Their agile locomotion system enables a high degree of mobility such that obstacle avoidance and complex path following can be realised. To achieve this aim, the proposed project will be focusing on the following three research objectives: 1 To investigate how to automatically detect and intelligently recognize the hazardous situations within the inspection areas of the asset in confined spaces.
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Funding is available to cover Home Fees and Stipend. Dr Erfu Yang received his Ph.
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He received his B. His main research interests include robotics, autonomous systems, manufacturing automation, space mechatronics, computer vision, nonlinear control, system modelling and simulation, condition monitoring, signal processing, fault diagnosis, multi-agent reinforcement learning, fuzzy logic, neural networks, bio-inspired optimization algorithms, wireless sensor networks, multi-objective optimizations, cognitive computation, etc.
He has over 70 publications in these areas, including more than 30 journal papers and 5 book chapters. He has extensive training and research experience in the interdisciplinary areas of aerospace engineering, computer science, manufacturing automation, control engineering, mechatronics, robotics and autonomous systems, etc.
Application of Sampling-Based Motion Planning Algorithms in Autonomous Vehicle Navigation
He is an associate editor for the Cognitive Computation journal published by Springer. He has been a PC member or session chair for many conferences, and reviewers for a series of journals. He is the founding director of a research centre on mechatronic systems technology — SMeSTech. He has published over journal and conference papers and book chapters published in leading international journals and conferences in mechatronic system design and modelling, engineering system design, haptic system modelling, computer modelling and design support tools for mechatronic system development and applications of design process in several engineering disciplines, manufacturing and assembly, bio-structure modelling.
Yan has been nominated for the award of Excellent Teaching for the award of Strathclyde Teaching Excellence in for "He is very skillful and hardworking teacher". Individuals interested in this project should email dmem-pgr-recruitment strath.
Machine Learning in Robotics – 5 Modern Applications | Emerj
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Hidden Markov model for dynamic obstacle avoidance of mobile robot navigation Abstract: Models and control strategies for dynamic obstacle avoidance in visual guidance of mobile robots are presented. Characteristics that distinguish the visual computation and motion control requirements in dynamic environments from that in static environments are discussed.
Smarter robot arms
Objectives of the vision and motion planning are formulated, such as finding a collision-free trajectory that takes account of any possible motions of obstacles in the local environments. Such a trajectory should be consistent with a global goal or plan of the motion and the robot should move at as high a speed as possible, subject to its kinematic constraints. A stochastic motion-control algorithm based on a hidden Markov model is developed.
Obstacle motion prediction applies a probabilistic evaluation scheme.