Robust adaptive synchronisation of a single-master multi-slave teleoperation system over delayed communication

Considering communication delays in networked multi-robot teleoperation systems, this paper proposes a new control strategy for synchronisation and stability purposes. A single-master and multi-slave (SMMS) networked robotic teleoperation system is considered. Based on a sliding surface combined with a smooth filtering and estimation methodology, a robust adaptive control is developed to guarantee the synchronisation and stability of the system in the presence of network-induced time-varying delays. Extensive simulation studies demonstrate the effectiveness of the developed control scheme

Adaptive type-2 fuzzy neural-network control for teleoperation systems with delay and uncertainties

Interacting with human operators, remote environment, and communication networks, teleoperation systems are considerably suffering from complexities and uncertainties. Managing these is of paramount importance for safe and smooth performance of teleoperation systems. Among the countless solutions developed by researchers, type-2 fuzzy (T2F) algorithms have shown an outstanding performance in modeling complex systems and tackling uncertainties. Moreover, artificial neural networks (NNs) are well known for their adaptive learning potentials. This article proposes an adaptive interval type-2 fuzzy neural-network control scheme for teleoperation systems with time-varying delays and uncertainties. The T2F models are developed based on the experimental data collected from a teleoperation setup over a local computer network. However, the resulted controller is evaluated on an intercontinental communication network through the Internet between Australia and Scotland. Moreover, the slave robot and the remote workspace are completely different and unforeseen. Stability and performance of the proposed control is analyzed by Lyapunov-Krasovskii method. Comprehensive comparative studies demonstrate that the proposed controller outperforms traditional techniques in experimental evaluations

Optimal Autonomous Driving Through Deep Imitation Learning and Neuroevolution

Imitation learning is an efficient paradigm for teaching and controlling intelligent autonomous cars. Obtaining a set of suitable demonstrations to learn an end-to-end policy from raw pixels is a challenging task in imitation learning problems. Deep neural networks have recently shown outstanding results in learning from raw high dimensional data for solving a wide range of real-world applications. The success of deep neural networks depends on finding suitable hyperparameters for constructing network architecture. Besides, designing hand-crafted deep architectures is not an efficient way for achieving the best performance. To address this issue, this paper performs a neuro-evolution method based on genetic algorithm for finding the optimal deep neural networks architecture in terms of hyperparameters. The experimental results show the effectiveness of the proposed approach for training an autonomous vehicl

Robust collaboration of a haptically-enabled double-slave teleoperation system under random communication delays

Communication delays are known to create stability and performance issues in multilateral teleoperation systems. Multilateral teleoperation configurations usually include more than two communication channels, which can become problematic for robot control when limitations in network bandwidth results in delays and uncertainties in data transmission routes. This study develops a sliding surface based on the synchronization errors characterized between each sides of the considered multilateral teleoperation system. Here, two slave robots receive commands from the master system to cooperatively execute the desired teleoperation task in the remote, shared workspace. Lyapunov stability analysis approach guarantees the performance of the proposed controller. Moreover, the effectiveness of the controller is experimentally evaluated through a real-world Internet-based double-slave teleoperation system