Control framework for cooperative robots in smart home using bio-inspired neural network

In this paper, we present a model-free tracking controller for a cooperative mobile-manipulators, which are the cornerstone for future smart homes. The mobile-manipulators are common in industries as they are flexible and mobile, and there are several control frameworks for their industrial applications. However, these control algorithms do not consider the unique complex environment of a smart home, which requires a specialized and customized control. For example, elderly care robots in smart homes are not monitored by skilled users in that particular field, as the goals are different in the home environment, so the controller should also be different. The paper is centered around the tracking control problem of the mobile-manipulators to perform tasks individually and cooperatively. We used an optimization-driven approach to formulate an optimization problem that includes our set goals. We then used a zeroing neural network with beetle antennae search (ZNNBAS) algorithm to solve it. ZNNBAS is a nature-inspired metaheuristic algorithm inspired by the food searching nature of the beetles. It also incorporates the parallel-processing capability of ZNN, which further enhances the computational power and efficiency of the system. The paper also deals with the theoretical analysis of ZNNBAS and proves that it is stable and convergent. To test our algorithm, we used a simulated model of IIWA14 (KUKA LBR), mounted on the P3-DX mobile platform. We used two mobile-manipulators and tested them in the following scenarios: (1) Robots working independently, (2) Robots working cooperatively. The simulation results show that ZNNBAS was able to accurately and robustly complete the assigned tasks. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper introduces a model-free tracking controller for cooperative mobile-manipulators in smart homes, utilizing an optimization-driven approach and a novel algorithm called ZNNBAS. The algorithm is proven to be stable and efficient in achieving accurate and robust task completion for the mobile-manipulators.