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

Human guided cooperative robotic agents in smart home using beetle antennae search

This brief focuses on the problem of fixed-time adaptive trajectory tracking control for a quadrotor unmanned aerial vehicle (QUAV) subject to error constraints. By virtue of the fixed-time command filter, the phenomenon of “explosion of complexity" (EOC) existing in the conventional backstepping method is successfully eliminated, meanwhile the effect of filtered error is skillfully removed via a new fractional power error compensation mechanism. By combining the prescribed performance control and backstepping design method together with command filter technique, a fixed-time adaptive control strategy is established. It is proved that all signals of the closed-loop system are fixed-time bounded, and the position and attitude tracking errors of the QUAV approach to an arbitrarily small region of the original point within the predefined performance bounds in a fixed time. Finally, simulation results are given to show the availability of the presented fixed-time control algorithm.

Fixed-Time Prescribed Performance Adaptive Trajectory Tracking Control for a QUAV

Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are composed of latency-sensitive, data-heavy, and compute-intensive tasks. The robots, however, are constrained in the computational power and storage capacity. The concept of multi-agent cloud robotics enables robot-to-robot cooperation and creates a complementary environment for the robots in executing large-scale applications with the capability to utilize the edge and cloud resources. However, in such a collaborative environment, the optimal resource allocation for robotic tasks is challenging to achieve. Heterogeneous energy consumption rates and application of execution costs associated with the robots and computing instances make it even more complex. In addition, the data transmission delay between local robots, edge nodes, and cloud data centres adversely affects the real-time interactions and impedes service performance guarantee. Taking all these issues into account, this paper comprehensively surveys the state-of-the-art on resource allocation and service provisioning in multi-agent cloud robotics. The paper presents the application domains of multi-agent cloud robotics through explicit comparison with the contemporary computing paradigms and identifies the specific research challenges. A complete taxonomy on resource allocation is presented for the first time, together with the discussion of resource pooling, computation offloading, and task scheduling for efficient service provisioning. Furthermore, we highlight the research gaps from the learned lessons, and present future directions deemed beneficial to further advance this emerging field.

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Enhanced Beetle Antennae Search with Zeroing Neural Network for online solution of constrained optimization

In this brief, we presented a framework for the trajectory optimization of the 5-link Biped Robot using the Beetle Antennae Search (BAS) algorithm. The biped robot is highly non-linear and has complex dynamical modeling. It is challenging to obtain a closed-form solution for the Robot. The robot modeling has two stages, i.e., the optimal trajectory generation and the robust control of a robot. The conventional methodologies treat both problems separately and are computationally expensive. This brief presented an optimization problem that combines the trajectory generation and control problem of a 5-link biped robot. We employed Beetle Antennae Search (BAS) for the problem’s online-solution, an intelligent online optimization approach. BAS is a single-particle searching algorithm, which makes it robust, computationally, and memory-wise efficient. We applied BAS on two non-linear systems, i.e., inverted pendulum and 5-link biped robot, and convincing results are obtained with BAS.

Ameer Tamoor Khan

Papers

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

Human guided cooperative robotic agents in smart home using beetle antennae search

Human guided cooperative robotic agents in smart home using beetle antennae search

Enhanced Beetle Antennae Search with Zeroing Neural Network for online solution of constrained optimization

Trajectory Optimization of 5-Link Biped Robot Using Beetle Antennae Search