Costas S. Tzafestas

Adaptive impedance control in haptic teleoperation to improve transparency under time-delay

This paper proposes the application of an adaptive impedance control scheme to alleviate some of the problems associated with the presence of time delays in a haptic teleoperation system. Continuous on-line estimation of the remote environments impedance is performed, and is then used as a local model for haptic display control. Lyapunov stability of the proposed impedance adaptation law is demonstrated. A series of experiments is performed to evaluate the performance of this teleoperation control scheme. Two performance measures are defined to assess transparency and stability of the teleoperator. Simulation results show the superior performance of the proposed adaptive scheme, with respect to direct teleoperation, particularly in terms of increasing the stability margin and of significantly ameliorating transparency in the presence of large time delays. Experimental results, using a phantom omni as the haptic master device, support this conclusion.

Temporal Occupancy Grid for mobile robot dynamic environment mapping

Mapping dynamic environments is an open issue in the field of robotics. In this paper, we extend the well known Occupancy Grid structure to address the problem of generating valid maps for dynamic indoor environments. We propose a spatiotemporal access method to store all sensor values (instead of preserving only one value for each cell as in the common occupancy grid case). By searching for similar time series, we can detect moving objects that appear only in a limited number of possible configurations (e.g. doors or chairs). Simulated experiments demonstrate the potentialities of the proposed system.

Maximum Likelihood SLAM in Dynamic Environments

Simultaneous Localization and Mapping in dynamic environments is an open issue in the field of robotics. Traditionally, the related approaches assume that the environment remains static during the robots exploration phase. In this work, we overcome this assumption and propose an algorithm that exploits the dynamic nature of the environment during robot exploration so as to improve the localization process. We use a Histogram Grid to store all the past occupancy values of every cell and thus to select the most probable pose of the robot based on the occupancy evolution. Experiments on a simulated robot indicate the effectiveness of the proposed approach.

Visuo-Haptic Interface for Teleoperation of Mobile Robot Exploration Tasks

With the spread of low-cost haptic devices, haptic interfaces appear in many areas in the field of robotics. Recently, haptic devices have been used in the field of mobile robot teleoperation, where mobile robots operate in unknown and dangerous environments performing particular tasks. Haptic feedback is shown to improve operator perception of the environment without, however, improving exploration time. In this paper, we present a haptic interface that is used to teleoperate a mobile robot in exploring polygonal environments. The proposed visuo-haptic interface is found to improve navigation time and operator perception of the remote environment. The human-operator can simultaneously select two different commands, the first one being set as active motion command, while the second one is set as a guarded motion type of navigation command. The user can feel a haptic equivalent for both types of teleguidance motion commands, and can also observe in real-time the sequential creation of the remote environment map. Comparative evaluation experiments show that the proposed system makes the task of remote navigation of unknown environments easier

Shared control for motion compensation in robotic beating heart surgery

This paper presents a shared control approach for motion compensation in robotic beating heart surgery. Motion compensation consists of three main tasks; motion synchronization, image stabilization and shared control. The paper discusses a unifying framework under which the three tasks combine seamlessly. In this work, the planar 1-manifold case is considered, where a strip-wise affine map is performed to achieve image stabilization onto a canonical space, where shared control emerges naturally. A prototype teleoperation system is also described, implementing the algorithms. Experiments were performed with medically trained users, and the positive effect of motion compensation is analyzed.

Revealing Cluster Formation over Huge Volatile Robotic Data

In this paper, we propose a driven by the robotics field method for revealing global clusters over a fast, huge and volatile stream of robotic data. The stream comes from a mobile robot which autonomously navigates in an unknown environment perceiving it through its sensors. The sensor data arrives fast, is huge and evolves quickly over time as the robot explores the environment and observes new objects or new parts of already observed objects. To deal with the nature of data, we propose a grid -- based algorithm that updates the grid structure and adjusts the so far built clusters online. Our method is capable of detecting object formations over time based on the partial observations of the robot at each time point. Experiments on real data verify the usefulness and efficiency of our method.

Multi-agent hierarchical architecture modeling kinematic chains employing continuous RL learning with fuzzified state space

In the context of multi-agent systems, we are proposing a hierarchical robot control architecture that comprises artificial intelligence (AI) techniques and traditional control methodologies, based on the realization of a learning team of agents in a continuous problem setting. In a multi-agent system, action selection is important for cooperation and coordination among the agents. By employing reinforcement learning (RL) methods in a fuzzified state-space, we accomplish to design a control architecture and a corresponding methodology, engaged in a continuous space, which enables the agents to learn, over a period of time, to perform sequences of continuous actions in a cooperative manner, in order to reach their goal without any prior generated task model. By organizing the agents in a nested architecture, as proposed in this work, a type of problem-specific recursive knowledge acquisition is attempted. Furthermore, the agents try to exploit the knowledge gathered in order to be in position to execute tasks that indicate certain degree of similarity. The agents correspond in fact to independent degrees of freedom of the system, and achieve to gain experience over the task that they collaboratively perform, by exploring and exploiting their state-to-action mapping space. A numerical experiment is presented in this paper, performed on a simulated planar 4 degrees of freedom (DOF) manipulator, in order to evaluate both the proposed hierarchical multi-agent architecture as well as the proposed methodological framework. It is anticipated that such an approach can be highly scalable for the control of robotic systems that are kinematically more complex, comprising multiple DOFs and potentially redundancies in open or closed kinematic chains, particularly dexterous manipulators.

Enhancing surgical accuracy using virtual fixtures and motion compensation in robotic beating heart surgery

This paper proposes a novel technique for applying virtual fixtures in a changing environment. The main targeted application is robotic beating heart surgery, which enables the surgeon to operate directly on a beating heart. Using a motion compensation framework, the motion of the heart surface is stabilized in a virtual space, which is presented to the surgeon to operate in. Consequently, the fixture is implemented in this static space, bypassing problems of dynamic fixtures such as position update, placement and force transients. Randomized experiments were performed using a trained surgeon comparing our approach to simple motion compensation and no compensation at all. The positive effect of the fixture in surgical accuracy for a tracking task is also discussed.

Web-based remote and virtual programming console of the V + robotic system

The objective of practical training is a major issue in students education, in many engineering disciplines. The access to specialized technological equipment for education is often limited by specific time restriction, or not provided at all. Therefore, the benefits by providing a Web-based platform for remote experimentation via LAN or Internet are evident. This paper describes the development of an e-laboratory platform intending to be used as a distance training system in the field of robotic task planning (e.g. programming of a robotic pick and place task). In prior work, this platform was evaluated by training students remotely to implement robotic tasks, using the robotpsilas Teach Pendant. This paper is focusing on the design of a training platform, aiming to make students familiar with the V+ robotic operating system. The proposed platform intends to remotely provide the students with the ability of programming robotic manipulation tasks using directly V+ scripts. An evaluation protocol, presented in [11], [12], is considered to be employed in the near future, in order to assess the performance of the proposed e-laboratory platform, with respect to the level of students learning and assimilating of the robotpsilas programming language (V+).

An Introduction to the Problem of Mapping in Dynamic Environments

Robotic mapping comprises one of the most important problems in the field of robotics. During the past two decades, a large number of algorithms have been proposed in order to solve the problem of constructing valid models of the robot environment. As a result, highly accurate maps of large-scale indoor and outdoor environments have been constructed thus far. There are still, though, much to be done in order to achieve fully autonomous mobile robots capable of mapping any kind of environment (structured or unstructured, static or dynamic). In this chapter, we discuss the problem of mapping dynamic environments, an issue that remains open and is extremely active nowadays. Dynamic environments are real world environments where moving objects (e.g. humans, robots, chairs and doors) change their positions over time. Widespread mapping algorithms developed in the past are based on the assumption that the environment remains static during the robot exploration phase. Thus, these algorithms provide imprecise results when applied in non-stationary environments. The need to map these environments has led in the development of new algorithms that are designed to exploit the dynamics of the environments towards efficient mapping. These algorithms have given so far promising results. Through this chapter, we examine the problem of dynamic environments through the mapping point of view. Two issues that are strongly connected to mapping are (a) localization, the process of estimating the position and the orientation of the robot and (b) navigation, the generation of valid paths for the robot. They are both of great importance and remain open fields of research especially when applied on dynamic environments. However, in this chapter we concentrate on the mapping problem and refer to the other two problems only when necessary. Our effort is towards providing the ideas behind the algorithms discussed in this chapter and avoid the mathematical details and formulas. We urge the interested readers to consult the referenced papers in order to gain a better insight on the techniques discussed in this chapter. The outline of the chapter is as follows: In Section 2, we present the mapping problem for static environments, so as to make the reader familiar with the concepts of the mapping problem. Next, in Section 3 we move to the problem of mapping in dynamic environments. More specifically, we discuss the main difficulties of the problem and present a number of