

Occupancy Grid Mapping via Resource-Constrained Robotic Swarms: A Collaborative Exploration Strategy

et al. 2023

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This paper addresses the problem of building an occupancy grid map of an unknown environment using a swarm comprising resource-constrained robots, i.e., robots with limited exteroceptive and inter-robot sensing capabilities. Past approaches have, commonly, used random-motion models to disperse the swarm and explore the environment randomly, which do not necessarily consider prior information already contained in the map. Herein, we present a collaborative, effective exploration strategy that directs the swarm toward ‘promising’ frontiers by dividing the swarm into two teams: landmark robots and mapper robots, respectively. The former direct the latter, toward promising frontiers, to collect proximity measurements to be incorporated into the map. The positions of the landmark robots are optimized to maximize new information added to the map while also adhering to connectivity constraints. The proposed strategy is novel as it decouples the problem of directing the resource-constrained swarm from the problem of mapping to build an occupancy grid map. The performance of the proposed strategy was validated through extensive simulated experiments.

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“…The SLAM algorithm plays an important role in many applications, such as civil infrastructure surveys, search and rescue, and autonomous exploration. Cartographer provides a map suitable for both 2D [12], [13] and 3D [10] autonomous exploration, employing the well known frontier detection method for exploration planning and execution.…”

Section: Navigationmentioning

confidence: 99%

Flying with Cartographer: Adapting the Cartographer 3D Graph SLAM Stack for UAV Navigation

Oršulić

Milijas

Batinović

et al. 2021

2021 Aerial Robotic Systems Physically Interacting With the Environment (AIRPHARO)

Self Cite

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This paper describes an application of the Cartographer graph SLAM stack as a pose sensor in a UAV feedback control loop, with certain application-specific changes in the SLAM stack such as smoothing of the optimized pose. Pose estimation is performed by fusing 3D LiDAR/IMU-based proprioception with GPS position measurements by means of pose graph optimisation. Moreover, partial environment maps built from the LiDAR data (submaps) within the Cartographer SLAM stack are marshalled into OctoMap, an Octree-based voxel map implementation. The OctoMap is further used for navigation tasks such as path planning and obstacle avoidance.

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Decentralized Strategy for Cooperative Multi-Robot Exploration and Mapping

Cited by 12 publications

References 19 publications

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Occupancy Grid Mapping via Resource-Constrained Robotic Swarms: A Collaborative Exploration Strategy

Flying with Cartographer: Adapting the Cartographer 3D Graph SLAM Stack for UAV Navigation

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