Multi-Robot Navigation Among Movable Obstacles (MR-NAMO) is a variant of the Multi-Agent Path Finding (MAPF) problem where the environment consists of both immovable and movable obstacles. In this paper, we introduce a special case of MR-NAMO called the multi-agent path finding with removable obstacles (MAPF-RO) problem in which the robots cooperate to remove some obstacles along the busy paths in the environment. We model the removable obstacles as pits and remove them by filling them using sandbags. Sandbags are modeled as movable obstacles but are removable when they are filled into a pit. The obstacles are removed or moved away from the paths while the total energy required for all robots to reach their goals is minimized. The nearby sandbag to fill a pit is identified by using a kd-tree-based heuristic search. The nearby robot to push a sandbag is identified using directional wavefront propagation algorithm. We simulate the scenario in randomized grid environments consisting of static, movable and removable obstacles. We find that our approach conserves energy by removing only the necessary obstacles and cooperatively shortens the path for other agents. This method can be applied to multi-robot cooperative environment modification, enabling robots to alter their surroundings to optimize task execution for their peers while reducing overall energy expenditure.

This paper describes how to establish performance charts for selection of network parameters for effective utilization of a bilateral teleoperated manipulator working under a wireless communication channel. The goal is to construct a set of charts that help researchers and engineers to select appropriate parameters of wireless network setup for a known configuration of environment obstruction. To achieve this goal, a teleoperated setup comprising a master haptic device, a slave manipulator dynamic simulator, and a communication channel emulated using the network simulator version 2 (NS2) simulator is first developed. Next, performance indices are defined to evaluate the quality of position tracking of the slave manipulator end-effector and force tracking of the master haptic. Three indices chosen in this paper are the integral of squared position and force errors, the integral of absolute position and force error, and the amplitude of position and force overshoot. Extensive experiments on the developed setup are then conducted to study effects of time-varying packet loss on the performance of the teleoperated system. The largest mean packet loss, at which the system exhibits satisfactory tracking, is then quantified. This packet loss is used as an indicator to define regions representing the quality of tracking. The effectiveness of the proposed technique is validated by testing a fully instrumented hydraulically actuated system under various real wireless channel scenarios.

This paper presents a proof of concept experiment designed to quantitatively and qualitatively test the supermedia streaming capabilities for telepresence via cellular data networks using a remote controlled (RC) helicopter. In the experiment, the RC helicopter flies with two smartphones attached. The first smartphone streams Skype video, 6-axis telemetry data (accelerometer), and round trip time (RTT) ping to a ground station via cellular data network. The second smartphone on the helicopter receives commands send from a remote location to take still pictures of the target. The system presented is capable of downloading still photos from the flying helicopter to the ground station. The experiment also characterizes packet loss, the positional error in the global positioning system (GPS) and radio reception strength (RSSI in dBm) of the smartphones onboard the RC helicopter. The mean delay exhibited in our experiment shows that supermedia telepresence is possible by cellular data networks.

An image compression method combining discrete wavelet transform (DWT) and vector quantization (VQ) is presented. First, a three-level DWT is performed on the original image resulting in ten separate subbands (ten codebooks are generated using the Self Organizing Feature Map algorithm, which are then used in Vector Quantization, of the wavelet transformed subband images, i.e. one codebook for one subband). These subbands are then vector quantized. VQ indices are Huffman coded to increase the compression ratio. A novel iterative error correction scheme is proposed to continuously check the image quality after sending the Huffman coded bit stream of the error codebook indices through the channel so as to improve the peak signal to noise ratio (PSNR) of the reconstructed image. Ten error codebooks (each for each subband of the wavelet transformed image) are also generated for the error correction scheme using the difference between the original and the reconstructed images in the wavelet domain. The proposed method shows better image quality in terms of PSNR at the same compression ratio as compared to other DWT and VQ based image compression techniques found in the literature. The proposed method of image compression is useful for various applications in which high quality (i.e. high precision) are critical (like criminal investigation, medical imaging, etc).

For Internet based real-time teleoperation systems, random time delay can cause instability in the closed loop control system and hence hinder task accomplishment. Event based control systems have been proposed to overcome the instability caused by the random time delay. High latency at the transport layer can still impede effective and reliable execution of tasks with high dexterity requirements. Network QoS based dynamic resource allocation has been proposed to increase the efficiency and reliability of task execution. However, these approaches only try to mitigate or overcome the effects of random time delay and do not address the cause of latency issues in the communication channel. This paper addresses the efficiency and reliability requirements for supermedia enhanced teleoperated systems by reducing the end-to-end transmission latency through the use of overlay networks. The proposed system reduces the transmission latency by using multiple, disjoint paths in overlay networks. The proposed system facilitates reliable and efficient task completion for tasks with high dexterity requirements. Experimental validation of the proposed teleoperated system using the PlanetLab Network is provided for the task of teleoperating a mobile manipulator system.

This paper reports a case study of using single 3D stereoscopic visual feedback for real-time teleoperation of dexterous tasks. In traditional teleoperation systems, real-time visual feedbacks of multiple monoscopic views of the robot workspace are provided for remote operator. However, it is difficult for the operator to control remote robot to perform dexterous tasks by looking at multiple video feedbacks at the same time. During teleoperation, remote operators usually find multiple 2D visual feedbacks confusing, especially when performing dexterous tasks that require accurate positioning and orientating of robot end-effectors. In this paper, we propose to provide single real-time 3D stereoscopic visual feedback for remote operators so that they perceive remote robot workspace with the sense of depth. This sense of 3D empowers remote operators to accurately position and orient robot end-effector with confidence. Experiments have been conducted to reveal the usefulness of real-time 3D stereoscopic video feedback over multiple monoscopic video feedback in real-time teleoperation.

The use of atomic force microscope (AFM) as a nanomanipulator has been evolving for various kinds of nanomanipulation tasks. Due to the bow effect of the piezo scanner of the AFM, the AFM space is different from the Cartesian space. In this paper, different 3-D nanomanipulation tasks using AFM such as nanolithography, pushing and cutting are discussed. 3-D path planning are performed directly in the AFM space and the 3-D paths are generated based on the 3-D topography information of the surface represented in the AFM space. This approach can avoid the mappings between the AFM space and Cartesian space in planning. By following the generated motion paths, the tip can either follow the topography of the surface or move across the surface by avoiding collision with bumps. Nanomanipulation using this method can be considered as the "true" 3-D operations since the cantilever tip can be controlled to follow any desired 3-D trajectory within the range of AFM space. The experimental study shows the effectiveness of the planning and control scheme.

In real-time Internet based teleoperation, different robotic tasks have different dexterity requirements during task progress and thus different network resources are required for safe and reliable task accomplishment. In order to control remote manipulators efficiently and smoothly via the Internet, dynamic bandwidth allocation is crucial to successful accomplishment of robotic tasks controlled by remote operator. In this paper, a novel bandwidth allocation mechanism is developed based on the online measured task dexterity index of current dexterous tasks so that operators can control remote manipulators efficiently and smoothly even under poor network quality. Experiments have been conducted to demonstrate the effectiveness of the presented resource (bandwidth) allocation algorithm in Internet based teleoperation system.

Previous studies on nanomanipulation using Atomic Force Microscope (AFM) go through the scan-design-manipulation-scan cycle, in which an operator does not have any real-time visual feedback during manipulation. In this paper, a simple model of tip-substrate-object interactive forces has been presented for the qualitative and quantitative analysis. Based on this model, the real-time tip-substrate-object interactive forces are used to update the AFM images in order to provide the operator with real-time visual feedback. The real-time visual display combining with real-time force feedback provides an augmented reality environment, in which the operator not only can feel the real-time 3-D interaction forces but also observe the real-time changes of the nano-environment.

This paper introduces new planning and control methods for supermedia-enhanced real-time telerobotic operations via the Internet. Supermedia is the collection of video, audio, haptic information, temperature, and other sensory feedback. However, when the communication medium used, such as the Internet, introduces random communication time delay, several challenges and difficulties arise. Most importantly, random communication delay causes instability, loss of transparency, and desynchronization in real-time closed-loop telerobotic systems. Due to the complexity and diversity of such systems, the first challenge is to develop a general and efficient modeling and analysis tool. This paper proposes the use of Petri net modeling to capture the concurrency and complexity of Internet-based teleoperation. Combined with the event-based planning and control method, it also provides an efficient analysis and design tool to study the stability, transparency, and synchronization of such systems. In addition, the concepts of event transparency and event synchronization are introduced and analyzed. This modeling and control method has been applied to the design of several supermedia-enhanced Internet-based telerobotic systems, including the bilateral control of mobile robots and mobile manipulators. These systems have been experimentally implemented in three sites test bed consisting of robotic laboratories in the USA, Hong Kong, and Japan. The experimental results have verified the theoretical development and further demonstrated the stability, event transparency, and event synchronization of the systems.

This paper presents a QoS based efficiency improving scheme for Internet-based teleoperation. One of the widely used QoS parameters for showing network status is network delay. With the help of event-based control technique, the stability of the teleoperation systems is guaranteed under random network delay. This paper investigates how to improve the efficiency of teleoperated tasks based on the network quality by introducing a Command Negotiator and a robot controller gain adjustment scheme using the measured QoS parameters into the proposed QoS based teleoperation systems. The presented methods improve the efficiency and system responses of the teleoperation systems when the network quality is poor. Moreover, a teleoperation experiment is presented to demonstrate the effectiveness of the proposed controller gains adjustment scheme.

Robotic operations carried out via the Internet face several challenges and difficulties. These range from human-computer interfacing and human-robot interaction to overcoming random time delay and task synchronization. These limitations are intensified when multi-operators at multisites are collaboratively teleoperating multirobots to achieve a certain task. In this paper, a new modeling and control method for Internet-based cooperative teleoperation is developed. Combining Petri net model and event-based planning and control theory, the new method provides an efficient way to model the concurrence and complexity of the Internet-based cooperative teleoperation. It also provides an efficient analysis tool to study the stability, transparency and synchronization of the system. Furthermore, the new modeling and control method enables us to design an Internet-based cooperative telerobotic system that is reliable, safe and intelligent. This new method has been experimentally implemented in a three site test bed consisting of robotic laboratories in the USA, Hong Kong and Japan. The experimental results have verified the theoretical development and further demonstrated the advantages of the new modeling and control method.

A new formulation of an adaptive categorization game of ART-type neural networks is described in this paper. Learning automata dynamics is introduced into the adaptive categorization game in ART-type networks so that the vigilance parameter /spl rho/ is adjusted to give variable-sized clusters. Moreover, repeated game analysis is also conducted on the learning automata-based adaptive categorization game in order to study the asymptotic behaviors of the game when data patterns are passed to the ART networks one after the other.

The e-world, also known as the Internet, has added a new dimension to many of the traditional concepts in industrial applications and everyday life. The use of robots has dramatically expanded the potential of e-services. Individuals with particular expertise can perform highly accurate and fairly complicated tasks remotely via the Internet. This increase in the human reachability is faced by several obstacles. Reliable and efficient robot facilitated services via the Internet face several challenges. These range from human-computer interfacing and overcoming random time delay to task synchronization and human-robot interaction. These limitations intensify when many operators in many sites are involved. This paper provides new theoretical and experimental results on these challenges. Specifically, multisite cooperative control of an Internet based mobile manipulator is presented. The two main characteristics of this system are Internet based real-time closed loop control and coordinated operation. In addition, it is shown that despite random time delay the stability and synchronization of the system were achieved using event-based control.

Factor analysis has been employed for data analysis in behavioral sciences for decades. In this paper, we propose to employ it in robot behavior studies so that important underlying factors that affect the decision-making in robot behavior actions can be extracted. Causal relationships among physical (observed) and logical (unobserved) perceptual dimensions are constructed. Factor analysis provides a simple mean for us to understand what the sensors data, that construct the robot behavioral perceptual space S, are measuring (logical perceptual space extraction). Learning can thus be conducted based on the logical dimensions of the perceptual space, which usually has much lower dimensionality than the original physical perceptual space, of robot behaviors. Analysis of simulated obstacle avoidance behavior is presented.

The development of autonomous robot navigation systems has been a challenge for robot specialists for decades. Different guiding or navigation styles are required for autonomous robots to operate in the intelligent space efficiently and safely under different situations. In this paper an extension of the classical potential based guiding model is presented. The vector field based guiding model is proposed as a unified representation for guiding style model to be used in intelligent space. Simulations have been conducted to demonstrate the effectiveness of the proposed guiding style model and its neuro-fuzzy realization.

The concept of adaptive categorization is introduced to ART-type networks in this paper. Adaptive categorization capability also improves learning performance in self-organizing systems and online learning systems. Classical ART-types networks, however, have only fixed single size cluster formation in categorization, which is controlled by the scalar vigilance parameter. This categorization methodology usually cannot give satisfactory results as the data pattern space is not covered thoroughly by fixed boundary clusters. A game-theoretic formulation and analysis on the competitive clustering nature of ART-type networks are presented. A game-theoretic vigilance parameter adaptation algorithm is then proposed to form variable sized clusters so that the data pattern space is covered much thoroughly. Simulations are presented to demonstrate reliable categorizations obtained from variable sized clusters using game-theoretic vigilance parameter adaptation.

Robot behavior learning is an emerging research topic in robotics. By incorporating learning capability to robots, engineers are not required to hard-code appropriate actions under every possible situation. Actually, this is an impossible task. In this paper, an architecture of behavior learning/operating module (BLOM) for a robot system is proposed. In the BLOM architecture, several categories of situations and actions are formed and mappings among the situation and action categories are established. A Knight Tournament (KT) strategy is proposed for adaptive categorization of situation and action patterns in learning. A computer simulation on learning a robot behavior is also presented.