Technical Lecture Program on Task Allocation using a Team of Robots
Abstract of the talk: Task allocation in a multi-robot system is required to determine which robots should execute which tasks, in order to achieve the overall system goals. Task allocation is an important aspect of many multi-robot systems, which ensures coordinated team behaviour. In this talk, we describe the features and complexity of multi-robot task allocation (MRTA) problems, which are determined by the requirements of the particular domain. We illustrate how to classify these problems in a systematic manner and show some real-life applications. We show a very general formulation of the task allocation problem that generalizes several versions that are well-studied in the existing literature. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate. We describe how the problem can be varied depending on the feasibility constraints, objective functions, and the level of dynamically changing information. We illustrate an algorithm to solve a particular variant of the MRTA problem and show how this algorithm performs better than current state-of-the-art algorithms on real datasets. We conclude the talk with some directions for future research.
Date and Time
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- Date: 15 Jan 2025
- Time: 04:00 PM to 06:00 PM
- All times are (UTC+05:30) Chennai
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- Amitava Dey Memorial Hall
- Jadavpur University
- KOLKATA, West Bengal
- India 700032
- Building: Computer Science and Engineering
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- Co-sponsored by CSE Department, Jadavpur University
Speakers
Arindam Pal of Director of Data Science and Optimization at Optym
Task Allocation using a Team of Robots
Abstract of the talk: Task allocation in a multi-robot system is required to determine which robots should execute which tasks, in order to achieve the overall system goals. Task allocation is an important aspect of many multi-robot systems, which ensures coordinated team behaviour. In this talk, we describe the features and complexity of multi-robot task allocation (MRTA) problems, which are determined by the requirements of the particular domain. We illustrate how to classify these problems in a systematic manner and show some real-life applications. We show a very general formulation of the task allocation problem that generalizes several versions that are well-studied in the existing literature. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate. We describe how the problem can be varied depending on the feasibility constraints, objective functions, and the level of dynamically changing information. We illustrate an algorithm to solve a particular variant of the MRTA problem and show how this algorithm performs better than current state-of-the-art algorithms on real datasets. We conclude the talk with some directions for future research.
Biography:
Arindam Pal is a Director of Data Science and Optimization at Optym, where he makes transportation companies more profitable and efficient through optimization and AI. His research interests are algorithms, artificial intelligence, data science, optimization, and machine learning. He has more than 17 years of industrial research experience in Data Science, Machine Learning, Cyber Security, and Optimization, working for world-class companies like Microsoft, Yahoo!, Novell, CSIRO, Cognizant, Optym, and TCS Research. He has published academic papers in reputed conferences and journals and was granted patents in various countries like India, USA, and Europe. He is a technical program member for several reputed conferences and a technical reviewer for many renowned journals. He earned his Ph.D. in Computer Science from the Indian Institute of Technology Delhi. He is a Senior Member of both ACM and IEEE.
Address:Director of Data Science and Optimization at Optym,
