Robotic Framework Research Articles

Design and Optimization of a Control Framework for Robot Assisted Additive Manufacturing Based on the Stewart Platform

Design and Optimization of a Control Framework for Robot Assisted Additive Manufacturing Based on the Stewart Platform

AN INTEGRATED AVOIDING AND APPROACHING HUMAN SYSTEMS FOR MOBILE SERVICE ROBOTS IN DYNAMIC SOCIAL ENVIRONMENTS

In this study, we present a socially aware navigation framework for mobile service robots by integrating the systems proposed in our previous studies into a completed mobile robot navigation system, including the human detection and tracking system that is used to detect and track humans; the encoder and laser-based localization and mapping system that is used to determine the position of the robot in the environment; the approaching pose estimation system that is used to estimate the approaching pose of the robot to the human; and the social timed elastic band-based motion planning system that is used to socially navigate the mobile robots to approach the human. In addition, in the paper we describe in detail the motor control system and the design of our mobile robot platform, which is then utilized to conduct experiments in the real-world environments. We verify the feasibility and usefulness of the proposed socially aware robot navigation framework through a series of experiments in a corridor-like environment. The experimental results show that our proposed framework is able to drive the mobile robots to both avoid and approach the humans, providing the safety and comfort for the humans and socially acceptable behaviors for the mobile service robots in the dynamic social environments.

Online Gain Adaptation of Whole-Body Control for Legged Robots with Unknown Disturbances.

This paper proposes an online gain adaptation approach to enhance the robustness of whole-body control (WBC) framework for legged robots under unknown external force disturbances. Without properly accounting for external forces, the closed-loop control system incorporating WBC may become unstable, and therefore the desired task goals may not be achievable. To study the effects of external disturbances, we analyze the behavior of our current WBC framework via the use of both full-body and centroidal dynamics. In turn, we propose a way to adapt feedback gains for stabilizing the controlled system automatically. Based on model approximations and stability theory, we propose three conditions to ensure that the adjusted gains are suitable for stabilizing a robot under WBC. The proposed approach has four contributions. We make it possible to estimate the unknown disturbances without force/torque sensors. We then compute adaptive gains based on theoretic stability analysis incorporating the unknown forces at the joint actuation level. We demonstrate that the proposed method reduces task tracking errors under the effect of external forces on the robot. In addition, the proposed method is easy-to-use without further modifications of the controllers and task specifications. The resulting gain adaptation process is able to run in real-time. Finally, we verify the effectiveness of our method both in simulations and experiments using the bipedal robot Draco2 and the humanoid robot Valkyrie.

Control of a Robot Expressive Movements Using Non-Verbal Features

This work presents a new interaction method that takes the salient features from human non-verbal communication and seeks to incorporate them into a robotic framework. The proposed method leverages data-driven techniques to extract the main essence from the human non-verbal characteristics through Autoencoder architectures. The final model is analyzed through a ten-movement performance study alongside three different control methodologies. The results show that the silent features captured by the network imposed various degrees of difference in the robot movement depending on the human movement, proving a correct recognition of the qualitative factors.

Graceful Transitions through Maximal Persistence of Behavior

Problems of locomotion typically encounter switching between gaits. In many applications, it is desirable to make such transitions between modes or gaits inconspicuous and graceful. This is achieved by keeping the typical behavior of the system as persistent as possible. It is shown that this problem can be defined in a more abstract sense, and therefore generalized to a new class of problems, called “maximum persistence of behavior.”In this presentation, I will give a precise definition of this concept, for signals and for systems, and illustrate some applications from thermodynamics to signal study and robotics. I will also introduce the dual problem of filtering in the context of “maximal persistence of behavior.” The results stem from joint work with Deryck Yeung (Trinity University, San Antonio, TX), Basit Memon (Habib University, Karachi, PK), and Vishal Murali (now at NVIDIA) while at Georgia Tech.The paradigm starts by defining typical behaviors in the framework initiated by J.C. Willems. Gluing two typical signals or typical system behaviors requires connections (raccordations) that belong to a larger class of behaviors, but are locally close to the typical behaviors. Solutions with persistent behavior are then sought via optimization for a kernel or an image problem. This solution encapsulates the controllability problem of Willems, morphing theory in image analysis, object shaping, quasi-stationary transitions from thermodynamics, and orbit transfer problems, and can be characterized in a geometric way. In the robotics framework, obvious applications are in gait transitions for biomimetic robots as when switching from walking (e.g., while inspecting) to running (evasion), or a gait transition necessitated by a change in terrain (solid, muddy, or granular). In particular, I will discuss a method of smoothly connecting periodic orbits of systems. We first deal with the case when the periodic signals to be connected are of the same period and then move on to discuss the case of different periods, requiring in addition a frequency warping.

A Personalized Social Knowledge Base Framework for Self-Learning Service Robots

A Personalized Social Knowledge Base Framework for Self-Learning Service Robots

Edge Deployment Framework of GuardBot for Optimized Face Mask Recognition With Real-Time Inference Using Deep Learning

Deep learning based models on the edge devices have received considerable attention as a promising means to handle a variety of AI applications. However, deploying the deep learning models in the production environment with efficient inference on the edge devices is still a challenging task due to computation and memory constraints. This paper proposes a framework for the service robot named GuardBot powered by Jetson Xavier NX and presents a real-world case study of deploying the optimized face mask recognition application with real-time inference on the edge device. It assists the robot to detect whether people are wearing a mask to guard against COVID-19 and gives a polite voice reminder to wear the mask. Our framework contains dual-stage architecture based on convolutional neural networks with three main modules that employ (1) MTCNN for face detection, (2) our proposed CNN model and seven transfer learning based custom models which are Inception-v3, VGG16, denseNet121, resNet50, NASNetMobile, XceptionNet, MobileNet-v2 for face mask classification, (3) TensorRT for optimization of all the models to speedup inference on the Jetson Xavier NX. Our study carries out several analysis based on the models’ performance in terms of their frames per second, execution time and images per second. It also evaluates the accuracy, precision, recall & F1-score and makes the comparison of all models before and after optimization with a main focus on high throughput and low latency. Finally, the framework is deployed on a mobile robot to perform experiments in both outdoor and multi-floor indoor environments with patrolling and non-patrolling modes. Compared to other state-of-the-art models, our proposed CNN model for face mask recognition based on the classification obtains 94.5%, 95.9% and 94.28% accuracy on training, validation and testing datasets respectively which is better than MobileNet-v2, Xception and InceptionNet-v3 while it achieves highest throughput and lowest latency than all other models after optimization at different precision levels.

A Novel Disturbance-Rejection Control Framework for Cable-Driven Continuum Robots With Improved State Parameterizations

A soft, or continuum, robot has great potential in many practical fields with confined space, such as manufacturing system, electrical industry and other, due to its flexibility and benign bending attribute. However, its innate nonlinear dynamics and infinite degrees of freedom pose challenges both in modeling and control. How to build a proper model, which can be used to analyze the characteristics of soft robots, and how to design a robustly stabilized controller, are still open issues. In this work, we build a model for cable-driven continuum robots using a piecewise constant-curvature method. An improved state parameterization is proposed to avoid singularities, which produce pathological behaviors mainly on straight configurations of continuum robots. Then, a novel disturbance-rejection control framework is proposed with a tailored sliding mode controller to achieve stabilized control. Aiming at better robustness, we lump all system uncertainties, including unmodeled dynamics, unknown external disturbances, and unknown parameter variations, together as an extended state of the system. A linear extended state observer is then proposed to estimate the total uncertainties online, enabling compensation for their negative effects. Stability analysis and numerical simulations are carried out to show the feasibility and effectiveness of the proposed approaches.

Autism Spectrum Disorder Therapy: Analysis of Artificial Intelligence integrated Robotic Approach

Autism Spectrum Disorder is a developmental disorder that may manifest in a myriad of ways such as difficulties in social interaction and a tendency to engage in repetitive patterns of behaviour. Over the years, several kinds of treatment protocols have been proposed and implemented. One such area that is attracting the attention of researchers in the field is a robot-based approach in the treatment of children diagnosed with the disorder. Here we propose a viable method via the integration of apex technological methods like Artificial Intelligence, Machine Learning and Medical Robotics, coupling it with problem specific algorithms in OpenCV along with principles of Applied Behavioural Analysis to help possibly alleviate a key symptom displayed by children in terms of level of social interaction - that of eye-contact. This would be achieved via an AI-integrated Robotic Framework. The project also considers the possibility of inclusion of the growing research field of Quantum Computing to realize the process and investigates its viability as a potential source of innovation in the future.

Mixed Reality Based Multi-Agent Robotics Framework for Artificial Swarm Intelligence Experiments

Mixed Reality Based Multi-Agent Robotics Framework for Artificial Swarm Intelligence Experiments

RFID Gazebo-Based Simulator With RSSI and Phase Signals for UHF Tags Localization and Tracking

Radio Frequency Identification (RFID) technology is becoming very popular in the new era of Industry 4.0, especially for warehouse management, retails, and logistics. RFID systems can be used for objects identification, localization, and tracking, facilitating everyday operators’ efforts. However, the deployment of RFID tags and reader antennas in real-world application scenarios is crucial and takes time. Indeed, deciding where to place tags and/or readers’ requires examining many conditions. If some weaknesses appear in the design, the arrangement must be reconsidered. The proposed work presents a novel open-source RFID simulator that allows modeling environments and testing the deployment of RFID tags and antennas apriori. In such a way, validating the performance of the localization or tracking algorithms in simulation, possible weaknesses that could arise may be fixed before facilities are applied on the field. Any number of tags and antennas can be placed in any position in the created scenario, and the simulator provides the phase and the RSSI signals for each tag. Every reader antenna is parametrized so that different antennas of different vendors can be reproduced. The simulator is implemented as a plugin of Gazebo, a widely used robotic framework integrated with the Robot Operating System (ROS), to reach a broad audience. In order to validate the simulator, a warehouse scenario is modeled, and a tag localization algorithm that uses the phase unwrapping technique and hyperbolae intersection method employing a reader antenna mounted on a mobile robot is used to estimate the position of the tags deployed in the scenario. The outcomes of the experiments showed realistic results.

A Decentralized Cluster Formation Containment Framework for Multirobot Systems

Cooperative control of multirobot systems (MRSs) has earned significant research interest over the past two decades due to its potential applications in multidisciplinary engineering problems. In contrast to a single specialized robot, the MRS can be designed to offer flexibility, reconfigurability, robustness to faults, and cost-effectiveness in solving complex and challenging tasks. In this article, we aim to develop a unified cluster formation containment coordination framework for networked robots that can be decomposed into two layers containing the leaders and the followers. According to the proposed methodology, the leader robots are first distributed into a set of distinct and nonoverlapping clusters depending on the positions and priorities of the targets exploiting a game-theoretic rule. Then, they are steered to attain the desired formations around the corresponding targets. Subsequently, the follower robots are made to converge into the convex hull spanned by the leaders of the individual clusters. A prototype search and rescue operation is considered to highlight the usefulness of the proposed coordination framework. Furthermore, real-time hardware experiments were conducted on miniature mobile robots to validate the feasibility of the theoretical results.

Intelligent framework for social robots based on artificial intelligence-driven mobile edge computing

In recent years, artificial intelligence technology has gradually expanded its application scenarios in the field of education. Nevertheless, at present, most AI application scenarios are still difficult to really penetrate into the core links. Educational robot based on artificial intelligence technology can provide humanized interaction and personalized intelligent teaching in different links, thus it has attracted more attention and development than ever. For mobile robots, vision based target recognition technology is a very basic but challenging topic. The result of robot target recognition is directly related to the positioning, navigation and other work of the robot. Therefore, this paper studies the intelligent education information framework of social robot based on artificial intelligence-driven mobile edge computing. The pattern recognition framework is applied to the system and the proposed model is tested on the public database. Compared to the other state-of-the-art methodologies, the proposed idea performs better.

Smart surgical control under RCM constraint using bio-inspired network

In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient’s safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints.

Multi-Contact Locomotion Planning With Bilateral Contact Forces Considering Kinematics and Statics During Contact Transition

In this letter, we propose a multi-contact locomotion planning framework for a humanoid robot to traverse complex environments utilizing bilateral contact forces with reasonable computational time. We hypothesize that a bilateral contact can be approximated by a pair of surface contacts, and expand the static CoM feasible region by this assumption, which we define as bSCFR. This assumption enables us to project centroidal statics with bilateral contact forces to a constraint on whole-body kinematics. Then, we formulate the whole-body inverse kinematics problem for all the discretized frames in the target contact transition as one optimization problem with bSCFR and CoM regularization, which can find feasible whole-body configurations while considering both kinematics and statics. After solving whole-body inverse kinematics, the contact forces are computed based on the centroidal statics, where appropriate bilateral contact forces are automatically generated according to the resulting CoM positions. We experimentally confirm that our proposed framework enabled HRP-5P to traverse steep stairs utilizing bilateral contact forces in the real world, and conclude that it expands the multi-contact locomotion capability of a humanoid robot.

CPG-Based Hierarchical Locomotion Control for Modular Quadrupedal Robots Using Deep Reinforcement Learning

Modular robots have the potential for an unmatched ability to perform versatile and robust locomotion. However, designing effective and adaptive locomotion controllers for modular robots is challenging, resulting in a number of model-based methods that typically require various forms of prior knowledge. Deep reinforcement learning (DRL) provides a promising model-free approach for locomotion control by trial-and-error. However, current DRL methods often require extensive interaction data, hindering many possible applications. In this letter, a novel two-level hierarchical locomotion framework for modular quadrupedal robots is proposed. The approach combines a low-level central pattern generator (CPG)-based controller with a high-level neural network to learn a variety of locomotion tasks using DRL. The low-level CPG controller is pre-optimized to generate stable rhythmic walking gaits, while the high-level network is trained to modulate the CPG parameters for achieving task goals based on high-dimensional inputs, including the robot states and user commands. The proposed approach is employed on a simulated modular quadruped. With a limited amount of prior knowledge, the proposed method is demonstrated to be capable of learning a variety of locomotion skills such as velocity tracking, path following, and navigating to a target. Simulation results show that the proposed method can achieve higher sample efficiency than the model-free DRL method and are substantially more robust than the baseline methods to external disturbances and irregular terrain.

Navigate-and-Seek: A Robotics Framework for People Localization in Agricultural Environments

The agricultural domain offers a working environment where many human laborers are nowadays employed to maintain or harvest crops, with huge potential for productivity gains through the introduction of robotic automation. Detecting and localizing humans reliably and accurately in such an environment, however, is a prerequisite to many services offered by fleets of mobile robots collaborating with human workers. Consequently, in this paper, we expand on the concept of a topological particle filter (TPF) to accurately and individually localize and track workers in a farm environment, integrating information from heterogeneous sensors and combining local active sensing (exploiting a robot's onboard sensing employing a Next-Best-Sense planning approach) and global localization (using affordable IoT GNSS devices). We validate the proposed approach in topologies created for the deployment of robotics fleets to support fruit pickers in a real farm environment. By combining multi-sensor observations on the topological level complemented by active perception through the NBS approach, we show that we can improve the accuracy of picker localization in comparison to prior work.

Service planning oriented efficient object search: A knowledge-based framework for home service robot

In the unstructured family environment, robots are expected to provide various services to improve the quality of human life, based on the performance of specific action sequences generated by service planning. This paper focuses on one of the greatest challenges in service planning that is aimed at accomplishing the service tasks by generating appropriate object sequences to guide the robot on searching the corresponding target objects efficiently and reasonably. A well-structured knowledge-based framework of object search is proposed in our approach as well as taking into account the multi-domain knowledge of applying object, scene, and service in design. In order to improve the searching efficiency and reasonability, an ontology-based hierarchical and interrelated knowledge structure is formed to support the implementation of complicated service planning with either single or multiple tasks. The proposed framework is tested by comprehensive experiments, and the performance is evaluated with other mainstream methods in both simulation and real-world environments. The experimental results demonstrate the feasibility and effectiveness of applying this knowledge-based framework to efficient object searching aspect in service planning.

Agent in a Box: A Framework for Autonomous Mobile Robots with Beliefs, Desires, and Intentions

This paper provides the Agent in a Box for developing autonomous mobile robots using Belief-Desire-Intention (BDI) agents. This framework provides the means of connecting the agent reasoning system to the environment, using the Robot Operating System (ROS), in a way that is flexible to a variety of application domains which use different sensors and actuators. It also provides the needed customisation to the agent’s reasoner for ensuring that the agent’s behaviours are properly prioritised. Behaviours which are common to all mobile robots, such as for navigation and resource management, are provided. This allows developers for specific application domains to focus on domain-specific code. Agents implemented using this approach are rational, mission capable, safety conscious, fuel autonomous, and understandable. This method was used for demonstrating the capability of BDI agents to control robots for a variety of application domains. These included simple grid environments, a simulated autonomous car, and a prototype mail delivery robot. From these case studies, the approach was demonstrated as capable of controlling the robots in the application domains. It also reduced the development burden needed for applying the approach to a specific robot.

On the Design of Social Robots Using Sheaf Theory and Smart Contracts.

The incorporation of robots in the social fabric of our society has taken giant leaps, enabled by advances in artificial intelligence and big data. As these robots become increasingly adept at parsing through enormous datasets and making decisions where humans fall short, a significant challenge lies in the analysis of robot behavior. Capturing interactions between robots, humans and IoT devices in traditional structures such as graphs poses challenges in the storage and analysis of large data sets in dense graphs generated by frequent activities. This paper proposes a framework that uses the blockchain for the storage of robotic interactions, and the use of sheaf theory for analysis of these interactions. Applications of our framework for social robots and swarm robots incorporating imperfect information and irrationality on the blockchain sheaf are proposed. This work shows the application of such a framework for various blockchain applications on the spectrum of human-robot interaction, and identifies key challenges that arise as a result of using the blockchain for robotic applications.