An Approach for Multi-UAV System Navigation and Target Finding in Cluttered Environments

Xiaolong, Zhu; Vanegas, Fernando; Gonzalez, Felipé

doi:10.1109/icuas48674.2020.9214062

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…To overcome these limitations, some researchers have framed MTS as a partially observable Markov decision process (POMDP), solved using search algorithms [29,30] or RL. [31] For instance, ref.…”

Section: Mtsmentioning

confidence: 99%

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Toward Collaborative Multitarget Search and Navigation with Attention‐Enhanced Local Observation

Xiao,

Pisutsin,

Feroskhan

2024

Advanced Intelligent Systems

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Collaborative multitarget search and navigation (CMTSN) is highly demanded in complex missions such as rescue and warehouse management. Traditional centralized and decentralized approaches fall short in terms of scalability and adaptability to real‐world complexities such as unknown targets and large‐scale missions. This article addresses this challenging CMTSN problem in three‐dimensional spaces, specifically for agents with local visual observation operating in obstacle‐rich environments. To overcome these challenges, this work presents the POsthumous Mix‐credit assignment with Attention (POMA) framework. POMA integrates adaptive curriculum learning and mixed individual‐group credit assignments to efficiently balance individual and group contributions in a sparse reward environment. It also leverages an attention mechanism to manage variable local observations, enhancing the framework's scalability. Extensive simulations demonstrate that POMA outperforms a variety of baseline methods. Furthermore, the trained model is deployed over a physical visual drone swarm, demonstrating the effectiveness and generalization of our approach in real‐world autonomous flight.

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Section: Mtsmentioning

confidence: 99%

“…[ 31 ] For instance, ref. [30] modeled MTS in 3D space using a POMDP framework and addressed it with the adaptive belief tree (ABT) algorithm. In an attempt to maintain multi‐UAV connectivity, ref.…”

Section: Related Workmentioning

confidence: 99%

Toward Collaborative Multitarget Search and Navigation with Attention‐Enhanced Local Observation

Xiao,

Pisutsin,

Feroskhan

2024

Advanced Intelligent Systems

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“…Vanegas and Gonzalez [14] applied a model-based solver to target-find in the presence of sensor, localization, and target uncertainty, but operated within a limited and known environment. Zhu et al [15] propose a system for multi-UAV target finding under motion and observation uncertainty using a decentralized model-based POMDP approach. However, the complexity and size of the environments is limited, the obstacles are known prior to operation, and the rate of the motion planner is limited to 1Hz, limiting the suitability of the approach in the presence of dynamic hazards.…”

Section: Related Workmentioning

confidence: 99%

Multi-UAV Mapping and Target Finding in Large, Complex, Partially Observable Environments

Walker,

Vanegas,

Gonzalez

2023

Remote Sensing

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Coordinating multiple unmanned aerial vehicles (UAVs) for the purposes of target finding or surveying points of interest in large, complex, and partially observable environments remains an area of exploration. This work proposes a modeling approach and software framework for multi-UAV search and target finding within large, complex, and partially observable environments. Mapping and path-solving is carried out by an extended NanoMap library; the global planning problem is defined as a decentralized partially observable Markov decision process and solved using an online model-based solver, and the local control problem is defined as two separate partially observable Markov decision processes that are solved using deep reinforcement learning. Simulated testing demonstrates that the proposed framework enables multiple UAVs to search and target-find within large, complex, and partially observable environments.

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“…In this paper, an observation-oriented Dec-POMDP (Ob-Dec-POMDP) model was therefore extended and modified and used to formulate and solve the problem described in Section III. This Ob-Dec-POMDP model is expanded from our previous work [24], which maintains the scalability and cooperation as a MAS without an intractable computational load for the current onboard processor, as a compromise and combination between the theoretical mathematic model and the real-wold implementation. The primary purpose of this Ob-Dec-POMDP formulation is to accomplish a comprehensive mission as an efficient cooperative MAS within fast reaction time in cluttered environments.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In this paper, we extend our previous work for searching to find a target without the use of GPS. In [24], we proposed a decentralised decision-making system based on POMDPs containing an information sharing module for target finding by multiple UAVs. That system was assigned several possible target positions within a known map, GPS signal for accurate localisation was available, and was assisted by the information sharing module to achieve an efficient and scalable teamwork.…”

Section: Introductionmentioning

confidence: 99%

A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments

Xiaolong

Vanegas

Gonzalez

et al. 2021

2021 International Conference on Unmanned Aircraft Systems (ICUAS)

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The use of multi-rotor Unmanned Aerial Vehicles (UAVs) for search and rescue as well as remote sensing is rapidly increasing. Multi-rotor UAVs, however, have limited endurance. The range of UAV applications can be widened if teams of multiple UAVs are used. We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Examples of such environments include indoor scenarios, urban or natural canyons, caves, and tunnels, where the GPS signal is limited or blocked. The framework is based on a probabilistic decentralised Partially Observable Markov Decision Process which accounts for the uncertainties in sensing and the environment. The team can cooperate efficiently, with each UAV sharing only limited processed observations and their locations during the mission. The system is simulated using the Robotic Operating System and Gazebo. Performance of the system with an increasing number of UAVs in several indoor scenarios with obstacles is tested. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.

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An Approach for Multi-UAV System Navigation and Target Finding in Cluttered Environments

Cited by 7 publications

References 21 publications

Toward Collaborative Multitarget Search and Navigation with Attention‐Enhanced Local Observation

Toward Collaborative Multitarget Search and Navigation with Attention‐Enhanced Local Observation

Multi-UAV Mapping and Target Finding in Large, Complex, Partially Observable Environments

A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments

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