Robot navigation in the social environment: Design challenges

June 28, 2022 by Kiran Jot Singh; Divneet Singh Kapoor; Balwinder Singh Sohi

robot with arrow coming out of it — ©SHUTTERSTOCK.COM/ZAPP2PHOTO, ARROWS— ©SHUTTERSTOCK.COM/TUULIJUMALA

As the number of robots is growing in our social environment, it is very important for them to understand and anticipate human behavior. One of the crucial skills for a robot is its navigation in the social environment. Several challenges are encountered when planning for the navigation of a robot in the presence of humans. It is very important for us to articulate these issues and the ways to address them. This will fuel the development of an optimal socially aware navigation framework catering to human comfort, safety, and acceptance during robot navigation. In this article, we classify various challenges and suggest probable solutions that can support roboticists for efficient robot navigation design.

For more about this article see link below.

https://ieeexplore.ieee.org/document/9810041

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

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