Swarming in forestry environments: collective exploration and network deployment

Couceiro, Micael S.; Portugal, David

doi:10.1049/pbce119f_ch12

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Only a scarcity of research studies have specifically investigated the utilization of teams of robots in forest-like applications. In particular, the focus has been primarily on UAVs [296,338,339] or UGVs, but without conducting real-world experimentation [340,341]. A few research works have also been devoted to precision agriculture [37,38,342], an application domain having similar requirements to robotic forestry.…”

Section: Cooperative Perceptionmentioning

confidence: 99%

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Ferreira,

Portugal,

Andrada

et al. 2023

Robotics

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Artificial perception for robots operating in outdoor natural environments, including forest scenarios, has been the object of a substantial amount of research for decades. Regardless, this has proven to be one of the most difficult research areas in robotics and has yet to be robustly solved. This happens namely due to difficulties in dealing with environmental conditions (trees and relief, weather conditions, dust, smoke, etc.), the visual homogeneity of natural landscapes as opposed to the diversity of natural obstacles to be avoided, and the effect of vibrations or external forces such as wind, among other technical challenges. Consequently, we propose a new survey, describing the current state of the art in artificial perception and sensing for robots in precision forestry. Our goal is to provide a detailed literature review of the past few decades of active research in this field. With this review, we attempted to provide valuable insights into the current scientific outlook and identify necessary advancements in the area. We have found that the introduction of robotics in precision forestry imposes very significant scientific and technological problems in artificial sensing and perception, making this a particularly challenging field with an impact on economics, society, technology, and standards. Based on this analysis, we put forward a roadmap to address the outstanding challenges in its respective scientific and technological landscape, namely the lack of training data for perception models, open software frameworks, robust solutions for multi-robot teams, end-user involvement, use case scenarios, computational resource planning, management solutions to satisfy real-time operation constraints, and systematic field testing. We argue that following this roadmap will allow for robotics in precision forestry to fulfil its considerable potential.

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Section: Cooperative Perceptionmentioning

confidence: 99%

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Ferreira,

Portugal,

Andrada

et al. 2023

Robotics

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“…To boost the efficiency in exploration, various multi-robot cooperative frontier-based methods have also been proposed in the literature, both in centralized [12], [13] and decentralized formats [14]. Centralized methods feature a ground station where a global multi-robot plan is first computed, and then broadcast to the agents under the assumption of perfect communication.…”

Section: Related Workmentioning

confidence: 99%

Fast Multi-UAV Decentralized Exploration of Forests

Bartolomei

Teixeira

Chli

2023

IEEE Robot. Autom. Lett.

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Efficient exploration strategies are vital in tasks such as search-and-rescue missions and disaster surveying. Unmanned Aerial Vehicles (UAVs) have become particularly popular in such applications, promising to cover large areas at high speeds. Moreover, with the increasing maturity of onboard UAV perception, research focus has been shifting toward higher-level reasoning for multi-robot missions. However, autonomous navigation and exploration of previously unknown large spaces still constitute an open challenge, especially when the environment is cluttered and exhibits large and frequent occlusions due to high obstacle density, as is the case of forests. Moreover, the problem of longdistance wireless communication in such scenes can become a limiting factor, especially when automating the navigation of a UAV fleet. In this spirit, this work proposes an exploration strategy that enables multiple UAVs to quickly explore complex scenes in a decentralized fashion. By providing the decisionmaking capabilities to each UAV to switch between different execution modes, the proposed strategy is shown to strike a great balance between cautious exploration of yet completely unknown regions and more aggressive exploration of smaller areas of unknown space. This results in full coverage of forest areas in multi-UAV setups up to 30% faster than the state of the art.

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“…The size of the sub-swarms need not be fixed. In Couceiro et al (2011a), Couceiro et al (2011b), Couceiro et al (2014) and Couceiro and Portugal (2018), work is done on a Robotic Darwinian PSO (RDPSO), adapted for use with ground robots from the original Darwinian PSO by Tillett et al (2005). Sánchez et al (2018) later expanded on this work with applications using underwater robots carrying out exploration in a 3D environment.…”

Section: Dynamic Agent Assignmentmentioning

confidence: 99%

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

2022

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Multi-agent systems and multi-robot systems have been recognized as unique solutions to complex dynamic tasks distributed in space. Their effectiveness in accomplishing these tasks rests upon the design of cooperative control strategies, which is acknowledged to be challenging and nontrivial. In particular, the effectiveness of these strategies has been shown to be related to the so-called exploration–exploitation dilemma: i.e., the existence of a distinct balance between exploitative actions and exploratory ones while the system is operating. Recent results point to the need for a dynamic exploration–exploitation balance to unlock high levels of flexibility, adaptivity, and swarm intelligence. This important point is especially apparent when dealing with fast-changing environments. Problems involving dynamic environments have been dealt with by different scientific communities using theory, simulations, as well as large-scale experiments. Such results spread across a range of disciplines can hinder one’s ability to understand and manage the intricacies of the exploration–exploitation challenge. In this review, we summarize and categorize the methods used to control the level of exploration and exploitation carried out by an multi-agent systems. Lastly, we discuss the critical need for suitable metrics and benchmark problems to quantitatively assess and compare the levels of exploration and exploitation, as well as the overall performance of a system with a given cooperative control algorithm.

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Swarming in forestry environments: collective exploration and network deployment

Cited by 8 publications

References 22 publications

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Fast Multi-UAV Decentralized Exploration of Forests

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

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