Real Robot System Research Articles

A Task-Space Tracking Control Approach for Duct Cleaning Robot Based on Fuzzy Wavelet Neural Network

In this study, a task-space adaptive robust control methodology which takes uncertainties and external disturbances into account is proposed for a class of duct cleaning mobile manipulators. First of all, the configuration of the real duct cleaning robot is introduced, and the Jacobian matrix and the dynamic model of the real robotic system are obtained. Then, the structure of adaptive robust controller based on sliding mode control (SMC) approach and the fuzzy wavelet neural network is detailed, the proposed control approach combines the advantages of SMC which can suppress the external disturbances with the fuzzy wavelet neural network which can compensate the uncertainties by its strong ability to approximate a nonlinear function to an arbitrary accuracy, the stability of the whole robotic control system, the uniformly ultimately boundedness of tracking errors, and the boundedness of fuzzy wavelet neural networks weight estimation errors are all guaranteed based on the Lyapunov stability theory. Finally, simulation results are presented to demonstrate the superior performance of the proposed approach, and experiments are given to illustrate that the proposed approach is useful for real duct cleaning robot system with well performance.

Design and Implementation of Visual Robot Mission Planning

The mission planning system (MPS) has become more prevalent as a high-level reasoning tool in real robotic systems. This paper analyzes the research prospects of MPS and presents the implementation of the core functions of MPS including Socket communication module, client module, server module, program construction method and mission planning module. This paper uses java based socket communication model to achieve two-way communication robot functions, robot task resolution, scheduling and exception handling. This paper also implements a task planner for an instance of shutting down the lights. After the simulation and testing of the system, it reveals that the robots are able to complete the mission and perform task decomposition and scheduling very effectively.

Simplifying the creation and management of utility models in continuous domains for cognitive robotics

Establishing goal/sub-goal hierarchies in robotic motivational systems for open-ended learning situations and modelling this utility in a manner that is useful for robots is an open research problem, especially when the robots’ state-spaces are continuous and may present ambiguities. In these cases, directly obtaining value functions, and in particular, precise Artificial Neural Network based value functions, becomes very difficult. In this paper, this issue is addressed through a new type of coarse utility functions for the representation of motivation. The proposed approach can be used as an intermediate step in order to be able to produce more consistent data for the subsequent training of precise value functions when and where it becomes necessary. This type of coarse utility functions, called Separable Utility Regions (SUR), are based on the use of the variation of sensor values as clues to the position of goals in state space. Moreover, areas in the state-space must be established where there are correlations between the desired direction the system should follow in its state-space towards a goal, and the direction of variation of the values of a particular sensor. The main focus of this paper is on the process of creating sub-goal hierarchies that permit leading the system in a consistent manner towards goals, so that it can autonomously learn to achieve them whatever its starting state. To this end, an approach to sub-goal determination and chaining based on a recursive establishment of consolidated goal domains as new goals for new utility functions is described. The approach is tested on a real robotic system and the results are extensively analysed and discussed.

A case study of robotic simulations using virtual commissioning supported by the use of virtual reality

Virtual Commissioning (VC) is a method and tool for verifying and testing the PLC control program on a virtual digital model of the manufacturing system. It allows to visualize and test the control system before the real commissioning of the production systems. The aim of the research is to implement virtual reality (VR) into the VC method and to verify the mutual interaction of signals between the simulation in VR environment, the digital model of the production system and the control system. The introduction of VR in VC increases the concept by adding more realistic visualization and tracking, which extends its validation capabilities. The changes made in VR virtual environment are transferred to the simulation model and can be validated in a real production system. The real production robotic system transformed into a virtual form will be a case study with its verification. Also will be tested security protocols and proven human interaction with the system to control the system through the virtual HMI (virtual user interface) using VR.

A Fast and Efficient Double-Tree RRT$^*$-Like Sampling-Based Planner Applying on Mobile Robotic Systems

As a variant of rapidly exploring random tree (RRT), RRT $^*$ is an important improvement of sampling-based algorithms. Although it can provide a feasible planning solution with a higher quality, more resources on optimization are required, resulting in a very slow convergence rate, which cannot satisfy the real-time requirements of most autonomous systems. In this paper, we propose a novel approach of RRT $^*$ in collaboration with a double-tree structure to separate the extension and optimization procedure. In our algorithm, the original RRT is employed to explore the unknown environment and to search feasible connecting areas, represented by piecewise lines. Different from the method of anytime RRT $^*$ , the RRT phase in our method is to find different homotopic paths during each iteration. Thereafter, a modified RRT $^*$ is used to obtain an optimal solution. Simulation results on two benchmarks demonstrate an improved performance of our approach in comparison with the original RRT $^*$ and its variants (e.g., DT-RRT). An additional evaluation on two real robotic systems further proves the efficiency of our approach.

Trophallaxis with predetermined energy threshold for enhanced performance in swarms of scavenger robots

We present an agent-based simulation of a group of robots that source their energy from their surrounding environment. The robots forage their energy from localised sources, distributed in their surrounding environment and share energy with each other through “trophallaxis”. We investigate the effect of energy loss during energy transfer by considering two model configurations. In the first configuration, neighbouring pairs of robots shared the sum of their combined energy equally, at each time-step, without considering the energy levels of each robot. In contrast, in the second configuration, only donor and recipient robots averaged their energy. The roles of donor and recipient were defined by predetermined energy thresholds. Levy walk behaviour was employed to improve foraging ability. The results show that bio-inspired trophallaxis and Levy walk enhances the performance of the groups of scavenger robots. Our results indicate that trophallaxis is particularly effective considering energy loss during energy transfer. This suggests us the possibility to apply the trophallaxis models to the real robot systems where energy loss occurs during energy transfer.

Dynamic Locomotion Gaits of a Compliantly Actuated Quadruped With SLIP-Like Articulated Legs Embodied in the Mechanical Design

The spring loaded inverted pendulum (SLIP) model has been extensively shown to be fundamental for legged locomotion. However, the way this low-order template model dynamics is anchored in high-dimensional articulated multibody systems describing compliantly actuated robots (and animals) is not obvious and has not been shown so far. In this letter, an articulated leg mechanism and a corresponding quadrupedal robot design are introduced, for which the natural oscillation dynamics is structurally equivalent to the SLIP. On the basis of this property, computationally simple and robust control methods are proposed, which implement the gaits of pronking, trotting, and dynamic walking in the real robotic system. Experiments with a compliantly actuated quadruped featuring only low-performance electrical drives validate the effectiveness of the proposed approach.

Fault-Tolerant Control of a CPG-Governed Robotic Fish

Abstract Fault tolerance is essential for the maneuverability of self-propelled biomimetic robotic fish in real-world aquatic applications. This paper explores the fault-tolerance control problem of a free-swimming robotic fish with multiple moving joints and a stuck tail joint. The created control system is composed of two main components: a feedback controller and a feedforward compensator. Specifically, the bio-inspired central pattern generator-based feedback controller is designed to make the robotic fish robust to external disturbances, while the feedforward compensator speeds up the convergence of the overall control system. Simulations are performed for control system analysis and performance validation of the faulty robotic fish. The experimental results demonstrate that the proposed fault-tolerant control method is able to effectively regulate the faulty robotic fish, allowing it to complete the desired motion in the presence of damage and thereby improving both the stability and the lifetime of the real robotic system.

From Offline Towards Real-Time Verification for Robot Systems

Robot systems have been widely used in industry and also play an important role in human social life. Safety critical applications usually demand rigorously formal verification to ensure correctness. But for the increasing complexity of dynamic environments and applications, it is not easy to build a comprehensive model for the traditional offline verification. In this paper, we propose RobotRV , the first data-centered real-time verification approach for the robot system. Within this approach, a domain-specific language named RoboticSpec is designed to specify the complex application scenario of the robot system, the data packets transmitted in the robot system, and the safety critical temporal properties. Then, we develop an engine to automatically translate the RoboticSpec model into a real-time verifier. The generated verifier serves as an independent plug-in component for the runtime verification of concerned temporal properties. We applied the proposed approach to a real robot system. As presented in experiment results, our method detected potential failures, and improved the safety of robot system.

Motion Planning by Demonstration With Human-Likeness Evaluation for Dual-Arm Robots

This paper presents a planning procedure that allows an anthropomorphic dual-arm robotic system to perform a manipulation task in a natural human-like way by using demonstrated human movements. The key idea of the proposal is to convert the demonstrated trajectories into attractive potential fields defined over the configuration space and then use an RRT*-based planning algorithm that minimizes a path-cost function designed to bias the tree growth toward the human-demonstrated configurations. This paper presents a description of the proposed approach as well as results from a conceptual and a real application example, the latter using a real anthropomorphic dual-arm robotic system. A path-quality measure, based on first-order synergies (correlations between joint velocities) obtained from real human movements, is also proposed and used for evaluation and comparison purposes. The obtained results show that the paths obtained with the proposed procedure are more human-like.

Adaptive Proportional Derivative Controller of Cooperative Manipulators

Abstract This study deals with the design of an automatic controller to solve the problem of trajectory tracking for collaborative robot manipulators. The control algorithm corresponds to an adaptive proportional derivative controller (APDC) aimed to reduce the needed energy to execute the tracking of some predefined reference trajectories. The controller was designed with the aim of keeping a constant distance between the final effectors of both manipulators. The derivative estimation used in the APDC was obtained by a set of robust exact differentiators based on the application of the super-twisting algorithm (STA). The controller was tested on two real cooperative multi-articulated robotic manipulators system. The experimental setup consisted of two robot manipulators designed with four degrees of freedom each one constructed by the 3D printing technology. The experimental results included comparisons with respect to the classical non-adaptive proportional-derivative controllers. The superior performance of the adaptive controller proposed in this study was confirmed by the estimation of the least mean square analysis of the tracking error.

Dynamic analysis of two-link flexible manipulator considering the link length ratio and the payload

Dynamic modeling and analysis of flexible manipulators play an essential role in optimizing mechanical design parameters and control law of real robot systems. In this paper, a nonlinear dynamic model of a manipulator is formulated based on the Finite Element Method. To analyze the dynamic behavior effectively, a numerical simulation scheme is proposed by taking full advantages of MATLAB and SIMULINK toolboxes. In this manner, the effect of varying payload and link length ratio of the manipulator to its elastic displacement is dynamically taken into account. The simulation results show that the payload and length link ratio have significant influences on the elastic displacements of the system. In particular, a proper spectrum of the link length ratio, in which the flexural displacement of the end point of the manipulator is smallest, is demonstrated. To this end, the proposed methodology could be used further to select optimal geometric parameters for the links of new robot designs.

A novel pipeline for bi-manual handover task

This paper addresses the problem of bi-manual object handover with a humanoid robot, i.e. the task of passing objects from one hand to the other. Bi-manual coordination is fundamental for improving manipulation capabilities of humanoid robots. We propose a novel and effective pipeline that tackles the problem by using visual and tactile feedback to localize the object and maintain grasp stability during the task. Given the object in one of the robot hand (first hand), the object in-hand pose is estimated by a localization algorithm, which makes use of vision and tactile information. Then, the estimated pose is used to automatically choose a suitable pose for the second hand among a set of candidates, a-priori annotated on the object model. The selected pose is finally used to accomplish the handover task. The performance of our approach is assessed on a real robotic system, the iCub humanoid robot, on a set of objects from the YCB dataset. Experiments demonstrate that the proposed method allows performing proper and reliable handovers with different every-day objects.

Memory Unscented Particle Filter for 6-DOF Tactile Localization

This paper addresses 6-DOF (degree-of-freedom) tactile localization, i.e. the pose estimation of tridimensional objects given tactile measurements. This estimation problem is fundamental for the operation of autonomous robots that are often required to manipulate and grasp objects whose pose is a-priori unknown. The nature of tactile measurements, the strict time requirements for real-time operation and the multimodality of the involved probability distributions pose remarkable challenges and call for advanced nonlinear filtering techniques. Following a Bayesian approach, this paper proposes a novel and effective algorithm, named Memory Unscented Particle Filter (MUPF), which solves the 6-DOF localization problem recursively in real-time by only exploiting contact point measurements. MUPF combines a modified particle filter that incorporates a sliding memory of past measurements to better handle multimodal distributions, along with the unscented Kalman filter that moves the particles towards regions of the search space that are more likely with the measurements. The performance of the proposed MUPF algorithm has been assessed both in simulation and on a real robotic system equipped with tactile sensors (i.e., the iCub humanoid robot). The experiments show that the algorithm provides accurate and reliable localization even with a low number of particles and, hence, is compatible with real-time requirements.

Analysis of non-geometric accuracy effects of articulated robots

PurposeArticulated robots are widely used in industrial applications owing to their high repeatability accuracy. In terms of new applications such as robot-based inspection systems, the limitation is a lack of pose accuracy. Mostly, robot calibration approaches are used for the improvement of the pose accuracy. Such approaches however require a profound understanding of the determining effects. This paper aims to provide a non-destructive analysis method for the identification and characterisation of non-geometric accuracy effects in relation to the kinematic structure for the purpose of an accuracy enhancement.Design/methodology/approachThe analysis is realised by a non-destructive method for rotational, uncoupled robot axes with the use of a 3D lasertracker. For each robot axis, the lasertracker position data for multiple reflectors are merged with the joint angles given by the robot controller. Based on this, the joint characteristics are determined. Furthermore, the influence of the kinematic structure is investigated.FindingsThis paper analyses the influence of the kinematic structure and non-geometric effects on the pose accuracy of standard articulated robots. The provided method is shown for two different industrial robots and presented effects incorporate tilting of the robot, torsional joint stiffness, hysteresis, influence of counter balance systems, as well as wear and damage.Practical implicationsBased on these results, an improved robot model for a better match between the mathematical description and the real robot system can be achieved by characterising non-geometric effects. In addition, wear and damages can be identified without a disassembly of the system.Originality/valueThe presented method for the analysis of non-geometric effects can be used in general for rotational, uncoupled robot axes. Furthermore, the investigated accuracy influencing effects can be taken into account to realise high-accuracy applications.

A comparison of fitness functions for identifying an LCD Glass-handling robot system

In this paper, system identification with variable fitness functions (FFs) by using the real-coded genetic algorithm (RGA) is proposed for a liquid crystal display (LCD) glass-handling robot system. The FFs including the state errors and energy balance equation are compared for the system. Firstly, the mechatronic modeling including mechanical and electrical dynamic equations are formulated. Secondly, the FFs including state errors and energy balance equations by using the RGA method are employed to maximize the FFs values, and to identify the unknown system parameters. From numerical simulations, it is found that the identified performance with energy balance equation FF by using the RGA method has the best identified performance among the other FFs. Finally, the RGA method with FFs are experimentally performed to identify the parameters for a real robot system. From the comparisons of the experimental results, it is also found that the energy balance equation FF has the best identified performance among the other FFs. Therefore, the unknown parameters of the real robot system can be correctly identified by the RGA method with the energy balance equation FF. The contribution of this paper is to propose an energetics FF to be implemented in system identification for the mechatronic system. It can be concluded that the more system states are used in the FF, the more correct system parameters are identified for the robot system.

First-Order Synergies for Motion Planning of Anthropomorphic Dual-Arm Robots

This paper addresses the problem of designing a planning algorithm for anthropomorphic dual-arm robotic systems to find paths that mimics the movements of real human beings by using first-order synergies (correlations between joint velocities). The key idea of the proposal is to convert captured human movements into a vector field of velocities, defined in the configuration space of the robot, and use it to guide the search of a solution path. The motion planning is solved using the proposed algorithm, called FOS-BKPIECE, that is a bidirectional version of the KPIECE planner working with an improved version of the extension procedure of the VF-RRT planner. The obtained robot movements follow the directions of the defined vector field and hence allow the robot to solve the task in a human-like fashion. The paper presents a description of the proposed approach as well as results from conceptual and application examples, the latter using a real anthropomorphic dual-arm robotic system. A thorough comparison with other previous planning algorithms shows that the proposed approach obtains better results.

Kernel dynamic policy programming: Applicable reinforcement learning to robot systems with high dimensional states

We propose a new value function approach for model-free reinforcement learning in Markov decision processes involving high dimensional states that addresses the issues of brittleness and intractable computational complexity, therefore rendering the value function approach based reinforcement learning algorithms applicable to high dimensional systems. Our new algorithm, Kernel Dynamic Policy Programming (KDPP) smoothly updates the value function in accordance to the Kullback–Leibler divergence between current and updated policies. Stabilizing the learning in this manner enables the application of the kernel trick to value function approximation, which greatly reduces computational requirements for learning in high dimensional state spaces. The performance of KDPP against other kernel trick based value function approaches is first investigated in a simulated n DOF manipulator reaching task, where only KDPP efficiently learned a viable policy at n=40. As an application to a real world high dimensional robot system, KDPP successfully learned the task of unscrewing a bottle cap via a Pneumatic Artificial Muscle (PAM) driven robotic hand with tactile sensors; a system with a state space of 32 dimensions, while given limited samples and with ordinary computing resources.

Collision-free formation and heading consensus of nonholonomic robots as a pose regulation problem

This paper presents a novel, decentralized control algorithm to address the problem of controlling of multiple, network connected, nonholonomic mobile robots to achieve a collision-free formation and heading consensus simultaneously. Our approach transforms the formation and heading consensus problem to a pose regulation problem. The pose is derived by a virtual graph, which is an isomorphic mapping of the graph of the network connected systems. If the system is holonomic, traditional consensus on the virtual graph can stabilize the real system to the desired formation. In the case of nonholonomic systems, such as mobile robots, each robot’s pose then is regulated to the known consensus pose by a novel smooth and continuous control law. The control law also induces the real robot systems to achieve not only the desired formation but also heading consensus. We prove the proposed control schemes are globally asymptotically stable. Theoretical analysis, several simulations, and experiments have been conducted to verify the effectiveness of the proposed approach.

Hybrid Task Planning Grounded in Belief: Constructing Physical Copies of Simple Structures

Symbolic planning methods have proved to be challenging in robotics due to partial observability and noise as well as unavoidable exceptions to rules that symbol semantics depend on. Often the symbols that a robot considers to support for planning are brittle, making them unsuited for even relatively short term use. Maturing probabilistic methods in robotics, however, are providing a sound basis for symbol grounding that supports using probabilistic distributions over symbolic entities as the basis for planning. In this paper, we describe a belief-space planner that stabilizes the semantics of feedback from the environment by actively interacting with a scene. When distributions over higher-level abstractions stabilize, powerful symbolic planning techniques can provide reliable guidance for problem solving. We present such an approach in a hybrid planning scheme that actively controls uncertainty and yields robust state estimation with bounds on uncertainty that can make effective use of powerful symbolic planning techniques. We illustrate the idea in a hybrid planner for autonomous construction tasks with a real robot system.