Trajectory Planning for Coal Gangue Sorting Robot Tracking Fast-Mass Target under Multiple Constraints

Article Energy & Fuels

Application of adaptive grid-based multi-objective particle swarm optimization algorithm for directional drilling trajectory design

Bihai Chen et al.

Summary: A new multi-objective optimization model is established in this paper to evaluate the drilling trajectory, with the minimum trajectory length, torque, and strain energy as objective functions and wellbore stability as a constraint. A novel adaptive grid-based multi-objective particle swarm optimization (AGMOPSO) is introduced to obtain the Pareto optimal solutions. The optimization results demonstrate that AGMOPSO outperforms the unmodified algorithm and reported results in terms of convergence and uniformity.

GEOENERGY SCIENCE AND ENGINEERING (2023)

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Review Automation & Control Systems

A survey Of learning-Based control of robotic visual servoing systems

Jinhui Wu et al.

Summary: The major difficulties and challenges of modern robotics systems lie in equipping robots with self-learning and self-decision-making abilities. Visual servoing control strategy is an important approach for robots to perceive the environment through vision. This survey focuses on describing state-of-the-art learning-based algorithms, particularly those that combine model predictive control (MPC) in visual servoing systems, and provides pioneering references and advanced numerical simulations. The survey introduces general modeling methods of visual servo and the impact of traditional control strategies on robotic visual servoing systems. It discusses the advantages of incorporating neural-network-based algorithms and reinforcement-learning-based algorithms into the systems. Finally, the survey summarizes and forecasts the future directions of robotic visual servoing systems based on existing research progress and references.

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS (2022)

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Article Computer Science, Artificial Intelligence

An ant colony optimization algorithm with adaptive greedy strategy to optimize path problems

Wei Li et al.

Summary: The proposed GSACO algorithm, based on adaptive greedy strategy, accelerates the convergence speed and better solves the path planning problem by continuously adjusting control parameters and changing the preference degree of ant colony.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2022)

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Article Computer Science, Information Systems

Deep reinforcement learning based moving object grasping

Pengzhan Chen et al.

Summary: This paper investigates the use of deep reinforcement learning (DRL) with a Kinect depth sensor to tackle the problem of locating unpredictable positions of moving objects under unstructured environments. The proposed grasping system integrates DRL algorithm, Soft Actor-Critic, and object detection techniques to achieve autonomous grasping of moving objects.

INFORMATION SCIENCES (2021)

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Article Computer Science, Information Systems