Sensing, perception, decision, planning and action of autonomous excavators

Eraliev, Oybek; Lee, Kwang-Hee; Shin, Dae-Young; Lee, Chulhee

doi:10.1016/j.autcon.2022.104428

Cited by 41 publications

(6 citation statements)

References 53 publications

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“…Artificial intelligence (AI) [1][2][3][4][5][6][7][8] and robots [9][10][11][12][13][14][15][16][17][18][19][20] have given birth to a new wave of technological break throughs whereby the fusion of control systems with cognitive capabilities is not merely a dream but a reality that is not only being improved but also exploited with passion. The convergence is underlined by the Unscented Kalman Filter (UKF) [21][22][23][24][25][26][27][28][29][30][31][32][33], a sophisticated algorithm that has done a lot in advancing the abilities of AI-based robotics [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48]. The ability of UKF to...…”

Section: Introductionmentioning

confidence: 99%

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Obaideen,

AlShabi,

Bettayeb

et al. 2024

Disruptive Technologies in Information Sciences VIII

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This paper gives a bibliometric summary of Unscented Kalman Filter (UKF) in AI-infused robotics, highlighting its role in unifying control and cognition. Using a systematic approach that includes literature collection from IEEE Xplore, Web of Science and Google Scholar, rigorous screening and selection, and VOSviewer for a comprehensive bibliometric analysis. This analysis reports major trends, primary contributors and central themes, highlighting UKF's pivotal role in improving robotics cognitive and control capacities. The study emphasizes the universally used UKF in many fields of robotics, i.e. in navigation and mapping, sensor fusion, and state estimation, as one of its principal developers, which illustrates its vital role in promoting robotic autonomy and intelligence. The integration of findings from the bibliometric analysis thus not only presents the current state of research but also identifies possible future research directions, highlighting the increasing unification of control theories and cognitive processes in robotics. This research adds to the body of knowledge by delivering a comprehensive map of the UKF application. In this light, the UKF will be able to penetrate AI-infused robotics, the future of robotic developments will rely on the deep fusion of control and cognition facilitated by UKF and alike.

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Section: Introductionmentioning

confidence: 99%

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Obaideen,

AlShabi,

Bettayeb

et al. 2024

Disruptive Technologies in Information Sciences VIII

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show abstract

“…The development of unmanned hydraulic excavators is obviously on the rise. [1][2][3] Curve excavation, straight line excavation, slope excavation, gully slope excavation, and sand and gravel stacking are all extremely repetitive excavation operations carried out by autonomous excavators. The sophisticated nonlinear hydraulic actuator is one of the most difficult aspects of its motion planning and control.…”

Section: Introductionmentioning

confidence: 99%

Research on the motion control strategy of autonomous excavator based on online compensation

Gong,

Zhang,

Jin

et al. 2023

Advances in Mechanical Engineering

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The motion control accuracy of the excavator manipulator is the primary guarantee for autonomous excavators to complete work tasks. This paper studies the motion control strategy of the manipulator of a certain autonomous excavator. By establishing a dynamic model of a 3-degree of freedom of excavator manipulator, the gravity term, inertia term, and centripetal force term in the model are equivalent to external disturbances for online compensation, which improves the motion control accuracy of the excavator manipulator. Based on the dynamic model, a control method of the bucket tooth tip trajectory of an autonomous excavator is proposed. The simulation and test results show that the maximum tracking errors of the joint angles of the boom, the bucket, and the bucket are reduced by 43.14, 26.56, and 51.05% respectively, and the average errors of the whole trajectory are reduced by 56.41, 61.33, and 64.26% respectively. The simulation and test results show that the proposed motion control strategy improves the operation accuracy of the excavator, and can effectively improve the operation accuracy and efficiency of the autonomous excavator.

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“…The realm of intelligent driving necessitates the establishment of safe and efficient interactions between vehicles and the various obstacles encountered within the road environment. To fulfill the driving tasks prescribed by the operator, an autonomous driving system typically comprises four fundamental modules: perception [ 1 ], decision making [ 2 ], planning [ 3 ], and control [ 4 ]. It is worth noting that sound behavior decision making and trajectory planning are pivotal components that chart a secure and rational course for the vehicle, ultimately underpinning the realization of intelligent driving [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Vehicle Decision-Making and Trajectory Planning Method Based on Deep Reinforcement Learning in the Frenet Space

Wang,

Chu,

Zhang

et al. 2023

Sensors

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The complexity inherent in navigating intricate traffic environments poses substantial hurdles for intelligent driving technology. The continual progress in mapping and sensor technologies has equipped vehicles with the capability to intricately perceive their exact position and the intricate interplay among surrounding traffic elements. Building upon this foundation, this paper introduces a deep reinforcement learning method to solve the decision-making and trajectory planning problem of intelligent vehicles. The method employs a deep learning framework for feature extraction, utilizing a grid map generated from a blend of static environmental markers such as road centerlines and lane demarcations, in addition to dynamic environmental cues including vehicle positions across varied lanes, all harmonized within the Frenet coordinate system. The grid map serves as the input for the state space, and the input for the action space comprises a vector encompassing lane change timing, velocity, and vertical displacement at the lane change endpoint. To optimize the action strategy, a reinforcement learning approach is employed. The feasibility, stability, and efficiency of the proposed method are substantiated via experiments conducted in the CARLA simulator across diverse driving scenarios, and the proposed method can increase the average success rate of lane change by 6.8% and 13.1% compared with the traditional planning control algorithm and the simple reinforcement learning method.

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Sensing, perception, decision, planning and action of autonomous excavators

Cited by 41 publications

References 53 publications

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Research on the motion control strategy of autonomous excavator based on online compensation

Intelligent Vehicle Decision-Making and Trajectory Planning Method Based on Deep Reinforcement Learning in the Frenet Space

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