This paper introduces Quarry-Bot, a Reconfigurable Cable-Suspended Robot developed to support the NASA Artemis program's efforts in preparing for the long-term colonization of the Moon and Mars. Quarry-Bot autonomously clears debris on the lunar surface, a key step in site preparation for future habitats and infrastructure. The system utilizes active control strategies, combined with the Moon's lower gravity, to perform underhand rock tosses as a scalable approach to extraterrestrial site preparation. Its reconfigurable structure, including motorized anchor points and a lightweight tripod design, adjusts cable tensions to generate swing motions for debris displacement. The system is driven by two Dynamixel MX-106 motors for movement and steering, along with a NEMA 17 stepper motor for cable adjustments. Simulations and experiments conducted under both Earth and lunar gravity conditions demonstrate the effectiveness of Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) strategies in achieving rock throws. Quarry-Bot reaches swing angles and projects rocks over distances that may support lunar site clearing and overall engineering purposes. The paper concludes by discussing potential areas for further system refinement, including adjustments for different terrain conditions and improved actuation strategies for lunar missions.

This paper presents Team Avatar-Hubo’s Avatar system designed for telepresence, along with our strategy to fulfill the testing task requirements in the ANA Avatar XPRIZE Finals. Our system is built upon the foundation of DRC-Hubo and incorporates a VR Motion Tracker system, bi-directional audio feedback, visual feedback, haptic sensing, and navigation. During the ANA Avatar XPRIZE Finals evaluation, our proposed Avatar system achieved the 11th Place Award among a pool of 17 Finalists. We provide comprehensive insights into our Avatar System, including its Hardware-Software Architecture, capabilities in Auditory-Vision, manipulation, Texture perception, locomotion, and Interface. Furthermore, we extensively discuss the key lessons we learned during our participation in the competition.

This paper presents a study that evaluates the effects on the walking pattern of a full-sized humanoid robot as it pushes different carts. Furthermore, it discuss a modified Zero Moment Point (ZMP) pattern based on a capture point method, and a friction compensation method for the arms. Humanoid researchers have demonstrated that robots can perform a wide range of tasks including handling tools, climbing ladders, and patrolling rough terrain. However, when it comes to handling objects while walking, humanoids are relatively limited; it becomes more apparent when humanoids have to push a cart. Many challenges become evident under such circumstances; for example, the walking pattern will be severely affected by the external force opposed by the cart. Therefore, an appropriate walking pattern dynamic model and arm compliance are needed to mitigate external forces. This becomes crucial in order to ensure the robot's self-balance and minimize external disturbances.

This paper presents a concept for haptic-based human-in-the-loop aerial manipulation for drilling. The concept serves as a case study for designing the human-drone interface to remotely drill with a mobile-manipulating drone. The notion of the work stems from using drones to perform dangerous tasks like material assembly, sensor insertion while being vertically elevated from bridge, wind turbine, and power line. Presented is the aerial manipulator, the customized haptic drill press, the gantry-based test-and-evaluation platform design, material drilling results in the gantry, and validation-and-verification results for indoor flight trials.

Automation in construction continues to be a topic of interest for many in industry and academia. However, the dynamic environments presented in construction sites prove these tasks to be difficult to automate reliably. This paper proposes a novel method of teleoperation for multiple heterogeneous robots within a construction environment. The system is achieved by creating a virtual reality interface that allows an operator to control multiple robots both synchronously and asynchronously. Feedback is provided from an array of RGBD cameras, force sensors, and precise odometry data. The DRC-Hubo and Spot robot platforms are used for implementation and experimentation. Experiments include useful tasks for construction including item manipulation and item delivery of tools and components. Results demonstrate the feasibility of implementing the system in a construction environment, including trajectory comparisons, task learning curves, and successful multi-robot collaboration.

As the number of users on social media platforms increases, a problem of great significance becomes clear: the increasing amount of negativity within the communities. We create a game that has two social media users, or players, trying to extend their outreach while keeping a positive community. Using five variables collected from 50 Instagram profiles, we create cost and payoff functions for the decision making part of the game. We implement those functions and make several discoveries: contrary to popular belief, the comments and negative comment percentages are not correlated; the number of followers directly impacted the number of likes on a user's posts; the cost function output increases with the payoff function output in a strong positive correlation; and that an increase in the number of followers number increase on an account results in an increase in number of comments.

In this paper, we present selected benchmark aerial manipulation tasks using an aerial vehicle endowed with multi-degree of freedom manipulators. The proposed tasks analyze environmental coupling and are broken into three general categories: momentary, loose, and strong coupling. A classical control structure is derived, tuned, and verified through experiments, conducted for benchmarking purposes to include pick-and-place, insertion, and valve-turning tasks. Although other nonlinear controllers may prove more effective, the classical control approach has been selected in order to analyze contact stability and provide benchmark results for future reference. An analysis of system stability is conducted and implemented into the controller. A vision-based high-level controller fuses motion tracking data in order to provide control of both the aircraft and the manipulators, allowing the system to become coupled to the environment and perform the required operation. We present recent results validating our framework using the proposed aircraft-arm system.

Graffiti work using a humanoid with artistic technique can convey the value of artists' work to people. Previous work that focused on drawing an image on a canvas accurately has not contained artistic processes like performance, drawing skills and other artists' intents at the time of creation. To combine such artistic processes, the work in this paper utilizes whole-body motion of a humanoid to paint an image on a wall using Pointillism. However, a biped humanoid consists of high Degree Of Freedom (DOF) system and is very sensitive to internal and external disturbances from interaction with environments. As is the case when graffitiing on a wall. Most notably, the vibration from impact contacts and mechanical uncertainties limit the humanoid in graffitiing properly. This paper presents an approach to realize drawing a large image on a wall through real-time motion planning for printing, artificial compliance, and a disturbance controller.

Supervision and teleoperation of high degree-of-freedom robots is a complex task due to environmental constraints such as obstacles and limited communication, as well as task specific requirements such as using more than one end-effector at the same time. In this work we present a supervision and teleoperation framework that allows an operator to see the surroundings of a robot in 3D, make necessary adjustments for a dual or single arm manipulation task, preview the task in simulation before execution, and finally execute the task on a real robot. The framework has been applied to the valve turning task of the DARPA Robotics Challenge on the PR2, Hubo2+, and DRCHubo robots.

This paper presents an autonomous planning and control framework for humanoid robots to climb general ladder- and stair-like structures. The approach consists of two major components: 1) a multi-limbed locomotion planner that takes as input a ladder model and automatically generates a whole-body climbing trajectory that satisfies contact, collision, and torque limit constraints; 2) a compliance controller which allows the robot to tolerate errors from sensing, calibration, and execution. Simulations demonstrate that the robot is capable of climbing a wide range of ladders and tolerating disturbances and errors. Physical experiments demonstrate the DRC-Hubo humanoid robot successfully mounting, climbing, and dismounting an industrial ladder similar to the one intended to be used in the DARPA Robotics Challenge Trials.

This paper introduces a new approach for humanoid robot quasi-static walking for improving stability performance, which is called ski-type walking. By adding two canes held by hands, the supporting region and stability margin are enlarged in comparison with biped walking. We first study the mechanism of cane-assisted walking by human beings. Based on the study, we develop two ski-type gaits, Crawl_1 and Craw-2, respectively for the humanoid robot Hubo. The stability performance for the two configurations is compared, which leads to the adoption of the Crawl_2 gait. Furthermore the length of the canes is selected to support a feasible while stable Crawl_2 gait. Finally simulation and experiments are performed to verify the new ski-type gait.

This video presents our preliminary work towards addressing the ladder climbing event in DARPA Robotics Challenge (DRC) using DRC-Hubo robot. A ladder-climbing motion planner is developed which generates a collision-free, stable quasi-static trajectory for execution. Compliance control is enabled on arm joints to compensate for the calibration error, modeling error and control error. We have demonstrated that DRC-Hubo can robustly climb a variety of ladders in simulation and successfully climb a ship ladder on the hardware.

We propose a framework for valve turning using an aerial vehicle endowed with dual multi-degree of freedom manipulators. A tightly integrated control scheme between the aircraft and manipulators is mandated for tasks requiring aircraft to environmental coupling. Feature detection is well-established for both ground and aerial vehicles and facilitates valve detection and arm tracking. Force feedback upon contact with the environment provides compliant motions in the presence of position error and coupling with the valve. We present recent results validating the valve turning framework using the proposed aircraft-arm system during flight tests.

In this paper, we report lessons learned through the design of a framework for teleoperating a humanoid robot to perform a manipulation task. We present a software framework for cooperative traded control that enables a team of operators to control a remote humanoid robot over an unreliable communications link. The framework produces statically-stable motion trajectories that are collision-free and respect end-effector pose constraints. After operator confirmation, these trajectories are sent over the data link for execution on the robot. Additionally, we have defined a clear operational procedure for the operators to manage the teleoperation task. We applied our system to the valve turning task in the DARPA Robotics Challenge (DRC). Our framework is able to perform reliably and is resilient to unreliable network conditions, as we demonstrate in a set of test runs performed remotely over the internet. We analyze our approach and discuss lessons learned which may be useful for others when designing such a system.

This paper describes the hardware and software components of a general-purpose humanoid robot system for autonomously driving several different types of utility vehicles. The robot recognizes which vehicle it is in, localizes itself with respect to the dashboard, and self-aligns in order to interface with the steering wheel and accelerator pedal. Low- and higher-level methods are presented for speed control, environment perception, and trajectory planning and following suitable for operation in planar areas with discrete obstacles as well as along road-like paths.

This paper presents a control scheme to achieve dynamic stability in an aerial vehicle with dual multi-degree of freedom manipulators. Arm movements assist with stability and recovery for ground robots, in particular humanoids and dynamically balancing vehicles. However, there is little work in aerial robotics where the manipulators themselves facilitate flight stability or the load mass is repositioned in flight for added control. We present recent results in arm motions that achieve increased flight stability without and with different load masses attached to the end-effectors. Our test flight results indicate that we can accurately model and control our aerial vehicle when both moving the manipulators and interacting with target objects.

In this research, an approach to implement a collaborative task between a humanoid robot (Hubo) and a human is presented. Velocity control using motion data generated from a motion capture system (MoCap) is used to control Hubo's lower body movement. The difference in moving direction and speed between the robot and a worker produced a step distance and turning angle of subsequent steps. For upper body control of Hubo, passive control enables the robot's arms to respond adaptively to human arm movements and diminishes undesired reaction forces from a human worker. For better interactive collaboration, several messages were chosen to assist communication between a human and Hubo. For each specific message, various kinds of sign language were initially designed and collected by MoCap. Captured signs were evaluated using Monte Carlo method and an optimized sign was determined based on the stability of carried objects and the robot itself. Finally, an experimental evaluation of the presented approach with the chosen signs was demonstrated through a real collaborative task between Hubo and a human worker which was carrying panels of various sizes.

Ongoing efforts toward mobile manipulation from an aerial vehicle are presented. Recent tests and results from a prototype rotorcraft have shown that our hybrid structure increases stability during flight and manipulation. Since UAVs require significant setup time, suitable testing locations, and have tendencies to crash, we developed an aerial manipulation test and evaluation environment that provides controllable and repeatable experiments. By using force feedback techniques, we have designed multiple, dexterous, redundant manipulators that can grasp objects such as tools and small objects. These manipulators are controlled in concert with an emulated aerial platform to provide hovering stability. The manipulator and aircraft flight control are tightly coupled to facilitate grasping without large perturbations in the end-effector.

This work shows a method of creating trajectories to achieve end-effector velocity control for high degree of freedom position controlled, high-gain, robots. The focus of this work is throwing an object. It is shown that the full reachable area of the end-effector does not need to be known to achieve the desired velocity when a good collision model of the robot is available. The end-effector velocity (magnitude and direction) is specified as well as a duration of this velocity. A sparse map of reachable end-effector positions in free space and the corresponding poses in joint space is created using random sampling in joint space and forward kinematics. The desired trajectory in free space is placed within the sparse map with the first point of the trajectory being a known pose from the original sparse map. The Jacobian Transpose Controller method of inverse kinematics is then used to find the subsequent points in the trajectory. Each pose in the trajectory is checked against the collision model to guarantee no self-collisions. This method was tested on the 130 cm tall full size humanoid Jaemi Hubo and its virtual representation.

In this research, an approach to optimize motions for a humanoids is presented. Rapidly-exploring Random Tree(RRT) were used to plan an initial suboptimal motion. A reinforcement learning was then implemented to optimize the trajectories with respect to energy consumption, similarity to a human's natural motion and, physical limits. Energy cost was estimated by joint torque from a dynamic model, and validated against actual measured torque values using system identification (SID). With a motion capture system, human motions were collected for a given set of tasks, producing a representative “natural” motion, another cost for optimization. Physical limits of each joint ensured spatial and temporal smoothness of generated trajectories. Finally, an experimental evaluation of the presented approach was demonstrated through simulation using MiniHubo model in OpenRAVE.

In pursuit of our long-term goal of developing an interactive humanoid musician, we are developing robust methods to determine musical beat locations from live acoustic sources. A variety of beat tracking systems have been previously developed, but for the most part they are optimized for direct audio input (no acoustic channel and no noise). The presence of an acoustic channel and noise typically degrades performance substantially. A robot's motors, in particular, create nonstationary noise that can be difficult for a beat detection system to accommodate, Using an algorithm previously developed by the authors, we explore techniques for reducing the effects of the acoustic channel and noise on the system, enabling a humanoid to robustly follow music under realistic conditions.

This paper details a new robotics programming framework called Conductor. The framework is unique in that it represents the hardware-software interface, and a user's interaction with that interface, in terms of state variables. Within Conductor hardware is represented to the user by its states of interest, with all other interface concerns abstracted to the greatest extent possible. This representation is enabled by a five-layered component structure which this paper describes. The structure of the program allows a designer to take advantage of bandwidth-saving optimizations in high degree of freedom cases and significantly improve performance over that of current tools.

To address the lack of motion feedback to a UAV pilot, a system was developed that integrates a motion simulator into UAV operations. The system is designed such that during flight, the angular rate of a UAV is captured by an onboard inertial measurement unit (IMU) and is relayed to a pilot controlling the vehicle from inside the motion simulator. Efforts to further increase pilot SA led to the development of a mixed reality chase view piloting interface. Chase view is similar to a view of being towed behind the aircraft. It combines real world onboard camera images with a virtual representation of the vehicle and the surrounding operating environment.

Future applications will bring unmanned aerial vehicles (UAVs) to near Earth environments such as urban areas, causing a change in the way UAVs are currently operated. Of concern is that UAV accidents still occur at a much higher rate than the accident rate for commercial airliners. A number of these accidents can be attributed to a UAV pilot's low situation awareness (SA) due to the limitations of UAV operating interfaces. The main limitation is the physical separation between the vehicle and the pilot. This eliminates any motion and exteroceptive sensory feedback to the pilot. These limitation on top of a small field of view from the onboard camera results in low SA, making near Earth operations difficult and dangerous.

Future applications will bring unmanned aerial vehicles (UAVs) to new environments such as urban areas, causing a change in the way that UAVs are currently operated. However, UAV accidents still occur at a much higher rate than the accident rate for commercial airliners. Therefore, there is a need to better train UAV pilots and augment their performance to minimize accidents. In this paper, the authors present two methods for generating a chase view point (similar to the view of being towed behind the aircraft). Through use of these viewpoints, the authors propose to increase the situational awareness of UAV operators when flying in cluttered environments. The chase view combines a rotated onboard camera view with a virtual representation of the vehicle and the surrounding operating environment. Experiments were conducted evaluating a chase view versus a traditional onboard camera view during UAV flights using a 6 DOF gantry system. Results showed that the chase view improved UAV operator performance.

Small bird-sized aerial robots are expendable and can fly over obstacles and through small openings to assist in the acquisition and distribution of intelligence during reconnaissance, surveillance, and search-and-rescue missions in urban environments. However, limited flying space and densely populated obstacle fields require a vehicle that is capable of hovering but is also maneuverable. A secondary flight mode was incorporated into a fixed-wing aircraft to preserve its maneuverability while adding the capability of hovering. An inertial measurement sensor and onboard flight control system were interfaced and used to transition the hybrid prototype from cruise to hover flight and sustain a hover autonomously. Furthermore, the hovering flight mode can be used to maneuver the aircraft through small openings such as doorways. An ultrasonic and infrared sensor suite was designed to follow exterior building walls until an ingress route was detected. Reactive control was then used to traverse the doorway and gather reconnaissance. This paper describes the holistic approach of platform development, sensor suite design, and control of the hybrid prototype.

Small unmanned air vehicles (UAVs) such as AeroVironments Puma, are designed for military support at the tactical level. These miniature UAVs can be rapidly deployed via a hand or bungee launch. Most commercial unmanned aircraft currently in service are autonomous in the sense that they can be programmed to follow GPS waypoint routes and can be updated in mid-flight. Typical missions include surveillance, reconnaissance, bomb damage assessment, and search and rescue. Although most missions are carried out at extremely low altitudes (e.g. 20100 meters), the UAV flight control systems do not have collision avoidance capabilities. Furthermore, flight in these low altitude or near-Earth environments, such as urban areas and mountainous terrain, often degrade GPS signals.

Recent events have proven the utility of aerial robots in search and rescue and military operations performed in near-Earth environments. However, autonomy in such environments is still limited by the robot's ability to perform collision avoidance. Real time, high fidelity maps can be utilized to assist the robot in performing collision avoidance. This paper explores the use of a laser range finder to accomplish real time map building in the presence of obscurants. Experiments were conducted to determine the probabilistic models of the sensor in the presence of rain and fog. These models are then utilized to map obstacles and terrain features in the presence of obscurants. Preliminary results show the implementation of these methods in an aerial vehicle testing facility.

Recently, there is a need to acquire intelligence in hostile or dangerous environments such as caves, forests, or urban areas. Rather than risking human life, backpackable, bird-sized aircraft, equipped with a wireless camera, can be rapidly deployed to gather reconnaissance in such environments. However, they first must be designed to fly in tight, cluttered terrain. This paper discusses an additional flight modality for a fixed-wing aircraft, enabling it to supplement existing endurance superiority with hovering capabilities. An inertial measurement sensor and an onboard processing and control unit, used to achieve autonomous hovering, are also described. This is, to the best of our knowledge, the first documented success of hovering a fixed-wing micro air vehicle autonomously

Today's aerial robots are being tasked to fly in near-Earth environments such as caves, forests and buildings. The lack of flight data and performance metrics poses a gap that prevents the analytical design of such robots. This paper describes a test rig with a full-scale diorama in its workspace. Lamps, fans, and generators allow the control of lighting, gust and obscurants to emulate conditions found in near-Earth environments. The rig's motions resemble the actual robotic aircraft through model reference adaptive control; sensor data feed into a high-fidelity math model of the aircraft's dynamics to generate rig motion response

Fully autonomous aerial robots are needed to accomplish near-Earth missions such as search and rescue, and bio-chemical sensing. Recent advances in sensor technologies urge the aerial robotics community towards full autonomy. Only then, the sensors that are needed to detect obstacles will become more accurate, reliable, and smaller. The lack of flight test data in near-Earth environments poses a gap in the modeling and integration of obstacle detection sensors. To help advance the aerial robotics field we designed a hardware-in-the-loop system for near-Earth flying robots. The hardware-in-the-loop system generates the motion of the actual robotic aircraft in near-Earth environment by implementing model reference adaptive control (MRAC). The high-fidelity math model of the robotic aircraft is the reference model of the MRAC. The controller structure of the system is explained in this paper

Platforms like gantries, booms, aircraft and submersibles are equipped with cameras. People teleoperate such platforms to capture desired views of a scene or a target. To avoid collisions with the environment and occluded views, such platforms are often equipped with redundant degrees-of-freedom. The operator must manually coordinate multiple degrees-of-freedom in order to get desired views. Tracking moving targets becomes especially tedious and often requires several highly skilled operators. Visual servoing some degrees-of-freedom may reduce operator burden and improve tracking performance. This video showcases the results using such an approach.

Near-earth environments are time consuming, labor intensive and possibly dangerous to safe guard. Accomplishing tasks like bomb detection, search-and-rescue and reconnaissance with aerial robots could save resources. This work describes the adoption of insect behavior and flight patterns to develop a MAV sensor suite. A prototype called CQAR: closed quarter aerial robot, which is capable of flying in and around buildings, through tunnels and in and out of caves is used to validate the efficiency of such a method when equipped with optic flow microsensors.

Cameras are often mounted on platforms that can move like rovers, booms, gantries and aircraft. People operate such platforms to capture desired views of a scene or a target. To avoid collisions with the environment and occlusions, such platforms often possess redundant degrees-of-freedom. As a result, manual manipulating of such platforms demands much skill. Visual-servoing some degrees-of-freedom may reduce operator burden and improve tracking performance. This concept, which we call human-in-the-loop visual-servoing, is demonstrated And applies a /spl alpha/ /spl rarr/ /spl beta/ /spl rarr/ /spl gamma/ filter and feedforward controller to a broadcast camera boom.

In times of disaster acquiring aerial images is challenging. Runaways may be crippled thus denying conventional aircraft in the area from taking off. Also the time required to schedule a satellite fly-by may delay first response efforts. Man backpackable aerial robots can be carried close to the disaster site and flown to capture aerial images. This paper integrated mechatronics, intelligent sensing, and mechanism synthesis in a teleoperable kite-mounted camera. Rapidly deployable, our system can quickly acquire, process and distribute aerial images. Image mosaicing, edge detection, 3D reconstruction and geo-referencing resulting from images acquired by our aerial platform are also presented.

More often homeland security, disaster mitigation and military operations are performed in urban environments. Time consuming, labor intensive and possibly dangerous tasks like bomb detection, search-and-rescue and reconnaissance done with robots could save resources. An aerial robot capable of flying in closed quarters like warehouses, stadiums, underground parking lots and tunnels is featured. The working prototype can fly slowly, safely and transmit wireless video for situational awareness. The design is analytic and employs a multi-disciplinary design optimization to formulate the integration of aerodynamics, sensor suite and task performance.

There are many features to take into consideration when designing servoed vision for robot control, especially if there are redundant degrees-of-freedom (DOF). When human teleoperated platforms like rovers, booms, gantries, aircraft and submersibles are involved, visual-servoing is particularly challenging. Along with the kinematics and dynamics characteristics in the robot DOF one must consider the human-in-the-loop. Another factor that must be considered is potential motion conflicts arising from the shared man-machine control of the camera and can lead to unstable performance. When the DOF are redundant, the tracking problem becomes more complicated because there are many ways to perform tracking. This paper illustrates these issues and suggests control approaches to resolve them.

There are many design factors and choices when mounting a vision system for robot control. Such factors may include the kinematic and dynamic characteristics in the robot's degrees of freedom (DOF), which determine what velocities and fields-of-view a camera can achieve. Another factor is that additional motion components (such as pan-tilt units) are often mounted on a robot and introduce synchronization problems. When a task does not require visually servoing every robot DOF, the designer must choose which ones to servo. Questions then arise as to what roles, if any, do the remaining DOF play in the task. Without an analytical framework, the designer resorts to intuition and try-and-see implementations. This paper presents a frequency-based framework that identifies the parameters that factor into tracking. This framework gives design insight which was then used to synthesize a control law that exploits the kinematic and dynamic attributes of each DOF. The resulting multi-input multi-output control law, which we call partitioning, defines an underlying joint coupling to servo camera motions. The net effect is that by employing both visual and kinematic feedback loops, a robot can quickly position and orient a camera in a large assembly workcell. Real-time experiments tracking people and robot hands are presented using a 5-DOF hybrid (3-DOF Cartesian gantry plus 2-DOF pan-tilt unit) robot.

Humans have degrees-of-freedom (DOF) of varying bandwidths and one casually observes that we coordinate these DOF while visually tracking. This suggests that joint interplay aids tracking performance. In a control scheme we call partitioning, both image and kinematic data are used to visually-servo a 5-DOF robot by defining a joint-coupling among the rotational and translational DOF. Analysis of simulations and experiments reveal that a robot's fast bandwidth joints physically serve as lead compensators when coupled to slower joints thus reducing tracking lag.

We present a novel approach we call partitioning where the robot's degrees-of-freedom (DOF) are categorized into two classes based on joint kinematics and dynamics to design a coupled multi-input control system. We use image data to visually servo the first class of joints that have quick response time. Position-based data is used to kinematically servo the second class of joints that have large kinematic range. The net effect is an active-vision system that synergistically tracks a diverse range of targets (without using CAD-based models) over a wide bandwidth of motion dynamics.

Image-based servoing systems are often used to track moving targets and their underlying control architecture is a regulation of the image. This regulation is a function of rigid camera-to-target geometric constraints. Satisfying such constraints requires that the robot motors have sufficient velocity bandwidths, and often these bandwidths are limited. This paper lays down the foundation for a partitioned controller. Such a controller would coordinate a camera's DOF into a synergistic move to overcome bandwidth limitations. Tracking experiments are shown on a custom designed 5-DOF gantry robot which highlight the limitations of regulator-based control, as well as show how partitioning can be used to achieve more robust control.

Most robotic hands are either sensorless or lack the ability to accurately and robustly report position and force information relating to contact. This paper describes a robotic hand system that uses a limited set of native-joint position and force sensing along with custom-designed tactile sensors and real-time vision modules to accurately compute finger contacts and applied forces for grasping tasks. Three experiments are described: integration of real-time visual trackers in conjunction with internal strain gauge sensing to correctly localize and compute finger forces, determination of contact points on the inner and outer links of a finger through tactile sensing and visual sensing, and determination of vertical displacement by tactile sensing for a grasping task.

Systems discretized using the conventional z-transform and zero-order-hold will lead to sampling zeros lying outside the unit circle upon fast sampling. The Euler operator has been shown to be an alternative to discretizing systems. The resulting discrete-time form approaches its original continuous-time form as the sampling interval approaches zero. A model reference adaptive controller is designed using the Euler operator. Simulations and experiment were performed on an electrohydraulic servo valve. The resulting controller is applied to a Stewart platform for simulating ship motion.