Open Robot Control Software Research Articles

Distributed real-time control architecture for electrohydraulic humanoid robots

PurposeThis paper aims to develop an adaptable control architecture for electrohydraulic humanoid robots (HYDROïD) that emulate the functionality of the human nervous system. The developed control architecture overcomes the limitations of classical centralized and decentralized systems by distributing intelligence across controllers.Design/methodology/approachThe proposed solution is a distributed real-time control architecture with robot operating system (ROS). The joint controllers have the intelligence to make decisions, dominate their actuators and publish their state. The real-time capabilities are ensured in the master controller by using a Preempt-RT kernel beside open robot control software middleware to operate the real-time tasks and in the customized joint controllers by free real-time operating systems firmware. Systems can be either centralized, where all components are connected to a central unit or decentralized, where distributed units act as interfaces between the I/Os and the master controller when the master controller is without the ability to make decisions.FindingsThe proposed architecture establishes a versatile and adaptive control framework. It features a centralized hardware topology with a master PC and distributed joint controllers, while the software architecture adapts based on the task. It operates in a distributed manner for precise, force-independent motions and in a decentralized manner for tasks requiring compliance and force control. This design enables the examination of the sensorimotor loop at both low-level joint controllers and the high-level master controller.Originality/valueIt developed a control architecture emulating the functionality of the human nervous system. The experimental validations were performed on the HYDROïD. The results demonstrated 50% advancements in the update rate compared to other humanoids and 30% in the latency of the master processor and the control tasks.

Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems

Multiagent control system (MACS) has become a promising solution for solving complex control problems. Using the advantages of MACS-based design approaches, a novel solution for advanced control of mechatronic systems has been developed in this paper. The study has aimed at integrating learning control into MACS. Specifically, learning feedforward control (LFFC) is implemented as a pattern for incorporation in MACS. The major novelty of this work is that the feedback control part is realized in a real-time periodic MACS, while the LFFC algorithm is done on-line, asynchronously, and in a separate non-real-time aperiodic MACS. As a result, a MACS-based LFFC design method has been developed. A second-order B-spline neural network (BSN) is used as a function approximator for LFFC whose input-output mapping can be adapted during control and is intended to become equal to the inverse model of the plant. To provide real-time features for the MACS-based LFFC system, the open robot control software (OROCOS) has been employed as development and runtime environment. A case study using a simulated linear motor in the presence of nonlinear cogging and friction force as well as mass variations is used to illustrate the proposed method. A MACS-based LFFC system has been designed and implemented for the simulated plant. The system consists of a setpoint generator, a feedback controller, and a time-index LFFC that can learn on-line. Simulation results have demonstrated the applicability of the design method.

The complete integration of MissionLab and CARMEN

Nowadays, a major challenge in the development of advanced robotic systems is the creation of complex missions for groups of robots, with two main restrictions: complex programming activities not needed and the mission configuration time should be short (e.g. Urban Search And Rescue). With these ideas in mind, we analysed several robotic development environments, such as Robot Operating System (ROS), Open Robot Control Software (OROCOS), MissionLab, Carnegie Mellon Robot Navigation Toolkit (CARMEN) and Player/Stage, which are helpful when creating autonomous robots. MissionLab provides high-level features (automatic mission creation, code generation) and a graphical mission editor that are unavailable in other significant robotic development environments. It has however some weaknesses regarding its map-based capabilities. Creating, managing and taking advantage of maps for localization and navigation tasks are among CARMEN’s most significant features. This fact makes the integration of MissionLab with CARMEN both possible and interesting. This article describes the resulting robotic development environment, which makes it possible to work with several robots, and makes use of their map-based navigation capabilities. It will be shown that the proposed platform solves the proposed goal, that is, it simplifies the programmer’s job when developing control software for robot teams, and it further facilitates multi-robot deployment task in mission-critical situations.

Adaptive control of a 3-DOF parallel manipulator considering payload handling and relevant parameter models

Abstract Model-based control improves robot performance provided that the dynamics parameters are estimated accurately. However, some of the model parameters change with time, e.g. friction parameters and unknown payload. Particularly, off-line identification approaches omit the payload estimation (due to practical reasons). Adaptive control copes with some of these structural uncertainties. Thus, this work implements an adaptive control scheme for a 3-DOF parallel manipulator. The controller relies on a novel relevant-parameter dynamic model that permits to study the cases in where the uncertainties affect: (1) rigid body parameters, (2) friction parameters, (3) actuator dynamics, and (4) a combination of the former cases. The simulations and experiments verify the performance of the proposed controller. The control scheme is implemented on the modular programming environment Open Robot Control Software (OROCOS). Finally, an experimental setup evaluates the controller performance when the robot handles a payload.