Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/178522
Title: Hardware schemes for smarter indoor robotics to prevent the backing crash framework using field programmable gate array-based multi-robots
Authors: Basha, Mudasar
Siva Kumar, Munuswamy
Chinnaiah, Mangali Chinna
Lam, Siew-Kei
Srikanthan, Thambipillai
Narambhatla, Janardhan
Dodde, Hari Krishna
Dubey, Sanjay
Keywords: Computer and Information Science
Issue Date: 2024
Source: Basha, M., Siva Kumar, M., Chinnaiah, M. C., Lam, S., Srikanthan, T., Narambhatla, J., Dodde, H. K. & Dubey, S. (2024). Hardware schemes for smarter indoor robotics to prevent the backing crash framework using field programmable gate array-based multi-robots. Sensors, 24(6), 1724-. https://dx.doi.org/10.3390/s24061724
Journal: Sensors
Abstract: The use of smart indoor robotics services is gradually increasing in real-time scenarios. This paper presents a versatile approach to multi-robot backing crash prevention in indoor environments, using hardware schemes to achieve greater competence. Here, sensor fusion was initially used to analyze the state of multi-robots and their orientation within a static or dynamic scenario. The proposed novel hardware scheme-based framework integrates both static and dynamic scenarios for the execution of backing crash prevention. A round-robin (RR) scheduling algorithm was composed for the static scenario. Dynamic backing crash prevention was deployed by embedding a first come, first served (FCFS) scheduling algorithm. The behavioral control mechanism of the distributed multi-robots was integrated with FCFS and adaptive cruise control (ACC) scheduling algorithms. The integration of multiple algorithms is a challenging task for smarter indoor robotics, and the Xilinx-based partial reconfiguration method was deployed to avoid computational issues with multiple algorithms during the run-time. These methods were coded with Verilog HDL and validated using an FPGA (Zynq)-based multi-robot system.
URI: https://hdl.handle.net/10356/178522
ISSN: 1424-8220
DOI: 10.3390/s24061724
Schools: College of Computing and Data Science 
School of Computer Science and Engineering 
Rights: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CCDS Journal Articles

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