Enhancement of High-Fidelity Haptic Feedback Through Multimodal Adaptive Robust Control for Teleoperated Systems

Pediredla, Vijay Kumar; Annamraju, Srikar; N, Ananda Rangan; Thondiyath, Asokan

doi:10.1109/jsyst.2020.3032965

Cited by 2 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The V-REP software receives information regarding the joint angles of a snake-like MR in every time step t from a Python script to perform locomotion. A snakelike MR consisting of four D-MR modules simulated in V-REP and Python script generates the joints angle based on (5). Each D-MR module had two joints that rotate along the z-axis in a range of -90 • to 90 • and has a maximum actuator torque of 0.52 Nm.…”

Section: Simulation Results and Comparisonmentioning

confidence: 99%

“…A snake-like MR uses linear progression motion to propel its body forward when each joint actuates at every time step t according to (5). As displayed in Fig.…”

Section: A Locomotion Controlmentioning

confidence: 99%

“…Often, these robots encounter situations where an MR may need to change gaits for locomotion at different stages. For example, during the teleoperation, an MR equipped with a camera can move at a fast gait speed for locomotion to reach the destination on a known terrain while using a slower gait speed in an unknown environment [4], [5]. In the oil and gas industries, snake robots might inspect pipes underwater for a longer duration, requiring energy-efficient gait control; during an oil leak, the robot needs to move fast to support the repair [6].…”

Section: Introductionmentioning

confidence: 99%

“…vary the speed of the snake-like MR and result in different values of power consumption and average velocity during each time slot k. The control parameters mentioned above are applied in(5) to generate a gait for the snake-like MR during each time slot k. Each episode contains K learning steps, and one learning step orThis article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Energy-Efficient Gait Optimization of Snake-Like Modular Robots by Using Multiobjective Reinforcement Learning and a Fuzzy Inference System

et al. 2022

View full text Add to dashboard Cite

Snake-like modular robots (MRs) are highly flexible, but, to traverse a challenging terrain or explore a region of interest, MR needs to attain efficient locomotion depending on a tradeoff between objectives like forward velocity and power consumption of the robot. The objectives can vary with different weights depending upon the situation, reflecting relative objective importance. This study developed a multiobjective reinforcement learning algorithm based on a fuzzy inference system (FI-MORL) to select the most appropriate gait parameters of snake-like MRs according to the objective weights. The developed algorithm employs a fuzzy inference system to reduce the number of states in an environment, which results in faster learning. The proposed approach uses the previously learned experience to rapidly achieve the best objective values in response to a change in weights. While setting equal importance to the objectives, FI-MORL delivers superior performance than single-objective reinforcement learning algorithms by consuming 2% less power and gaining 2.5% higher velocity since it mitigates the effect of weight change, similar performance found comparing Actor-Critic algorithm. Likewise, the proposed method outperforms by consuming 14% less power and achieving 11% higher velocity than traditional methods like Proximal Policy Optimization, Deep Q-Network, and Vanilla Policy Gradient. Even after weight change, FI-MORL achieved a 14% higher reward than the above methods. The proposed FI-MORL framework can effectively converge quicker and efficiently handle the changes in objective weights.

show abstract

Section: Simulation Results and Comparisonmentioning

confidence: 99%

“…A snake-like MR uses linear progression motion to propel its body forward when each joint actuates at every time step t according to (5). As displayed in Fig.…”

Section: A Locomotion Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Energy-Efficient Gait Optimization of Snake-Like Modular Robots by Using Multiobjective Reinforcement Learning and a Fuzzy Inference System

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The proposed SMC-DOB performance was evaluated through simulation and experiment, confirming it tackles the chattering problem and significantly improved system tracking performance compared with traditional SMC. The authors in [180] proposed a multimodal adaptive robust control (MARC) algorithm for haptic teleoperated systems to enhance haptic feedback quality. The proposed SMC ensured system stability and provided highfidelity transparency while handling varying time delay, position/velocity error, parametric uncertainty, external forces, and environment nonlinearities.…”

mentioning

confidence: 99%

QoS Provisioning: Key Drivers and Enablers Toward the Tactile Internet in Beyond 5G Era

et al. 2022

View full text Add to dashboard Cite

The Tactile Internet has become a revolution for Internet technology, greatly improving the transmission of skill sets (audio, video, text, and haptics) over communication channels compared with traditional triple-play data (audio, video, text). It is a strong candidate to support next-generation delay-sensitive and loss-intolerant smart applications. However, stringent requirements for the Tactile Internet, including ultra-low latency, ultra-high reliability, high availability, and ultra-security, present critical challenges to ensure Quality of Service (QoS). Consequently, several approaches have been proposed to meet these QoS requirements. This article reviews QoS provisioning approaches for the Tactile Internet. First, we present key concepts for the fifth-generation and beyond technologies, Tactile Internet, and haptic communication. Second, we discuss the Tactile Internet use cases along with strict QoS requirements. Third, we classify existing solutions, including haptic codecs, control system designs, hybrid schemes, and intelligent prediction models; provide in-depth discussion regarding these approaches to improve QoS for the Tactile Internet applications; and investigate strengths and weaknesses for each proposed solution. Finally, we present open research challenges and discuss potential future research avenues to realize the Tactile Internet services.

show abstract

Enhancement of High-Fidelity Haptic Feedback Through Multimodal Adaptive Robust Control for Teleoperated Systems

Cited by 2 publications

References 40 publications

Energy-Efficient Gait Optimization of Snake-Like Modular Robots by Using Multiobjective Reinforcement Learning and a Fuzzy Inference System

Energy-Efficient Gait Optimization of Snake-Like Modular Robots by Using Multiobjective Reinforcement Learning and a Fuzzy Inference System

QoS Provisioning: Key Drivers and Enablers Toward the Tactile Internet in Beyond 5G Era

Contact Info

Product

Resources

About