Field Robotics and Artificial Intelligence (AI)

Special Issue Editors


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Guest Editor
Department of Engineering, La Trobe University, Melbourne, VIC 3086, Australia
Interests: robotics; sensors; IoT; educational escape rooms

E-Mail Website
Guest Editor
Department of Engineering, La Trobe University, Melbourne, VIC 3086, Australia
Interests: field robotics; condition assessment; sensing; electronic design; mechatronics

Special Issue Information

Dear Colleagues,

The use of robotics and artificial intelligence to solve problems, improve safety and yield new insights across a growing number of application domains is a major global trend. The diverse field of robotics applications is expanding, and ranges from medicine and agriculture to condition assessment and factory floors. Recent advances in sensor technologies, along with on-board processing resources, have enabled the application of many robotics with higher levels of autonomy and intelligent decision making. The focus of this Special Issue is to highlight how researchers employ robotics and/or artificial intelligence in the field to address some of these key application domains.

The scope of this Special Issue includes (but is not limited to):

  • Field robotics systems, trials and applications;
  • Robotic sensor technologies with field applications;
  • Applications of AI to robotics, vision and sensor domains;
  • Visual and non-visual sensor algorithms and implementations;
  • Mobile robotics and navigation;
  • Human–robot interactions;
  • Bio-inspired robotics;
  • Computational intelligence in robotics;
  • Haptics;
  • Algorithms;
  • Video and image processing;
  • Field trials;
  • Mechatronics systems and applications;
  • Swarm robotics;
  • Social robotics;
  • IoT robotics crossover applications.

This Special Issue will act as a repository for state-of-the-art research on the use, breadth and implementation of robotics to address real-world problems across different domains. The repository will supplement existing literature by demonstrating and evaluating the use of robots in the field to demonstrate the implementation and use of robotics in contrast to simply theoretical models.

Dr. Robert Ross
Dr. Alex Stumpf
Guest Editors

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Keywords

  • field robotics
  • robotic systems
  • sensing
  • vision
  • AI

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Published Papers (1 paper)

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Research

18 pages, 2027 KiB  
Article
Runtime Verification-Based Safe MARL for Optimized Safety Policy Generation for Multi-Robot Systems
by Yang Liu and Jiankun Li
Big Data Cogn. Comput. 2024, 8(5), 49; https://doi.org/10.3390/bdcc8050049 - 16 May 2024
Cited by 1 | Viewed by 1011
Abstract
The intelligent warehouse is a modern logistics management system that uses technologies like the Internet of Things, robots, and artificial intelligence to realize automated management and optimize warehousing operations. The multi-robot system (MRS) is an important carrier for implementing an intelligent warehouse, which [...] Read more.
The intelligent warehouse is a modern logistics management system that uses technologies like the Internet of Things, robots, and artificial intelligence to realize automated management and optimize warehousing operations. The multi-robot system (MRS) is an important carrier for implementing an intelligent warehouse, which completes various tasks in the warehouse through cooperation and coordination between robots. As an extension of reinforcement learning and a kind of swarm intelligence, MARL (multi-agent reinforcement learning) can effectively create the multi-robot systems in intelligent warehouses. However, MARL-based multi-robot systems in intelligent warehouses face serious safety issues, such as collisions, conflicts, and congestion. To deal with these issues, this paper proposes a safe MARL method based on runtime verification, i.e., an optimized safety policy-generation framework, for multi-robot systems in intelligent warehouses. The framework consists of three stages. In the first stage, a runtime model SCMG (safety-constrained Markov Game) is defined for the multi-robot system at runtime in the intelligent warehouse. In the second stage, rPATL (probabilistic alternating-time temporal logic with rewards) is used to express safety properties, and SCMG is cyclically verified and refined through runtime verification (RV) to ensure safety. This stage guarantees the safety of robots’ behaviors before training. In the third stage, the verified SCMG guides SCPO (safety-constrained policy optimization) to obtain an optimized safety policy for robots. Finally, a multi-robot warehouse (RWARE) scenario is used for experimental evaluation. The results show that the policy obtained by our framework is safer than existing frameworks and includes a certain degree of optimization. Full article
(This article belongs to the Special Issue Field Robotics and Artificial Intelligence (AI))
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