Design and implementation of a fault-tolerant system for industrial robots under hostile operating conditions

Highlights

• The loss of information causes degradation in the control of the path of the robot.

• Strategy for protecting trajectory data to be executed by industrial robots working under hostile operating conditions.

• Information is protected through a convolutional code.

• The controller response is evaluated under communication disturbances by means of performance indexes.

• The experimental results show that corrupted data is corrected allowing the optimal operation of the robot.

Abstract

This paper presents the design and implementation of a strategy for protecting trajectory data that will be executed by industrial robots working under hostile operating conditions. Specifically, the Controller Area Network protocol is used to send information about trajectories between two terminals: the controller and the robot. To prevent the adverse effects of corrupt information on robot performance, information is protected through a convolutional code. In this way, the receptor detects and corrects the bits damaged during communication. The communication system designed is validated by assessing the performance of a 3-DoF¹ planar robot that executes default trajectories under hostile operating conditions. The controller response is evaluated under communication disturbances by means of performance indexes and considering two scenarios: with or without the convolutional code designed.

Introduction

The demand for industrial robots has grown exponentially since 2010. Meeting rising global demands in manufacturing output and quality, as well as reducing operation costs, are some of the challenges faced by these robots [1]. Due to the natural trend toward automation, the applications for them have expanded, for example, to the electrical and electronics, rubber and plastic, pharmaceutical, makeup, and metal industries, among others. Between 2018 and 2021, estimations indicate that almost 2.1 million industrial robots will be installed in manufacturing plants all over the world [2]. This has brought some advances to the field, such as an increase in productivity, more continuity in production lines and fewer accidents in risky operations.

Modern manufacturing systems should comply with high reliability and availability standards in order to guarantee efficient planning and optimization. However, the operation of industrial robots generates intermittent interactions in manufacturing systems [3]. Autonomous robots are intelligent systems that accept tasks, change their description into executable commands and, finally, autonomously perform them in a complex, unknown and changing environment [4]. These embedded systems are heterogeneous systems whose hardware and software are designed to solve specific problems within a larger system to which they belong [5]. However, the presence of external disturbances, system model inaccuracies and measurement noise hinder tracking performance and even devastate system stability [6].

The reliability analyses for industrial robots test possible operating faults that may occur during their operation. Said analysis considers variables that allow for the assessment of the state of each robot joint, such as average speed, average torque, accumulated absolute position, number of emergency stops, execution time in hours, waiting time in hours and corrupt information, among others [7].

Since, in general, industrial robots operate under hostile conditions [8], their communication systems are exposed to electromagnetic fields, connectors disconnections, short circuits, bus ruptures and high data load [9]. Consequently, during operation, both the control and communication systems of these robots are highly susceptible to faults. In this context, information losses or corrupted data can cause failures or interruptions of the productive process, leading to economic losses, equipment damage and unsafe conditions for the staff around the robot, among other adverse phenomena. Therefore, industrial robots should be equipped with systems to reconfigure information, which allows robots to receive and/or send information related to the execution of specific tasks in a comprehensive way, despite external disturbances.

To ensure that the transmitted message is received complete, communication systems can include error detection and correction codes. To achieve this, two methods are considered: backward error correction, and forward error correction. In the former case, parity control only identifies the error and requests the retransmission of information. In the latter case, corrupted data is reconstructed through algebraic methods denominated block codes [10] such as Hamming code [11], Bose Chaudhuri Hocquenghem (BCH) code [12] and Reed-Solomon code [13]. Emergent applications require the transmission of short or medium units, for example, communication between machines, the Internet of Things, remote command links and messenger services, among others. As a consequence, the interest in coding has seen considerable growth [14].

Specifically, the Controller Area Network (CAN) protocol, which is widely used in data networks for vehicles, process automation and real-time embedded systems [15], uses backward error correction. Backward error correction is an algebraic procedure of Cyclic Redundancy Verification. This method includes a field to which verification bits are added in the information grid and then a calculation is executed in the receptor, in which verification bits are collated with the bits received to detect errors without correcting them [16].

Forward error correction methods include a great variety of algebraic procedures denominated coding, which has been used to improve data transmission systems. For example, in [17] a BCH coder and decoder (7,4,1) is designed as a feasible alternative for possible faults or omissions from a traditional BCH coder and decoder, since it is equipped with a radial basis neural network for the detection and correction of errors in a digital communication system. Another application is presented in [18], in which the Modbus-RTU is used to design systematic codes to add redundant information to messages and thereby guarantee the integrity of transmitted data. In [19], the concatenation of Reed Solomon and convolutional codes is proposed for the implementation of links in an optical communication channel. This study concluded that the performance of this concatenation is better than using the two codes separately. Moreover, [20] employs the Modbus-RTU for error correction and detection through a systematic code implemented in a dsPIC30F microcontroller. In [21], the application of a convolutional code to an AWGN² channel is presented, in which the error detection capacity is improved by means of a cyclic redundancy verification code. In [22], using the Reed Solomon code, a substantial reduction of error occurrence is achieved in an electric line communication system, mitigating the effects of background noise on the change signal, which affects the BFSK³ modulation.

Since convolutional codes offer lower bit-error rates than block codes [19,21,23], this work uses them to design and implement a strategy for protecting the data of trajectories to be executed by industrial robots that work under hostile operating conditions. This paper is organized as follows. First, the general system structure is described and a convolutional code and Viterbi decoder are established. Then, the design and implementation of a fault-tolerant system, nodes, trajectories and a controller of the robot are presented. Finally, the results of the experimental tests are analyzed considering two scenarios, namely with or without the convolutional code designed and implemented in this work.