A reinforcement learning‐based approach for modeling and coverage of an unknown field using a team of autonomous ground vehicles

Faryadi, Saba; Velni, Javad Mohammadpour

doi:10.1002/int.22331

Cited by 27 publications

(12 citation statements)

References 25 publications

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“…Reinforcement learning (RL) turns out to be an effective approach to learn the optimal control policy for an agent without knowledge of the dynamic model information (see, e.g., Reference [19]). Over the latest decade, RL has attracted much attention in many complicated flight tasks for a single quadrotor (see, References [20–23]) and has also become a promising method to develop model‐free optimal cooperative controllers for some linear multiagent systems (see, References [24–28]).…”

Section: Introductionmentioning

confidence: 99%

Model‐free attitude synchronization for multiple heterogeneous quadrotors via reinforcement learning

2021

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In this paper, a model‐free optimal synchronization controller is designed to achieve the aggressive attitude synchronization for multiple heterogeneous quadrotor systems with highly nonlinear and coupled dynamics by using a reinforcement learning (RL) approach. A distributed observer is first designed for each following quadrotor to estimate the states of a virtual leader. A performance function is then utilized for each quadrotor to penalize the observed synchronization error and the control effort. An RL approach is finally employed to learn the optimal control law without any knowledge of the dynamic model information of the followers. The control law depends on the quadrotor states and the observer states, and guarantees that the attitude synchronization error converges to zero for all quadrotors, under aggressive maneuvers. Simulation results are provided to verify the effectiveness of the proposed controller.

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Section: Introductionmentioning

confidence: 99%

Model‐free attitude synchronization for multiple heterogeneous quadrotors via reinforcement learning

2021

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“…Harvested muddy moss in farmland [13] Plowed [47] None Monitored vineyards [28] Drove in a greenhouse [56] Environmental monitored [49,50] Harvested farmland [48] Collected farmland information [71] Precision irrigation in the vineyards [72] Sprayed in the Orchard [66] Compared with 2D maps [14,[73][74][75], 3D maps can provide more information. By fusing the original measurement or small local maps generated from multiple robots to construct global maps at the same time, or matching the 3D maps constructed by heterogeneous robots, more and more abundant data are obtained, which is a new research direction of mapping.…”

Section: Applicationmentioning

confidence: 99%

“…Precision irrigation in the vineyards [72] Easy to implement real-time control; Problem with local extreme points;…”

Section: • Formation Controlmentioning

confidence: 99%

Research Progress on Synergistic Technologies of Agricultural Multi-Robots

Mao

Liu

et al. 2021

Applied Sciences

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Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation, path planning, formation control, and communication, and summarizes the technological progress and development characteristics of these five technologies. Finally, because of these development characteristics, it is shown that the trends and research focus for agricultural multi-robots are to optimize the existing technologies and apply them to a variety of agricultural multi-robots, such as building a hybrid architecture of multi-robot systems, SLAM (simultaneous localization and mapping), cooperation learning of robots, hybrid path planning and formation reconstruction. While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development demand, we conclude that it is realistic to expect automated multi-robot systems in the future.

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“…Moreover, it eliminates the obstacles faced by the conventional activation function design and it is very robust, easy to train and achieve effective performance. 34,35 The combined features, which is referred to as feature vector E obtained from the feature extraction module, are fed as an input into the DMON for classifying the grids and detects the presence of target in the grid.…”

Section: Dmon For Target Grid Classificationmentioning

confidence: 99%

Visual features and Deep Maxout Network‐based moving target detection using radar signals under sea clutter

Battula

Jaya

2021

Int J Intell Syst

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Radar echoes have emerged as a rapid technology that is utilized in a wide range of applications especially in target identification under sea-clutter background. The detection of moving targets under a sea-clutter background is still a complex task because of the entering and evacuating of targets, which is highly unpredictable and unexpectable. Because of the lack of essential information, target detection and parameter estimations have become key constraints. Besides, it is very difficult to differentiate directly the small targets from the clutter background. However, the accurate location of the target cannot be determined exactly by the existing approaches.To address such limitations, an efficient strategy is proposed in this study for recognizing the moving targets under sea-clutter background using the proposed Magnetic Ant Lion Optimization algorithm, which is derived by the integration of Magnetic Optimization and Ant Lion Optimization. However, the proposed method achieves effective performance in terms of detection rate, mean-squared error, and the missed target rate with the values of 0.988, 0.012, and 0.224 for vertical-horizontal polarity.

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A reinforcement learning‐based approach for modeling and coverage of an unknown field using a team of autonomous ground vehicles

Cited by 27 publications

References 25 publications

Model‐free attitude synchronization for multiple heterogeneous quadrotors via reinforcement learning

Model‐free attitude synchronization for multiple heterogeneous quadrotors via reinforcement learning

Research Progress on Synergistic Technologies of Agricultural Multi-Robots

Visual features and Deep Maxout Network‐based moving target detection using radar signals under sea clutter

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