Robotics Middleware: A Comprehensive Literature Survey and Attribute-Based Bibliography

Abstract

Autonomous robots are complex systems that require the interaction between numerous heterogeneous components (software and hardware). Because of the increase in complexity of robotic applications and the diverse range of hardware, robotic middleware is designed to manage the complexity and heterogeneity of the hardware and applications, promote the integration of new technologies, simplify software design, hide the complexity of low-level communication and the sensor heterogeneity of the sensors, improve software quality, reuse robotic software infrastructure across multiple research efforts, and to reduce production costs. This paper presents a literature survey and attribute-based bibliography of the current state of the art in robotic middleware design. The main aim of the survey is to assist robotic middleware researchers in evaluating the strengths and weaknesses of current approaches and their appropriateness for their applications. Furthermore, we provide a comprehensive set of appropriate bibliographic references that are classified based on middleware attributes.

Data Exchange Platform Dedicated to Distributed Control Systems

“…For example, NASA has been working on CLARAty platform, which has been designed for the development and maturation of various research technologies. CLARAty platform is currently used in the Mars Exploration Program and Rover Mission [9,10]. The most popular open solutions are: Player, MIRO, ROS, OROCOS and RT-Middleware [9].…”

“…CLARAty platform is currently used in the Mars Exploration Program and Rover Mission [9,10]. The most popular open solutions are: Player, MIRO, ROS, OROCOS and RT-Middleware [9]. The main purpose of the above systems is to provide software genericness of higher-level applications and to provide communication primitives [12].…”

Computational time scales for social interaction in multi-scale robotics

“…In this chapter, robot control designs and architectures and the various types of robotic functionalities and co-dependent relationships that are enabled are specifically examined. As autonomous robots are complex systems that require interaction between numerous heterogeneous components of both software and hardware [51], the organisation of AI in autonomous robot control is a significant problem and challenge that needs to be addressed when designing and implementing robots [16]. Robotic cognitive organisation and control architectures are a critical influence on computational performance to support demanding functional requirements within constantly changing operational environments and uncertainties.…”

“…The main aims of robotic middleware are to manage the complexity and heterogeneity of the hardware and applications, promote the integration of new technologies, simplify software design, hide the complexity of low-level communication and sensor heterogeneity, improve software quality, reuse robotic software infrastructure across multiple hardware platforms, and to reduce production costs [51]. Standardised modules for certain robot functionalities can also be deployed as part of the robot middleware that maintains its cross-platform support and portability.…”

“…However, in contrast to Miro, the Skilligent robotic framework [55] predominantly focuses on providing a robot behaviour control system, robot behaviours and their extensibility. The core of the Skilligent Robot Learning and Behaviour Control System (see Figure 8) consists of various modules, such as Audio Recognition and Robot Learning, and Behaviour Execution and Coordination [51]. Various external software modules can be attached to the core.…”