Applying model mediation method to a mobile robot bilateral teleoperation system experiencing time delays in communication

Teleoperation systems consist of two subsystems namely, the master and the slave. Master is used by the human operator to send commands to the slave to achieve a task. In bilateral teleoperation, the interaction forces acquired from the slave sub-system is sent to the master to increase the level of tele-presence. In this kind of a setting, data has to be transferred through a communication line in which package losses and time delays occur. Such deficiencies in the communication line results in stability problems in the system. In this paper, HIPHAD desktop haptic device as the master sub-system and an omni-directional mobile robot as the slave subsystem is used to develop an unlimited workspace teleoperation system. The system’s stability and tracking performance under a constant time delay is measured for direct teleoperation and when model mediation method is applied to ensure stability. The results of the tests are given and the conclusions are derived.The Scientific and Technological Research Council of Turke

Reducing Safety Interventions in Provably Safe Reinforcement Learning

Deep Reinforcement Learning (RL) has shown promise in addressing complex robotic challenges. In real-world applications, RL is often accompanied by failsafe controllers as a last resort to avoid catastrophic events. While necessary for safety, these interventions can result in undesirable behaviors, such as abrupt braking or aggressive steering. This paper proposes two safety intervention reduction methods: proactive replacement and proactive projection, which change the action of the agent if it leads to a potential failsafe intervention. These approaches are compared to state-of-the-art constrained RL on the OpenAI safety gym benchmark and a human-robot collaboration task. Our study demonstrates that the combination of our method with provably safe RL leads to high-performing policies with zero safety violations and a low number of failsafe interventions. Our versatile method can be applied to a wide range of real-world robotic tasks, while effectively improving safety without sacrificing task performance.Comment: 8 pages, 6 figure

ROS Based Multi-sensor Navigation of Intelligent Wheelchair

Our society is moving towards an ageing society and the number of population with physical impairments and disabilities will increase dramatically. It is necessary to provide mobility support to these people so that they can live independently at home and integrated into the society. This paper presents a ROS (Robot Operating System) based multi-sensor navigation for an intelligent wheelchair that can help the elderly and disabled people. ROS provides an easy to use framework for rapid system development at a reduced cost. Some experimental results are given in the paper to demonstrate the feasibility and performance of the developed system

System identification and nonlinear model predictive control with collision avoidance applied in Hexacopters UAVs

Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller

Kinect Enabled Monte Carlo Localisation for a Robotic Wheelchair

Proximity sensors and 2D vision methods have shown to work robustly in particle filter-based Monte Carlo Localisation (MCL). It would be interesting however to examine whether modern 3D vision sensors would be equally efficient for localising a robotic wheelchair with MCL. In this work, we introduce a visual Region Locator Descriptor, acquired from a 3D map using the Kinect sensor to conduct localisation. The descriptor segments the Kinect’s depth map into a grid of 36 regions, where the depth of each column-cell is being used as a distance range for the measurement model of a particle filter. The experimental work concentrated on a comparison of three different localization cases. (a) an odometry model without MCL, (b) with MCL and sonar sensors only, (c) with MCL and the Kinect sensor only. The comparative study demonstrated the efficiency of a modern 3D depth sensor, such as the Kinect, which can be used reliably for wheelchair localisation

Content Modification Attacks on Networked Robotic Systems

With the advent of communication networks in robotic systems, distributed networked robotic systems can be deployed to perform certain tasks collaboratively. However, this makes the networked robotic systems vulnerable to cyber attacks. Thus, the rigorous study of the impact of cyber attacks and the development of corresponding defense mechanisms are necessary. In this dissertation, the cyber-physical security issue of networked robotic systems is studied under a specific type of cyber attack called content modification attack, which can modify the data content transmitted in the communication networks among the robots. Specifically, algorithms for attack design and detection for content modification attacks are studied. The physics of the robotic system is utilized to design and detect the cyber attacks for networked robotic systems. Content modification attacks are studied for the synchronization problem in networked robotic systems. The considered systems include multi-robot systems, bilateral teleoperation systems and bilateral tele-driving systems. To demonstrate the potential severity of the attack, a constructive methodology for attack design is also developed. Specifically, a destabilizing content modification attack referred to as a malignant content modification attack (MCoMA) is designed based on the system storage function, which can lead to system instability and even physical system damage. To protect the system, a physics-based attack detection scheme with an encoding-decoding structure is proposed for general content modification attacks. As part of the tele-driving system study, a novel passivity-based adaptive bilateral tele-driving control scheme is also proposed in the presence of network delays and dynamics parametric uncertainties. Simulations and experiments have also been conducted to validate the proposed algorithms. This study demonstrates the potential of utilizing the physics of the robotic system to better understand and strengthen the security of the networked robotic systems

Sensors, SLAM and Long-term Autonomy: A Review

Simultaneous Localization and Mapping, commonly known as SLAM, has been an active research area in the field of Robotics over the past three decades. For solving the SLAM problem, every robot is equipped with either a single sensor or a combination of similar/different sensors. This paper attempts to review, discuss, evaluate and compare these sensors. Keeping an eye on future, this paper also assesses the characteristics of these sensors against factors critical to the long-term autonomy challenge

Passivity-Based adaptive bilateral teleoperation control for uncertain manipulators without jerk measurements

In this work, we consider the bilateral teleoperation problem of cooperative robotic systems in a Single-Master Multi-Slave (SM/MS) configuration, which is able to perform load transportation tasks in the presence of parametric uncertainty in the robot kinematic and dynamic models. The teleoperation architecture is based on the two-layer approach placed in a hierarchical structure, whose top and bottom layers are responsible for ensuring the transparency and stability properties respectively. The load transportation problem is tackled by using the formation control approach wherein the desired translational velocity and interaction force are provided to the master robot by the user, while the object is manipulated with a bounded constant force by the slave robots. Firstly, we develop an adaptive kinematic-based control scheme based on a composite adaptation law to solve the cooperative control problem for robots with uncertain kinematics. Secondly, the dynamic adaptive control for cooperative robots is implemented by means of a cascade control strategy, which does not require the measurement of the time derivative of force (which requires jerk measurements). The combination of the Lyapunov stability theory and the passivity formalism are used to establish the stability and convergence property of the closed-loop control system. Simulations and experimental results illustrate the performance and feasibility of the proposed control scheme.No presente trabalho, considera-se o problema de teleoperação bilateral de um sistema robótico cooperativo do tipo single-master e multiple-slaves (SM/MS) capaz de realizar tarefas de transporte de carga na presença de incertezas paramétricas no modelo cinemático e dinâmico dos robôs. A arquitetura de teleoperação está baseada na abordagem de duas camadas em estrutura hierárquica, onde as camadas superior e inferior são responsáveis por assegurar as propriedades de transparência e estabilidade respectivamente. O problema de transporte de carga é formulado usando a abordagem de controle de formação onde a velocidade de translação desejada e a força de interação são fornecidas ao robô mestre pelo operador, enquanto o objeto é manipulado pelos robôs escravos com uma força constante limitada. Primeiramente, desenvolve-se um esquema de controle adaptativo cinemático baseado em uma lei de adaptação composta para solucionar o problema de controle cooperativo de robôs com cinemática incerta. Em seguida, o controle adaptativo dinâmico de robôs cooperativos é implementado por meio de uma estratégia de controle em cascata, que não requer a medição da derivada da força (o qual requer a derivada da aceleração ou jerk). A teoria de estabilidade de Lyapunov e o formalismo de passividade são usados para estabelecer as propriedades de estabilidade e a convergência do sistema de controle em malha-fechada. Resultados de simulações numéricas ilustram o desempenho e viabilidade da estratégia de controle proposta

A recursive Bayesian filter for landmark-based localisation of a wheelchair robot

An odometry model, represented by a set of nodes (waypoints), is considered to be the infrastructure of any probabilistic-based localisation method. Gaussian and nonparametric filters utilise an odometry model to localise robots, while predictions are made by the filters to actively correct the robot's location and coordination. In this work, we present a recursive Bayesian filter for landmark recognition, which is used to verify the pose of a robotic wheelchair at a certain node location. The Bayesian rule in the proposed method does not incorporate a control action to rectify the robot's pose (passive localisation). The filter approximates the robot's pose based on a feature extraction sensor model. Features are extracted from local environmental regions (landmarks), and each landmark is assigned with a distinct posterior probability (signature), at each node location. A node is verified by the robot when the covariance between the posterior and prior probability falls bellow a threshold. We tested the proposed method in an indoor environment where accurate localisation results have been obtained. The experimentation demonstrated the robustness of the filter to work for passive localisation