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Autonomous space robots will be required for such future missions as the construction of large space structures and repairing disabled satellites. These robots will need to be precisely controlled. However, factors such as manipulator joint/actuator friction and spacecraft attitude control thruster inaccuracies can substantially degrade control system performance. Sensor-based control algorithms can be used to mitigate the effects of actuator error, but sensors can add substantially to a space system’s weight, complexity, and cost, and reduce its reliability. Here, a method is presented to determine the sensor architecture that uses the minimum number of sensors that can simultaneously compensate for errors and disturbance in a space robot’s manipulator joint actuators, spacecraft thrusters, and reaction wheels. The placement and minimal number of sensors is determined by analytically structuring the system into “canonical chains” that consist of the manipulator links and spacecraft with force/torque sensors placed between the space robot’s spacecraft and its manipulators. These chains are combined to determine the number of sensors needed for the entire system. Examples of one- and two-manipulator space robots are studied and the results are validated by simulation.

Modular self-assembling on-orbit robots have the potential to reduce mission costs, increase reliability, and permit on-orbit repair and refueling. Modules with a variety of specialized capabilities would self-assemble from orbiting inventories. The assembled modules would then share resources such as power and sensors. As each free-flying module carries its own attitude control actuators, the assembled system has substantial sensor and actuator redundancy. Sensor redundancy enables sensor fusion that reduces measurement error. Actuator redundancy gives a system greater flexibility in managing its fuel usage. In this paper, the control of self-assembling space robots is explored in simulations and experiments. Control and sensor algorithms are presented that exploit the sensor and actuator redundancy. The algorithms address the control challenges introduced by the dynamic interactions between modules, the distribution of fuel resources among modules, and plume impingement.

Cars and trucks are susceptible to accidents due to rollover. In the United States in 2005, 21.1% of a total of 54,718 deaths in vehicle crashes were caused by rollover [1]. Significant research has therefore been de voted to detecting and preventing rollover through active control. Numerous approaches attempt to detect or pre dict wheel liftoff using onboard sensing and a combina tion of automatic steering and braking to keep the wheels on the ground [2]-[4]. Rather than focusing on how to keep the wheels on the ground, it is also useful to under stand how to control a vehicle while two wheels are in the air. This understanding may enable the design of control laws that can safely return the vehicle to the ground after inadvertent tip-up.

World oil demand and advanced oil recovery techniques have made the rehabilitation of previously abandoned oil wells economically attractive. This requires relatively fast mapping of the shape and location of the down-hole well structures. Practical factors prohibit the use of visual and other range sensors in this situation. Here, the feasibility of using a robotic manipulator for tactile mapping is studied. A method is developed that requires only robot joint encoders and avoids the use of any force or tactile sensors, which are complex and unreliable in such a hostile environment. This paper addresses the general problem of intelligent tactile exploration of constrained internal geometries when time is critical. It is assumed that the time required to move a manipulator to acquire a new touch point outweighs computational time. This approach models the down-hole structures with geometric primitives and focuses on exploration efficiency by intelligently searching for new touch points to build the geometric models. The algorithms developed here are shown in simulations and laboratory experiments to substantially reduce the data acquisition effort for exploration with a tactile manipulator.

PurposeThis paper seeks to present recent work demonstrating the feasibility of Microbots' mobility in rough terrain. Microbots are a new search and rescue concept based on the deployment of teams of small spherical mobile robots. In this concept, hundreds to thousands of cm‐scale, sub‐kilogram Microbots are released over a search site such as collapsed building rubble or caves. Microbots use hopping, bouncing, and rolling to infiltrate subterranean spaces in search of possible survivors.Design/methodology/approachThe feasibility of the Microbot mobility concept is evaluated through laboratory prototypes and mobility simulations.FindingsExperimental studies have demonstrated the feasibility of using dielectric elastomer actuators (DEAs) to generate autonomous hops. High‐efficiency hydrogen fuel cells were shown to be able to power DEAs. Simulation results show that Microbots of proper diameter and hop height can successfully traverse very rough terrains.Research limitations/implicationsThe implication of this research is that small hopping robots are appropriate for certain search and rescue missions. The limitation of the research to date is that issues of control, path planning, and communication have not yet been addressed.Practical implicationsKey technologies of the Microbot mobility, that use high‐energy‐density micro fuel cells combined with low cost and lightweight DEAs, are feasible. These technologies have the potential to make a significant impact on the search and rescue robots.Originality/valueThese results suggest that a team of Microbots‐based DEAs and micro fuel cells can be a useful and effective tool for search and rescue operations.

Dielectric Elastomer Actuators (DEAs) show promise for robotics and mechatronics applications. They are lightweight, low costs, and have shown good performance in laboratory demonstration. However, these actuators have not been widely applied commercially after more than 10 years of development. One reason is that the mechanisms governing their performance are not completely understood. Hence designing practical actuators is difficult. This paper has the objective of understanding the dominant performance mechanisms of DEAs made with VHB 4905/4910 from 3 M. To do so, an experimental characterization of actuator performance is conducted in terms of force, power, current consumption, work output, and efficiency. Key performance mechanisms of viscoelasticity and current leakage are identified from experimental observations and analytical models are developed. The models explain well the experimental observations and should aid designers in selecting applications that are appropriate for DEAs as well as designing effective DEAs.

Future space applications will require robotic systems to assemble, inspect, and maintain large space structures in orbit. For effective planning and control, robots will need to know the deformation and vibration of the structures with which they interact. This paper presents a method for estimating the shape, motion, and dynamic model parameters of a vibrating space structure using range imaging sensors. The method assumes that the mode shapes are approximately known a priori. An unscented Kalman filter exploits a mechanics-based dynamic model to extract the modal frequencies and damping as well as the modal coefficients and their time rate of change. Both asynchronous-capture (raster-scanning) and synchronous-capture sensors are treated. Theoretical development and experimental results using emulated space hardware are presented.

Wheel sinkage is an important indicator of mobile robot mobility in natural outdoor terrains. This paper presents a vision-based method to measure the sinkage of a rigid robot wheel in rigid or deformable terrain. The method is based on detecting the difference in intensity between the wheel rim and the terrain. The method uses a single grayscale camera and is computationally efficient, making it suitable for systems with limited computational resources such as planetary rovers. Experimental results under various terrain and lighting conditions demonstrate the effectiveness and robustness of the algorithm.

At high speeds in natural terrain, unmanned ground vehicles can experience unexpected situations that require rapid hazard avoidance maneuvers. In these scenarios there is often limited time to replan a path based on high-order dynamic models. This paper presents a novel method for high speed navigation and hazard avoidance based on the two dimensional “trajectory space,” a compact and computationally efficient model-based representation of a robot's dynamic performance limits on natural terrain. This paper also presents a novel method for trajectory replanning, based on a “curvature matching” technique. Experimental results on a small high-speed UGV demonstrate the method's effectiveness.

Unmanned ground vehicles have important applications in high speed rough terrain scenarios. In these scenarios, unexpected and dangerous situations can occur that require rapid hazard avoidance man ...

In field environments it is not usually possible to provide robots in advance with valid geometric models of its environment and task element locations. The robot or robot teams need to create and use these models to locate critical task elements by performing appropriate sensor based actions. This paper presents a multi-agent algorithm for a manipulator guidance task based on cooperative visual feedback in an unknown environment. First, an information-based iterative algorithm to intelligently plan the robot's visual exploration strategy is used to enable it to efficiently build 3D models of its environment and task elements. The algorithm uses the measured scene information to find the next camera position based on expected new information content of that pose. This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. Second, after an appropriate environment model has been built, the quality of the information content in the model is used to determine the constraint-based optimum view for task execution. The algorithm is applicable for both an individual agent as well as multiple cooperating agents. Simulation and experimental demonstrations on a cooperative robot platform performing a two component insertion/mating task in the field show the effectiveness of this algorithm.

Important robotic tasks could be effectively performed by powerful and accurate manipulators. However, high accuracy is generally difficult to obtain in large manipulators capable of producing high forces due to system elastic and geometric distortions. In this work, a high-accuracy patient positioning system is calibrated, consisting of a six degree of freedom manipulator used to position cancer patients during proton therapy sessions. It is found that the original manipulator does not meet the required absolute accuracy due to both geometric and elastic deformation positioning errors. The experimentally identified errors are used to predict, and compensate for, end-point errors as a function of configuration and measured forces, improving the system absolute accuracy. Experimental results show that the adopted methodology is able to effectively correct for the system errors.

In field environments it is often not possible to provide robot teams with detailed a priori environment and task models. In such unstructured environments, robots will need to create a dimensionally accurate three-dimensional geometric model of its surroundings by performing appropriate sensor actions. However, uncertainties in robot locations and sensing limitations/occlusions make this difficult. A new algorithm, based on iterative sensor planning and sensor redundancy, is proposed to build a geometrically consistent dimensional map of the environment for mobile robots that have articulated sensors. The aim is to acquire new information that leads to more detailed and complete knowledge of the environment. The robot(s) is controlled to maximize geometric knowledge gained of its environment using an evaluation function based on Shannon’s information theory. Using the measured and Markovian predictions of the unknown environment, an information theory based metric is maximized to determine a robotic agent’s next best view (NBV) of the environment. Data collected at this NBV pose are fused using a Kalman filter statistical uncertainty model to the measured environment map. The process continues until the environment mapping process is complete. The work is unique in the application of information theory to enhance the performance of environment sensing robot agents. It may be used by multiple distributed and decentralized sensing agents for efficient and accurate cooperative environment modeling. The algorithm makes no assumptions of the environment structure. Hence, it is robust to robot failure since the environment model being built is not dependent on any single agent frame, but is set in an absolute reference frame. It accounts for sensing uncertainty, robot motion uncertainty, environment model uncertainty and other critical parameters. It allows for regions of higher interest receiving greater attention by the agents. This algorithm is particularly well suited to unstructured environments, where sensor uncertainty and occlusions are significant. Simulations and experiments show the effectiveness of this algorithm.

A simplified, closed-form version of the basic mechanics of a driven rigid wheel on low-cohesion deformable terrain is presented. This approach allows the formulation of an on-line terrain parameter estimation algorithm, which has important applications for planetary exploration rovers. Analytical comparisons of the original and simplified equations are presented, and are shown to closely agree. Experimental results from a single-wheel testbed operating in dry sand shows that the simplified equations can be used for mobility prediction with good accuracy. Methods for incorporating the simplified equations into an on-line terrain parameter algorithm are discussed.

Mobile robots are being developed for high-risk missions in rough terrain situations, such as planetary exploration. Here, a rough-terrain control methodology is presented that exploits the actuator redundancy found in multiwheeled mobile robot systems to improve ground traction and reduce power consumption. The algorithm optimizes individual wheel torque based on multiple optimization criteria, which are a function of the local terrain profile. A key element of the method is to be able to include estimates of wheel-terrain contact angles and soil characteristics. A method using an extended Kalman filter is presented for estimating these angles using simple on-board sensors. Simulation and experimental results for a micro-rover traversing challenging terrain demonstrate the effectiveness of the algorithm.

Future planetary exploration missions will use cooperative robots to explore and sample rough terrain. To succeed robots will need to cooperatively acquire and share data. Here a cooperative multi-agent sensing architecture is presented and applied to the mapping of a cliff surface. This algorithm efficiently repositions the systems' sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This map is then distributed among the agents using an information based relevant data reduction scheme. Experimental results for cliff face mapping using the JPL Sample Return Rover (SRR) are presented. The method is shown to significantly improve mapping efficiency over conventional methods.

This paper presents the design of a new lightweight, hyper-redundant, deployable Binary Robotic Articulated Intelligent Device (BRAID), for space robotic systems. The BRAID is intended to meet the challenges of future space robotic systems that need to perform more complex tasks than are currently feasible. It is lightweight, has a high degree of freedom, and has a large workspace. The device is based on embedded muscle type binary actuators and flexure linkages. Such a system may be used for a wide range of tasks, and requires minimal control computation and power resources.@DOI: 10.1115/1.1637647#

The control system for a personal aid for mobility and health monitoring (PAMM) for the elderly is presented. PAMM is intended to assist the elderly living independently or in senior assisted living facilities. It provides physical support and guidance, as well as monitoring basic vital signs for users that may have both limited physical and cognitive capabilities. This paper presents the design of a bi-level control system for PAMM. The first level is an admittance-based mobility controller that provides a natural and intuitive human machine interface. The second level is an adaptive shared controller that allocates control between the user and the computer based on metrics of the user's performance. Field trials at an eldercare facility show the effectiveness of the design.

Future robotic vehicles will perform challenging tasks in rough terrain, such as planetary exploration and military missions. Rovers with actively articulated suspensions can improve rough-terrain mobility by repositioning their center of mass. This paper presents a method to control actively articulated suspensions to enhance rover tipover stability. A stability metric is defined using a quasi-static model, and optimized on-line. The method relies on estimation of wheel-terrain contact angles. An algorithm for estimating wheel-terrain contact angles from simple on-board sensors is developed. Simulation and experimental results are presented for the Jet Propulsion Laboratory Sample Return Rover that show the control method yields substantially improved stability in rough-terrain.

Robots are needed to perform important field tasks such as hazardous material clean-up, nuclear site inspection, and space exploration. Unfortunately their use is not widespread due to their long development times and high costs. To make them practical, a modular design approach is proposed. Prefabricated modules are rapidly assembled to give a low-cost system for a specific task. This paper described the modular design problem for field robots and the application of a hierarchical selection process to solve this problem. Theoretical analysis and an example case study are presented. The theoretical analysis of the modular design problem revealed the large size of the search space. It showed the advantages of approaching the design on various levels. The hierarchical selection process applies physical rules to reduce the search space to a computationally feasible size and a genetic algorithm performs the final search in a greatly reduced space. This process is based on the observation that simple physically based rules can eliminate large sections of the design space to greatly simplify the search. The design process is applied to a duct inspection task. Five candidate robots were developed. Two of these robots are evaluated using detailed physical simulation. It is shown that the more obvious solution is not able to complete the task, while the non-obvious asymmetric design develop by the process is successful.

This analytic and experimental study proposes a control algorithm for coordinated position and force control for autonomous multi-limbed mobile robotic systems. The technique is called Coordinated Jacobian Transpose Control (CJTC). Such position/force control algorithms will be required if future robotic systems are to operate effectively in unstructured environments. Generalized Control Variables (GCVs), express in a consistent and coordinated manner the desired behavior of the forces exerted by the multi-limbed robot on the environment and a system‘s motions. The effectiveness of this algorithm is demonstrated in simulation and laboratory experiments on a climbing system.

The treatment of disease using particle beams requires highly accurate patient positioning. Patients must be well immobilized and precisely aligned with the treatment beam to take full advantage of the dose localization potential. Robots can be used as high accuracy patient positioning systems. In this paper, the first such implementation using robotics techniques for patient positioning will be discussed. This robot is being developed for the Northeast Proton Therapy Center at the Massachusetts General Hospital. The unique requirements and design characteristics of the patient positioning system are presented. Of special interest is the system’s patient positioning accuracy. A systematic methodology to perform the error analysis of serial link manipulators and its application to the PPS is described. Experimental measurements that verified the validity of the method are shown.

A simplified Cartesian computed torque (SCCT) control scheme and its application to an experimental climbing robot named LIBRA is presented. SCCT control is developed exploiting some of the characteristics of highly geared mobile robots. The effectiveness of the method is shown by simulation and experimental results using the LIBRA robot. SCCT control is shown to have improved performance, over traditional Jacobian transpose control, for the LIBRA multilimbed robot. [S0022-0434(00)03501-2]

In 1983 a panel, which included the author, was assembled by the Untied States National Research Council. The goal of the panel was to assess the state-of-the-art in robotics technology, predict its capabilities in five years (1988), and speculate on the state of robot technology in ten years (1993). This paper briefly reviews the council's findings. It also attempts to compare the robotics technology used in industry today to the technology available in today's research laboratories, and to the predictions made by the panel 15 years ago. The objective of this paper is to stimulate discussion of how the robotics research community can enhance its contributions to the future development of robotics.

This work develops a method for tactilely mapping unknown harsh environments such as oil wells, using a manipulator equipped with only position sensors. Because contact with the environment may occur anywhere on the manipulator, determining the contact location is challenging. Here a method is developed, based on a probabilistic classification of the data according to the contact location on the manipulator, and the reconstruction of the surface using such classified data. The approach effectiveness is demonstrated in several case studies and laboratory experiments.

A dynamic modeling method is presented for spatial elastic manipulators that can account for a number of their realistic properties, including bearing clearances, actuator dynamics, and control system characteristics. Forces in the bearing clearances are modeled by nonlinear functions of the links’ relative motions and the internal geometry of the connection, or by experimentally measured properties. A detailed model is given for a revolute connection with radial and axial clearances. Results obtained for a SCARA manipulator show that the combined dynamic effects of bearing clearances, link elasticity, and control system characteristics can significantly degrade the system’s performance.

Some important dynamics and control problems unique to space robotic systems are discussed. Particular attention is paid to free-flying and free-floating space robots that might be used for such tasks as space station repair and construction. Advances in solving these problems are briefly reviewed. Three promising methods for planning and controlling the motion of space robotic systems are presented. It is suggested that a thorough understanding of the fundamental dynamics of these systems will result in effective solutions to their control problems. >

World oil demand and advanced oil recovery techniques have made it economically attractive to rehabilitate previously abandoned oil wells. This requires relatively fast mapping of the shape and location of the down-hole well structures. Practical factors prohibit the use of visual and other range sensors in this situation. Here, the feasibility of robotic tactile mapping is studied. A method is developed that only uses the robot joint encoders and avoids any force or tactile sensor, which are complex and unreliable in such a hostile environment. This paper addresses the general problem of intelligent tactile exploration of constrained internal geometries where time is critical. It is assumed that the time required to move a manipulator to acquire a new touch point outweighs computational time. This approach models the down-hole structures with geometric primitives and focuses on exploration efficiency by intelligently searching for new touch points to build the geometric models. The algorithms developed here are shown in simulations and hardware experiments to substantially reduce the data acquisition effort for exploration with a tactile manipulator.

A parallel manipulator concept using bistable polymer actuators has been developed to perform prostate cancer biopsy, and deliver therapy within the bore of a magnetic resonance imaging (MRI) scanner. The dielectric elastomer actuators (DEAs) used in this manipulator concept are promising for MRI-compatible robotics because they do not interfere with the high magnetic fields of MRI while having good mechanical performance and being low cost. In the past, these actuators have been plagued by robustness problems when used in a continuous manner. Recent studies show that reliability significantly improves when DEAs are used in a bistable manner, as proposed here. This paper investigates the potential of the proposed manipulator concept by evaluating the positioning accuracy and MRI compatibility of a laboratory prototype, developed for clinically relevant design criteria. An analytical model of the manipulator kinematics is presented. Analytical and experimental results validate that the proposed technology can provide an accurate needle placement required to perform prostate cancer treatments. The prototype’s MRI compatibility is validated in a 3 T clinical MRI scanner. The parallel manipulator concept using bistable polymer actuators is shown to be a viable approach to perform MRI-guided needle insertions for prostate cancer biopsy and therapy.

It is suggested that nearly any control algorithm that can be used for fixed-based manipulators also can be employed in the control of free-floating space manipulator systems, with the additional conditions of estimating or measuring a spacecraft's orientation and of avoiding dynamic singularities. This result is based on the structural similarities between the kinematic and dynamic equations for the same manipulator but with a fixed base. Barycenters are used to formulate the kinematic and dynamic equations of free-floating space manipulators. A control algorithm for a space manipulator system is designed to demonstrate the value of the analysis. >

Future robotic manipulator systems will be required to per form complex tasks in space such as satellite repair. These robotic manipulators will encounter a number of kinematic, dynamic, and control problems caused by the dynamic coup ling between the manipulators and its spacecraft. This dy namic coupling also makes it difficult to analyze these sys tems. This paper introduces a new analytical modeling method for space manipulators called the Virtual Manipula tor (VM), which has a fixed base in inertial space at a point called a Virtual Ground. The kinematics and dynamics of the manipulator, spacecraft, and payload can be described relatively easily in terms of the VM. With its fixed base, the Virtual Manipulator is shown to have the potential to be an effective aid for the analysis, design, and development of future space manipulator systems.

Fundamental studies of Dielectric Elastomer Actuators (DEAs) using viscoelastic materials such as VHB 4905/4910 from 3M showed significant advantages at high stretch rates. The film's viscous forces increase actuator life and the short power-on times minimize energy losses through current leakage. This paper presents a design paradigm that exploits these fundamental properties of DEAs called discrete actuation. Discrete actuation uses DEAs at high stretch rates to change the states of robotic or mechatronic systems in discrete steps. Each state of the system is stable and can be maintained without actuator power. Discrete actuation can be used in robotic and mechatronic applications such as manipulation and locomotion. The resolution of such systems increases with the number of discrete states, 10 to 100 being sufficient for many applications. An MRI-guided needle positioning device for cancer treatments and a space exploration robot using hopping for locomotion are presented as examples of this concept.

Safe, autonomous mobility in rough terrain is an important requirement for planetary exploration rovers. Knowledge of local terrain properties is critical to ensure a rover’s safety on slopes and uneven surfaces. This paper presents a method to classify terrain based on vibrations induced in the rover structure by wheel-terrain interaction during driving. Vibrations are measured using an accelerometer on the rover structure. The classifier is trained using labeled vibration data during an off-line learning phase. Linear discriminant analysis is used for on-line identification of terrain classes such as sand, gravel, or clay. This approach is experimentally validated on a laboratory testbed.

This paper presents a new mission concept for planetary exploration, based on the deployment of a large number of small spherical mobile robots (“microbots”) over vast areas of a planet’s surface and subsurface, including structures such as caves and near‐surface crevasses (see Figure 1). This would allow extremely large‐scale in situ analysis of terrain composition and history. This approach represents an alternative to rover and lander‐based planetary exploration, which is limited to studying small areas of a planet’s surface at a small number of sites. The proposed approach is also distinct from balloon or aerial vehicle‐based missions, in that it would allow direct in situ measurement. In the proposed mission, a large number (i.e. hundreds or thousands) of cm‐scale, sub‐kilogram microbots would be distributed over a planet’s surface by an orbital craft and would employ hopping, bouncing and rolling as a locomotion mode to reach scientifically interesting artifacts in very rugged terrain. They would be powered by high energy‐density polymer “muscle” actuators, and equipped with a suite of miniaturized imagers, spectrometers, sampling devices, and chemical detection sensors to conduct in situ measurements of terrain and rock composition, structure, etc. Multiple microbots would coordinate to share information, cooperatively analyze large portions of a planet’s surface or subsurface, and provide context for scientific measurements.

Future planetary exploration missions will use cooperative robots to explore and sample rough terrain. To succeed robots will need to cooperatively acquire and share data. Here a cooperative multi-agent sensing architecture is presented and applied to the mapping of a cliff surface. This algorithm efficiently repositions the systems’ sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This map is then distributed among the agents using an information based relevant data reduction scheme. Experimental results for cliff face mapping using the JPL Sample Return Rover (SRR) are presented. The method is shown to significantly improve mapping efficiency over conventional methods.

Future planetary exploration missions can require rovers to perform difficult tasks in rough terrain, with limited human supervision. Knowledge of terrain physical characteristics would allow a rover to adapt its control and planning strategies to maximize its effectiveness. This paper describes recent and current work at MIT in the area of onboard terrain estimation and sensing utilizing visual, tactile, and vibrational feedback. A vision-based method for measuring wheel sinkage is described. A tactile method for on-line terrain parameter estimation is also presented. Finally, a method for terrain classification based on analysis of vibration in the rover suspension is described. It is shown through simulation and experimental results that these methods can lead to accurate and efficient understanding of a rover's physical surroundings.

Mobile robots have important applications in high speed, rough-terrain scenarios. In these scenarios, unexpected and hazardous situations can occur that require rapid hazard avoidance maneuvers. At high speeds, there is limited time to perform re-planning based on detailed vehicle and terrain models. Furthermore, detailed models often do not accurately predict the robot’s performance due to model parameter and sensor uncertainty. This paper presents a method for high speed hazard avoidance. The method is based on the concept of the trajectory space, which is a compact model-based representation of a robot’s dynamic performance limits in uneven, natural terrain. A Monte Carlo method for analyzing system performance despite model parameter uncertainty is briefly presented, and its integration with the trajectory space is discussed. Simulation results for the hazard avoidance algorithm are presented and demonstrate the effectiveness of the method.

An architecture for the estimation of dynamic state, geometric shape, and model parameters of objects in orbit using on-orbit cooperative 3-D vision sensors is presented. This has application in many current and projected space missions, such as automated satellite capture and servicing, debris capture and mitigation, and large space structure assembly and maintenance. The method presented here consists of three parts: (1) kinematic data fusion, which condenses sensory data into coarse kinematic surrogate measurements; (2) Kalman filtering, which filters these surrogate measurements and extracts the full dynamic state and model parameters of the target; and (3) shape estimation, which uses filtered pose information and the raw sensory data to build a body-fixed probabilistic map of the target's shape. This method does not rely on feature detection, optical flow, or model matching, but rather exploits the well-modeled dynamics of objects in space using the Kalman filter. The architecture is computationally fast since only coarse measurements need to be provided to the Kalman filter. This paper illustrates the three steps of the architecture in the context of rigid body (satellite and debris) estimation and flexible structure estimation.

This paper presents an architecture for the estimation of dynamic state, geometric shape, and inertial parameters of objects in orbit, using on-orbit cooperative 3-D vision sensors. This has application in many current and projected space missions, such as satellite capture and servicing, debris capture and mitigation, and large space structure assembly and maintenance. The method presented here consists of three distinct parts: (1) kinematic data fusion, which condenses sensory data into a coarse estimate of target pose; (2) Kalman filtering, which filters these coarse estimates and extracts the full dynamic state and inertial parameters of the target; and (3) shape estimation, which uses filtered pose information and the raw sensory data to build a probabilistic map of the target’s shape. This method does not rely on feature detection, optical flow, or model matching, and therefore is robust to the harsh sensing conditions of space. Instead, it exploits the well-modeled dynamics of objects in space through the Kalman filter. The architecture is computationally fast since only coarse measurements need to be provided to the Kalman filter. This paper will summarize the three steps of the architecture. Simulation results will follow showing the theoretical performance of the architecture.

A long duration robotic presence on lunar and planetary surfaces will allow the acquisition of scientifically interesting information from a diverse set of surface and sub-surface sites. The wide range of terrain types including plains, cliffs, sand dunes, and lava tubes will require the development of robotic systems that can adapt to possibly rapidly changing terrain. These systems include single as well as teams of robots. In this paper, we describe the development of an integrated suite of autonomous, adaptive hardware/software control methods called SMART (System for Mobility and Access to Rough Terrain) that enables mobile robots to explore potentially important science sites currently beyond the reach of conventional rover designs. SMART uses the behavior coordination mechanisms of CAMPOUT, a previously developed system for multi-agent control. For the specific application area of cliffside exploration, SMART consists of a distributed sensing system for cooperative map-making called MITSAF (Model-based Information Theoretic Sensing And Fusion), a mobility system for rappelling down a cliff and moving to a designated way-point, and science sample acquisition from the cliff face. We also report the results of some experimental studies on highly sloped cliff faces.

In field environments it is not usually possible to provide robotic systems with valid geometric models of the task and environment. The robot or robot teams will need to create these models by performing appropriate sensor actions. Here, an algorithm based on iterative sensor planning and sensor redundancy is proposed to enable them to efficiently build 3D models of the environment and task. The method assumes stationary robotic vehicles with cameras carried by articulated mounts. The algorithm uses the measured scene information to find new camera mount poses based on information content. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulations show the effectiveness of this algorithm.

A practical method to obtain the global time optimal motions of robotic manipulators is presented. This method takes into account the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacles. Previously developed methods of optimizing manipulator motions along given paths and a local path optimization are utilized. A set of best paths is obtained first in a global search over the manipulator workspace, using graph search and hierarchical pruning techniques. These paths are used as initial conditions for a continuous path optimization to yield the global optimal motion. Examples of optimized motions of a six-degree-of-freedom manipulator, operating in a three-dimensional space with obstacles, are presented. >

Long development times and high costs prevent robots from being practical for use in many important field missions. Here a modular design approach is proposed to produce a rapidly deployable low cost field robotic system. An inventory of components such as actuated joints, links, power supplies, and software modules are assembled to produce a field robotic system for a specified task. The proposed design method uses a hierarchical selection process combined with a genetic algorithm to select a robot configuration and action plan for a given task. This method is applied to an inspection task required for the preservation of the USS Constitution, an historic naval ship.

A practical method is proposed for estimating the inertial parameters of robot manipulators with substantial unmodeled joint friction and actuator dynamics. The manipulator is mounted on a six-axis force/torque sensor. Sensor measurements and joint velocities recorded during manipulator motion are used to identify the inertial parameters. The unmodeled joint friction and actuator dynamics do not degrade the estimation results, as in conventional methods. The estimation algorithm does not require difficult-to-measure acceleration measurements. Experimental results presented show that an accurate estimation of inertia parameters is attainable. Since the sensor is external to the manipulator, the same sensor can be used for parameter estimation for a number of different systems.

This paper presents the application of an action module planning method to an experimental climbing robot named LIBRA. The method searches for a sequence of physically realizable actions, called action modules, to produce a plan for a given task. The search is performed with a hierarchical selection process that uses task and configuration filters to reduce the action module inventory to a reasonable search space. Then, a genetic algorithm search finds a sequence of actions that allows the robot to complete the task without violating any physical constraints. The results for the LIBRA climbing robot show the method is able to produce effective plans.

High-speed unmanned ground vehicles have important potential applications, including reconnaissance, material transport, and planetary exploration. During high-speed operation, it is important for a vehicle to sense changing terrain conditions, and modify its control strategies to ensure aggressive, yet safe, operation. In this paper, a framework for terrain characterization and identification is briefly described, composed of 1) vision-based classification of upcoming terrain, 2) terrain parameter identification via wheel-terrain interaction analysis, and 3) terrain classification based on auditory wheel-terrain contact signatures. The parameter identification algorithm is presented in detail. The algorithm derives from simplified forms of classical terramechanics equations. An on-line estimator is developed to allow rapid identification of critical terrain parameters. Simulation and experimental results show that the terrain estimation algorithm can accurately and efficiently identify key terrain parameters for sand.

During the last decade, there has been significant progress toward a supervised autonomous robotic capability for remotely controlled scientific exploration of planetary surfaces. While planetary exploration potentially encompasses many elements ranging from orbital remote sensing to subsurface drilling, the surface robotics element is particularly important to advancing in situ science objectives. Surface activities include a direct characterization of geology, mineralogy, atmosphere and other descriptors of current and historical planetary processes-and ultimately-the return of pristine samples to Earth for detailed analysis. Toward these ends, we have conducted a broad program of research on robotic systems for scientific exploration of the Mars surface, with minimal remote intervention. The goal is to enable high productivity semi-autonomous science operations where available mission time is concentrated on robotic operations, rather than up-and-down-link delays. Results of our work include prototypes for landed manipulators, long-ranging science rovers, sampling/sample return mobility systems, and more recently, terrain-adaptive reconfigurable/modular robots and closely cooperating multiple rover systems. The last of these are intended to facilitate deployment of planetary robotic outposts for an eventual human-robot sustained scientific presence. We overview our progress in these related areas of planetary robotics R&D, spanning 1995-to-present.

While significant recent progress has been made in development of mobile robots for planetary suface exploration,there remain major challenges. These include increased autonomy of operation, traverse of challenging terrain, and fault-tolerance under long, unattended periods of use.