A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation

Tang, Hengliang; Wang, Anqi; Xue, Fei; Yang, Jiaxin; Cao, Yang

doi:10.1109/access.2021.3062457

Cited by 34 publications

(12 citation statements)

References 28 publications

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“…This work has similar baseline design attributes with robotic smart homes based on stabilized feedback Episodic memory (SF-EM) [58], stewards robot smart homes [59], humanoid defense smart homes [60], Indoor autonomous robots [61], and Smart home activities IoT [62]. These similarities are highlighted below [63], [64], [65]:…”

Section: Summary Of Related Work (Automated Home)mentioning

confidence: 93%

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

Okafor¹,

Longe²

2022

IJACSA

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In the next 5 to 10 years, digital Artificial Intelligence with Machine Circuit Learning Algorithms (MCLA) will become the mainstream in complex automated robots. Its power concerns, ethical perspectives, including the issues of digital sensing, actuation, mobility, efficient processcomputation, and wireless communication will require advanced neuromorphic process variable controls. Existing home automated robots lack memristic associative memory. This work presents Cyber-Physical Home Automation System (CPHAS) using Memristive Reconfigurable Algorithmic State Machine (MRASM) chart. A process control architecture that supports Concurrent Wireless Data Streams and Power-Transfer (CWDSPT) is developed. Unlike legacy systems with powersplitting (PS) and time-switching (TS) controls, the MRASM-ROBOT explores granular wireless signal controls through unmodulated high-power continuous wave (CW). This transmits infinite process variables using Orthogonal Space-Time Block Code (OSTBC) for interference reduction. The CWDSPT transmitter and receiver circuit design for signal processing are implemented with complexity noise-error reduction during telemetry data decoding. Received signals are error-buffered while gathering control variables' status.Transceiver Memristive neuromorphic circuits are introduced for computational acceleration in the design. Hardware circuit design is tested for system reliability considering the derived schematic models for all process variables. Under small range space diversity, the system demonstrated significant memory stabilization at the synchronous iteration of the synaptic circuitry.

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Section: Summary Of Related Work (Automated Home)mentioning

confidence: 93%

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

Okafor¹,

Longe²

2022

IJACSA

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“…In the SOL domain, Irannezhad, Prato, and Hickman (2020) successfully incorporate a multi-agent RL solution into a full-fledged port decision support system and evaluate agent collaboration strategies, showing that a cooperative strategy, rather than one focused on individual reward maximization, results in the highest overall vehicle utilization and lowest travel distance and costs. Tang et al (2021) improve on another model-free RL method, Soft Actor-Critic (SAC) (Haarnoja et al 2018) -an off-policy model like DQN -with improved sample efficiency by incorporating an entropy factor and regularization. The authors apply it to an unmanned warehouse environment where autonomous robotic platforms are used for order-picking.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics

Pingen¹,

Ommeren²,

Leeuwen³

et al. 2022

ICAPS

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Logistics planning is a complex optimization problem involving multiple decision makers. Automated scheduling systems offer support to human planners; however state-of-the-art approaches often employ a centralized control paradigm. While these approaches have shown great value, their application is hindered in dynamic settings with no central authority. Motivated by real-world scenarios, we present a decentralized approach to collaborative multi-agent scheduling by casting the problem as a Distributed Constraint Optimization Problem (DCOP). Our model-based heuristic approach uses message passing with a novel pruning technique to allow agents to cooperate on mutual agreement, leading to a near-optimal solution while offering low computational costs and flexibility in case of disruptions. Performance is evaluated in three real-world field trials with a logistics carrier and compared against a centralized model-free Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach, a Mixed-Integer Linear Programming (MILP)-based solver, and both human and heuristic baselines. The results demonstrate that it is feasible to have virtual agents make autonomous decisions using our DCOP method, leading to an efficient distributed solution. To facilitate further research in Self-Organizing Logistics (SOL), we provide a novel real-life dataset.

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“…In recent years, mobile robot gradually plays a more and more important role in several areas, including rescue and disaster relief (Niroui et al, 2019), logistics and distribution (Tang et al, 2021), medical services (Taylor et al, 2021), fault detection (Karkoub et al, 2021), and so on. Mobile robot often needs to follow pre-defined trajectories to accomplish these applications.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive robust control for trajectory tracking of mobile robot with uncertainties

Xiao

Wang

Jin

et al. 2023

Journal of Vibration and Control

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The main objective of this paper is to handle the trajectory tracking control for a class of three-wheel mobile robot by using a novel adaptive robust control. The mobile robot consists of nonholonomic constraints and uncertainties. The uncertainties include initial condition offset, mass, and moment of inertia of the system, which are time-varying and bounded (unknown). The control force in analytical form is obtained by UK theory, and the adaptive robust control is formulated to handle the uncertainties. For estimating the unknown bounds of uncertainties, the leakage-type adaptive law is designed, which can automatically adjust its own value according to the trajectory tracking errors. Then we verify the uniform boundedness and uniform ultimate boundedness of the proposed control by Lyapunov method. Finally, numerical simulations are conducted to show the trajectory tracking effectiveness of the designed control method.

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A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation

Cited by 34 publications

References 28 publications

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics

A novel adaptive robust control for trajectory tracking of mobile robot with uncertainties

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