Hierarchal Application of Receding Horizon Synthesis and Dynamic Allocation for UAVs Fighting Fires

Shaffer, Joshua; Carrillo, Estefany; Xu, Huan

doi:10.1109/access.2018.2885455

Cited by 31 publications

(8 citation statements)

References 12 publications

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“…Unmanned Aerial Vehicles (UAVs) are an example of an autonomous system that has seen diverse utilization in areas of agriculture, disaster relief, remote sensing, surveillance, etc. [1], [2], [3], [4], [5]. UAVs are often deployed in uncontrolled environments, and are hence required to adapt to dynamic conditions in real-time with minimal sacrifice to functionality and performance.…”

mentioning

confidence: 99%

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

et al. 2020

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System identification is a key discipline within the field of automation that deals with inferring mathematical models of dynamic systems based on input-output measurements. Conventional identification methods require extensive data generation and are thus not suitable for real-time applications. In this paper, a novel real-time approach for the parametric identification of linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT) is proposed. The proposed approach requires only a single steady-state cycle of MRFT, and guarantees stability and performance in the identification and control phases. The MRFT output is passed to a trained DL model that identifies the underlying process parameters in milliseconds. A novel modification to the Softmax function is derived to better conform the DL model for the process identification task. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented approach. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.

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confidence: 99%

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

et al. 2020

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“…forest firefighting, breeding monitoring, agriculture management, etc.). [1][2][3][4] Sensor-equipped UAVs that are augmented with the techniques of computer photography 1 and artificial intelligence 5 to manipulate tracking data for detecting people, targets, and environments, and identifying events that occur in the real scene (e.g. vehicles driving on a highway).…”

Section: Introductionmentioning

confidence: 99%

A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles

Chen,

Meng,

Wang

et al. 2024

International Journal of Advanced Robotic Systems

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Currently, for years, unmanned aerial vehicles have been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, it can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of collaborative unmanned aerial vehicles will offer diverse interpretations which support a multiperspective view of the scene. Due to diverse interpretations of unmanned aerial vehicles usually deviates, thus, unmanned aerial vehicles require a consensus interpretation for the scenario. To previous purposes, this study presents an original consensus-based method to pilot multi-unmanned aerial vehicle systems for achieving consensus on their observation as well as constructing a group situation-based depiction of the scenario. Further, a fuzzy neural network generalized prediction control system known as a recurrent self-evolving fuzzy neural network is mainly used to ensure stability through the use of a descending gradient online learning rule. At the same time, users can think along the lines of evolutionary biological design. Unmanned aerial vehicles can be modeled as system experts for solving group problems that require the definition of conditions that best describe the scene. First, this method allows each unmanned aerial vehicle to set high-level conditions for detection events by aggregating events based on fuzzy information. These aggregated events are modeled by a fuzzy system ontology, which allows each unmanned aerial vehicle to report its preferences in conditions. Therefore, the interpretation of each drone is compressed to achieve a collective interpretation of the state. The final polls, consent and affinity polls confirmed the final decision group’s reliability ratings. The rated consensus indicates how well the collective interpretation of the scene matches each drone’s point of view.

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“…D ISTRIBUTED task allocation involves autonomous agents dividing a set of tasks among themselves. Task allocation within autonomous multi-agent systems has been proposed for use in a variety of applications, including: search and rescue [1], agriculture [2], surveillance [3] and firefighting [4].…”

Section: Introductionmentioning

confidence: 99%

Distributed Task Allocation Algorithms for Multi-Agent Systems With Very Low Communication

et al. 2022

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In this paper we explore the problem of task allocation when communication is very low, e.g., when the probability of a successful message between agents is ≪ 0.01. Such situations may occur when agents choose not to communicate for reasons of stealth or when agent-to-agent communication is actively jammed by an adversary. In such cases, agents may need to divide tasks without knowing the locations of each other. Given the assumption that agents know the total number of agents in the workspace, we investigate solutions that ensure all tasks are eventually completed-even if some of the agents are destroyed. We present two task allocation algorithms that assume communication may not happen, but that benefit whenever communications are successful. (1) The Spatial Division Playbook Algorithm divides task among agents based on an area decomposition. (2) The Traveling Salesman Playbook Algorithm considers mission travel distance by leveraging Christofides' 3/2 approximation algorithm. These algorithms have task completion runtime complexity of O(m log m) and O(m 3 ), respectively, where m refers to the total number of tasks. We compare both algorithms to four state-of-the-art task allocation algorithms -ACBBA, DHBA, PIA and GA -across multiple communication levels and multiple numbers of targets, and using three different communication models. The new algorithms perform favorably, in terms of the time required to ensure all targets are visited, in the special case when communication is very low.

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Hierarchal Application of Receding Horizon Synthesis and Dynamic Allocation for UAVs Fighting Fires

Cited by 31 publications

References 12 publications

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles

Distributed Task Allocation Algorithms for Multi-Agent Systems With Very Low Communication

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