Programming paths for robotic welding conventionally requires precise positioning of workpieces, detailed 3D models and/or tedious teach pendant programming. A new method is introduced in this paper that enables an operator to teach the weld path to the robot through a haptic-visual interface. The operator teaches the path by guiding the tool tip to contact on the workpiece surface with force feedback through the haptic device, and drawing exploratory paths that intersect the edge to be welded as well as adjoining surfaces. Tool-tip positions in contact with the workpiece are recorded. A RANSAC-type algorithm is used to automatically estimate a piecewise parametric curve along the edge as well as geometric parameters of the adjoining surfaces. The required tool trajectory for the robot to weld along the workpiece edge is automatically generated. Experiments performed in simulation and on a physical KUKA IIWA7 robot demonstrate that the developed method can successfully detect workpiece edges within a maximum deviation of 1mm. Furthermore, the method is intuitive, and requires no knowledge of robot programming for an operator to program multi-segment weld paths quickly.

We present a new paradigm for performing remote haptic-robot interactive operations. The new paradigm is anchored on an architecture that combines local autonomy with a high-level exchange strategy of reference input. This represents a departure from the conventional reliance on direct exchange of low-level control signals in a global feedback control system. The new approach establishes two local autonomous controllers acting on the robot and the haptic device, interfaced at a higher level via a dual-proxy model. The dual proxy is a passive bridge between the local autonomous controllers. It generates appropriate motion and force reference inputs that are consistent with the task physical interactions and the levels of autonomy. Its model is adjusted online with respect to exchanged position, contact, and environment geometry information. A key component in this methodology is the perception algorithm on the robot side, the force-space particle filter, designed to reliably estimate in real time the environment contact geometry. The series of simulations and physical experimental validations of the approach demonstrate the transparency and high fidelity in haptic-robot interaction and its inherent robustness to communication delays.

Hard coded robotic manipulation skills work well in known, predictable and repeatable situations. Human environments, however, are better described as dynamic, chaotic, uncertain or unstructured. Therefore, plans relying on preprogrammed trajectories are bound to fail in these settings. In order to increase robustness to uncertainty and avoid coding new skills from scratch, we can make flexible plans that execute existing autonomous primitives based on the sensed state of the environment. A key challenge of this approach is finding the sequence of primitives required to perform the desired task. This work uses a variation of a Hidden Markov Model (HMM) with an augmented particle filter to find the primitive sequence using only a reduced number of human demonstrations. The algorithm was tested on 40 demonstrations of two different manipulation tasks involving six primitives. It was seeded with a single manually labelled demonstration of each task and was able to automatically label the other 38 demonstration sequences with an average success of 81.5%. The results show improved convergence and a 9% increase in accuracy over other versions of the algorithm.

Robots have been successfully used in well-structured and deterministic environments, but they are still unable to function in unstructured environments mainly because of missing reliable real-time systems that integrate perception and control. In this paper, we close the loop between perception and control for real-time obstacle avoidance by introducing a new robust perception algorithm and a new collision avoidance strategy, which combines local artificial potential fields with global elastic planning to maintain the convergence towards the goal. We evaluate our new approach in real-world experiments using a Franka Panda robot and show that it is able to robustly avoid dynamic or even partially occluded obstacles while performing position or path following tasks.

In a human-robot coexisting environment, it is pivotal for a mobile service robot to arrive at the goal position safely and efficiently. In this article, an elastic band-based rapidly exploring random tree (EB-RRT) algorithm is proposed to achieve real-time optimal motion planning for the mobile robot in the dynamic environment, which can maintain a homotopy optimal trajectory based on current heuristic trajectory. Inspired by the EB method, we propose a hierarchical framework consisting of two planners. In the global planner, a time-based RRT algorithm is used to generate a feasible heuristic trajectory for a specific task in the dynamic environment. However, this heuristic trajectory is nonoptimal. In the dynamic replanner, the time-based nodes on the heuristic trajectory are updated due to the internal contraction force and the repulsive force from the obstacles. In this way, the heuristic trajectory is optimized continuously, and the final trajectory can be proved to be optimal in the homotopy class of the heuristic trajectory. Simulation experiments reveal that compared with two stateof-the-art algorithms, our proposed method can achieve better performance in dynamic environments.

When performing tasks in uncertain environments and around humans, robots are likely to collide unexpectedly with people or objects. In order to ensure safety, most approaches rely on collision avoidance and try to prevent any contact from happening, which may result in unnecessary interruption of a task that would be feasible in spite of the obstacle. On the one hand, when an unexpected contact occurs, a safe robot behavior is required. On the other hand, it might be interesting to exploit the contact instead of moving away from it. In this paper, we present a contact-driven approach for safe and interactive robot operation to react to unforeseen contact events. This approach offers the possibility to control the contact while minimizing its effects on the robot tasks. It relies exclusively on the robot model and proprioceptive sensors. It is tested in simulation and hardware experiments on a 7 degrees of freedom robot arm and shows a safe contact behavior that does not interfere with the task, and as little as possible with the robot posture requirements.

For robots to solve real world tasks, they often require the ability to reason about both symbolic and geometric knowledge. We present a framework, called KABouM, for integrating knowledge-level task planning and motion planning in a bounding geometry. By representing symbolic information at the knowledge level, we can model incomplete information, sensing actions and information gain; by representing all geometric entities--objects, robots and swept volumes of motions--by sets of convex polyhedra, we can efficiently plan manipulation actions and raise reasoning about geometric predicates, such as collisions, to the symbolic level. At the geometric level, we take advantage of our bounded convex decomposition and swept volume computation with quadratic convergence, and fast collision detection of convex bodies. We evaluate our approach on a wide set of problems using real robots, including tasks with multiple manipulators, sensing and branched plans, and mobile manipulation.

Direct force control of robots is challenging, particularly since the interaction with the environment can render the robot unstable. This paper presents the results of novel approaches for passivity-based stability for a particular direct force control method, namely explicit force control. A step-by-step procedure to passivate and stabilise the control loop is presented and it explains how Time Domain Passivity Approach, a passivity-based tool widely used in teleoperation and haptics has been extended and applied in explicit force control. The electrical circuit and network-port representations derived in the process allows the analytical evaluation of the system and can be applied in other control architectures as well. The stability methods are presented both qualitatively and quantitatively with simulations and hardware experiments. A discussion about the results obtained and the energy behavior is also provided. Results are promising and suggest that these methods can be used for stable and high-bandwidth force control of robotic manipulators.

This paper extends the previously proposed Explicit Force Controller based on Time Domain Passivity Approach. When using the classical passivity observer, we encounter an energy accumulation problem: if the system stays a long time in stable contact, energy is dissipated and the passivity observer builds up a large value. This causes the passivity controller to be triggered late after the interaction becomes unstable, so we lose the advantage of the passivity controller. In order to deal with this energy accumulation issue, we propose two new passivity observers that allow us to quickly detect potential instability despite the accumulated energy. We prove the theoretical validity of these new observers. In addition, we propose a more generalized way of implementing the passivity based explicit force controller on a multi-DoF manipulator, using a model for the robot and environment that includes sensor flexibility, and a hybrid position/force controller in the operational space framework. The proposed method is experimentally tested with KUKA IIWA, and the improved performance is verified.

In this paper, we demonstrate that it is feasible to conduct reliable multi-axis force control motor neuroimaging experiments in humans using a novel five degree-of-freedom Haptic fMRI interface (HFI-5). We demonstrate that HFI-5 supports accurate force and torque control for all its axes. In addition, it minimizes the force-to-torque coupling induced by its closed chain kinematic structure. HFI-5 has low backdrive friction (<; 0.5N; <; 0.05Nm), which improves its haptic transparency. While the device is large and consequently flexible, a linear model can correct kinematic accuracies due to device deflection. Resulting position error measurements are similar in scale to human hand jitter, and are thus satisfactory. To test HFI-5's efficacy, we performed force perception and force discrimination tests with it on four human subjects. The test revealed that humans could readily perceive forces above 1N and could discern forces that were about 33% apart. As such, HFI-5's force range (x:±16.36N; y:±4.62N; z:±6.9N; passively gravity compensated) can support a wide variety of forces that are perceptible and distinguishable. Finally, we performed ten fMRI scans with a human subject who performed a visually guided force-control experiment. Temporal noise patterns in fMRI measurements matched the fMRI baseline (0.8%), which demonstrates that HFI-5's electromagnetic motors do not introduce substantial noise. HFI-5 is thus ready to be used in motor neuroimaging experiments.

This paper presents the contact-consistent elastic strips (CES) framework, a motion planning approach capable of producing complex multi-contact whole-body humanoid behaviors in dynamic environments. Planning multi-contact motions for humanoid robots is known to be non-trivial since it involves aspects of autonomous balancing, obstacle avoidance, ensuring global connectivity of the workspace and concurrent consideration of the kinematic and dynamic constraints. Previous works at motion planning for humanoid systems tend to focus on joint space planning and deal with obstacle avoidance and contact-point searching as the separated problems. CES framework, however, simultaneously considers all these requirements and constraints in task space while planning a valid sequence of contact-points and corresponding motions. This resulted in considerable improvements to efficiency and significantly reduced planning time. With the use of CES framework, complex multi-contact locomotion behaviors and real-time adjustments to the robot motions in 3D unstructured environments is possible. Several simulations are demonstrated to evaluate and verify the performance of CES framework.

For intelligent robots to solve real-world tasks, they need to manipulate multiple objects, and perform diverse manipulation actions apart from rigid transfers, such as pushing and sliding. Planning these tasks requires discrete changes between actions, and continuous, collision-free paths that fulfill action-specific constraints. In this work, we propose a multi-modal path planner, named MOPL, which accepts generic definitions of primitive actions with different types of contact manifolds, and randomly spans its search trees through these subspaces. Our evaluation shows that this generic search technique allows MOPL to solve several challenging scenarios over different types of kinematics and tools with reasonable performance. Furthermore, we demonstrate MOPL by solving and executing plans in two real-world experimental setups.

We present a light, actuated smart staff with 5DOF tip force sensing which can be used by a humanoid robot operating in challenging terrain. The staff has an extension mechanism that employs mechanical multiplexing to achieve a high extension ratio in a stiff and compact package. The tip force sensor uses two metal diaphragms to achieve decoupling of axial and radial forces and the ability to tune the maximum range of forces in each direction independently. With the force sensor, the robot can characterize the coefficient of friction and orientation of a surface with simple motion primitives. Two sets of experiments were conducted with the smart staff manipulated by a 7 DOF robot arm. Using the sensor, the robot was able to determine the orientations of sloped surfaces within two degrees in two orthogonal directions.

We present an efficient method for addressing online the inversion of differential task kinematics for redundant manipulators, in the presence of hard limits on joint space motion that can never be violated. The proposed Saturation in the Null Space (SNS) algorithm proceeds by successively discarding the use of joints that would exceed their motion bounds when using the minimum norm solution. When processing multiple tasks with priority, the SNS method realizes a preemptive strategy by preserving the correct order of priority in spite of the presence of saturations. In the single- and multitask case, the algorithm automatically integrates a least possible task-scaling procedure, when an original task is found to be unfeasible. The optimality properties of the SNS algorithm are analyzed by considering an associated quadratic programming problem. Its solution leads to a variant of the algorithm, which guarantees optimality even when the basic SNS algorithm does not. Numerically efficient versions of these algorithms are proposed. Their performance allows real-time control of robots executing many prioritized tasks with a large number of hard bounds. Experimental results are reported.

Maintaining humanoid robot stability in unstructured environments is nontrivial because robots lack humanlike tactile sensing and require complex task-specific controllers to integrate information from multiple sensors. To deploy humanoid robots in cluttered and unstructured environments such as disaster sites, it is necessary to develop advanced techniques in both locomotion and control. This paper proposes to incorporate a pair of actuated smart staffs with vision and force sensing that transforms biped humanoids into tripeds or quadrupeds or more generally, SupraPeds. The concept of SuprePeds not only improves the stability of humanoid robots while traversing rough terrain but also retains the manipulation capabilities. In order to control the potentially numerous contact forces on SupraPeds, we develop a friction-consistent whole-body control framework that implements generic multi-contact control for arbitrary humanoids, which enables autonomous balancing while complying with friction constraints. The simulation results are presented to demonstrate that the proposed control framework can efficiently deal with multi-contact locomotion in 3D unstructured environments.

Generating robot motion trajectories instantaneously in the moment unforeseen sensor events happen is very essential for many real-world robot applications. Using a previous work on online trajectory generation as a basis, this paper proposes an alternative approach that also considers dynamic models. The former class of algorithms does not take into account dynamically changing acceleration capabilities based on maximum actuator forces/torques. This paper extends target velocity-based algorithms of the previous approach by taking into consideration the entire system dynamics when generating trajectories online within one control cycle (typically 1 ms or less). The extension includes the acceleration capabilities of a robot at every discrete time step assuming constant values for the maximum actuator forces/torques, thus allowing the generation of adaptive trajectory profiles during the motion of the robot. Several real-world experimental results using a seven-degree-of-freedom lightweight robot arm underline the relevance of this extension.

This paper presents an adaptive algorithm for checking collisions over any continuous robot motion set when tasks or constraints are given. As robots have begun to operate in human environments, which are unstructured and dynamically changing, the need for on-line robot planning and control strategies has increased. In implementing an on-line system, a fast and reliable collision checking method for continuous paths is a critical element. However, since external objects move unexpectedly, collision checking along the continuous path of a robot's motion suffers from increased uncertainty. Furthermore, computing the desired motion path or trajectory of a complex robotic task is very complex and slow. Therefore, we have developed a new collision checking strategy that can be applied to many types of motions that satisfy many given constraints. Our algorithm defines the applicable robot motions in a constraint-based manner, which is suitable for the multiple-task motion of a complex robot. This method can check the collision for the entire motion by finding the worst case with a small amount of computation, so that we can use the method for on-line applications. Moreover, our algorithm has a feature of adaptive resolution, which provides advantages in dynamically changing environments. The proposed method has been tested on high d.o.f. robots and the experimental results show that the method is suitable for on-line applications of multiple-tasks.

All mammals but humans use whiskers in order to rapidly acquire information about objects in the vicinity of the head. Collisions of the head and objects can be avoided as the contact point is moved from the body surface to the whiskers. Such a behavior is also highly desirable during many robot tasks such as for human-robot interaction. Using novel capacitive proximity sensors, robots sense when they approach a human (or an object) and react before they actually collide with it. We propose a sensor and control concept that mimics the behavior of whiskers by means of capacitive sensors. Major advantages are the absence of physical whiskers, the absence of blind spots and a very short response time. The sensors are flexible and thin so that they feature skin-like properties and can be attached to various robotic link and joint shapes. In comparison to capacitive proximity sensors, the proposed virtual whiskers offer better sensitivity towards small conductive as well as non conductive objects. Equipped with the new proximity sensors, a seven-joint robot for humanrobot interaction tasks shows the efficiency and responsiveness of our concept.

Human-centered robotics has received growing interest in low-impedance actuations. In particular, pneumatic artificial muscles (PAMs) provide compliance and high force-to-weight ratio, which allow for safe actuation. However, several performance drawbacks prevent PAMs from being more pervasive. Although many approaches have been proposed to overcome the low control bandwidth of PAMs, some limitations of PAMs, such as restricted workspace and torque capacity, remain to be addressed. This paper analyzes the characteristics and limitations of PAMs-driven joints and subsequently provides an optimization strategy for circular pulleys (CPs) in order to improve joint torque capacity over a large workspace. In addition to CPs, this paper proposes a design methodology to synthesize a pair of variable radius pulleys (VRPs) for further improvement. Simulation and experimental results show that newly synthesized VRPs significantly improve torque capacity in the enlarged workspace without loss of dynamic performance. Finally, the characteristics of CPs and VRPs are discussed in terms of physical human-robot interaction.

The paper investigates the dynamic characteristics that shape human skills using the task-space methods found in robotics research. It is driven by the hypothesis that each subject's physiology can be reflected to the task dynamics using the operational space acceleration characteristics and that elite performers achieve the optimum transmission from their available muscle induced torque capacity to the desired task in goal oriented dynamic skills. The methodology is presented along with the full body human musculoskeletal model used for the task-based analyzes. The robotics approach for human motion characterization is demonstrated in the biomechanical analysis of an elite golf swing. This approach allows us to trace the acceleration capacities in a given subject's task space. The results of the motion characterization show that humans in fact follow a path of trajectory in line with the maximum available operational space accelerations benefiting from their physiology shaped by the combination of the force generating capacities of the muscles as well as by the joint and limb mechanics.

In this paper a real-time collision avoidance approach is presented for safe human-robot coexistence. The main contribution is a fast method to evaluate distances between the robot and possibly moving obstacles (including humans), based on the concept of depth space. The distances are used to generate repulsive vectors that are used to control the robot while executing a generic motion task. The repulsive vectors can also take advantage of an estimation of the obstacle velocity. In order to preserve the execution of a Cartesian task with a redundant manipulator, a simple collision avoidance algorithm has been implemented where different reaction behaviors are set up for the end-effector and for other control points along the robot structure. The complete collision avoidance framework, from perception of the environment to joint-level robot control, is presented for a 7-dof KUKA Light-Weight-Robot IV using the Microsoft Kinect sensor. Experimental results are reported for dynamic environments with obstacles and a human.

We present a novel efficient method addressing the inverse differential kinematics problem for redundant manipulators in the presence of different hard bounds (joint range, velocity, and acceleration limits) on the joint space motion. The proposed SNS (Saturation in the Null Space) iterative algorithm proceeds by successively discarding the use of joints that would exceed their motion bounds when using the minimum norm solution and reintroducing them at a saturated level by means of a projection in a suitable null space. The method is first defined at the velocity level and then moved to the acceleration level, so as to avoid joint velocity discontinuities due to the switching of saturated joints. Moreover, the algorithm includes an optimal task scaling in case the desired task trajectory is unfeasible under the given joint bounds. We also propose the integration of obstacle avoidance in the Cartesian space by properly modifying on line the joint bounds. Simulation and experimental results reported for the 7-dof lightweight KUKA LWR IV robot illustrate the properties and computational efficiency of the method.

We present an efficient method for motion control of redundant robots performing multiple prioritized tasks in the presence of hard bounds on joint range, velocity, and acceleration/ torque. This is an extension of our recently proposed SNS (Saturation in the Null Space) algorithm developed for single tasks. The method is defined at the level of acceleration commands and proceeds by successively discarding one at a time the commands that would exceed their bounds for a task of given priority, and reintroducing them at their saturated levels by projection in the null space of a suitable Jacobian associated to the already considered tasks. When processing all tasks in their priority order, a correct preemptive strategy is realized in this way, i.e., a task of higher priority uses in the best way the feasible robot capabilities it needs, while lower priority tasks are accommodated with the residual capability and do not interfere with the execution of higher priority tasks. The algorithm automatically integrates a multi-task least possible scaling strategy, when some ordered set of original tasks is found to be unfeasible. Simulation and experimental results on a 7-dof lightweight KUKA LWR IV robot illustrate the good performance of the method.

For robots to operate in human environments, they are required to react safely to unexpected changes in the work area. However, existing manipulation task planning methods take more than several seconds or minutes to update their solutions when environmental changes are recognized. Furthermore, the computation time exponentially increases in case of highly complex structures such as humanoid robots. Therefore, we propose a reactive system for high d.o.f. robots to perform interactive manipulation tasks under real-time conditions. The paper describes the implementation of the Elastic Strip Framework, a plan modification approach to update initial motion plans. To improve its real-time performance and reliability, the previous geometric approximation is replaced by an implicit method that constructs an elastic tunnel for collision checking. Additionally, in order to maintain a robust system even in exceptional situations, such as undetected obstacles, the force transformer module executes compliant motions, and the current elastic strip adapts the path tracking motion by monitoring tracking errors of the actual motion. The proposed system is applied to a Honda humanoid robot. Real-time performance is successfully demonstrated in real-world experiments.

In this paper, we present a novel grasp selection algorithm to enable a robot with a two-fingered end-effector to autonomously grasp unknown objects. Our approach requires as input only the raw depth data obtained from a single frame of a 3D sensor. Additionally, our approach uses no explicit models of the objects and does not require a training phase. We use the grasping capability to demonstrate the application of a robot as an autonomous checkout clerk. To perform this task, the robot must identify how to grasp an object, locate the barcode on the object and read the numeric code. We evaluate our grasping algorithm in experiments where the robot was required to autonomously grasp unknown objects. The robot achieved a success of 91.6%in grasping novel objects. We performed two sets of experiments to evaluate the checkout robot application. In the first set, the objects were placed in many orientations in front of the robot one at a time. In the second set, the objects were placed several at a time with varying amounts of clutter. The robot was able to autonomously grasp and scan the objects in 49/50 of the single-object trials and 46/50 of the cluttered trials.

A new generation of robots is being designed for human occupied workspaces where safety is of great concern. This research demonstrates the use of a capacitive skin sensor for collision detection. Tests demonstrate that the sensor reduces impact forces and can detect and characterize collision events, providing information that may be used in the future for force reduction behaviors. Various parameters that affect collision severity, including interface friction, interface stiffness, end tip velocity and joint stiffness irrespective of controller bandwidth are also explored using the sensor to provide information about the contact force at the site of impact. Joint stiffness is made independent of controller bandwidth limitations using passive torsional springs of various stiffnesses. Results indicate a positive correlation between peak impact force and joint stiffness, skin friction and interface stiffness, with implications for future skin and robot link designs and post-collision behaviors.

Whole-body operational space control is a powerful compliant control approach for robots that physically interact with their environment. The underlying mathematical and algorithmic principles have been laid in a large body of published work, and novel research keeps advancing its formulation and variations. However the lack of a reusable and robust shared implementation has hindered its widespread adoption.

Joint stiffness plays an important role in both safety and control performance, particularly in human-friendly robots using artificial pneumatic muscles. Due to the limited control bandwidth of pneumatic muscles, stiffness characteristics and their effects on safety in the frequency domain should be taken into account. This paper introduces the concept of instantaneous stiffness and validates its model with the Stanford Safety Robot (S2ρ. The potential effects of instantaneous stiffness on safety is explored through experimental comparison of peak impact accelerations under various impact conditions. Instantaneous stiffness demonstrates different effects on the impact acceleration depending on impact velocity and controller gain. Finally, the paper discusses the stiffness characteristics as a guideline for design and control to improve the robot safety while maintaining the control performance.

There is a growing interest in utilizing pneumatic artificial muscles (PAMs) as actuators for human-friendly robots. However, several performance drawbacks prevent the widespread use of PAMs. Although many approaches have been proposed to overcome the low control bandwidth of PAMs, some limitations of PAMs such as restricted workspace and torque capacity remain to be addressed. This paper analyzes the limitations of conventional circular pulley joints and subsequently proposes a design methodology to synthesize a pair of variable radius pulleys to improve joint torque capacity over a large workspace. Experimental results show that newly synthesized variable radius pulleys significantly improve position tracking performance in the enlarged workspace.

Humans are capable of manipulating objects based solely on the sense of touch. For robots to achieve the same feat in unstructured environments, global localization of objects via touch is required. Bayesian approaches provide the means to cope with uncertainties of the real world, but the estimation of the Bayesian posterior for the full six degrees of freedom (6-DOF) global localization problem is computationally prohibitive. We propose an efficient Bayesian approach termed Scaling Series. It is capable of solving the full problem reliably in real time. This is a Monte Carlo approach that performs a series of successive refinements coupled with annealing. We also propose an analytical measurement model, which can be computed efficiently at run time for any object represented as a polygonal mesh. Extensive empirical evaluation shows that Scaling Series drastically outperforms prior approaches. We demonstrate general applicability of the approach on five common solid objects, which are rigidly fixed during the experiments. We also consider 6-DOF localization and tracking of free-standing objects that can move during tactile exploration.

A formidable challenge in the development of human-friendly robots is to simultaneously achieve desired levels of performance and safety. To address this issue, a hybrid actuation concept has been proposed, combining large, low impedance actuators and small, high-frequency actuators. However, the determination of design parameters remains a challenge, as stiffness and electrical motor torque capacity simultaneously affect both the control performance and the safety of the manipulator. Using analytical models of the hybrid actuation system, we propose a methodology to achieve a combination of low impedance and high control bandwidth. The optimized parameters are verified and compared with previous ones through simulation and experimentation.

This paper presents a new methodology for the analysis and control of internal forces and center-of-mass (CoM) behavior, which are produced during multicontact interactions between humanoid robots and the environment. The approach leverages the virtual-linkage model that provides a physical representation of the internal and CoM resultant forces with respect to reaction forces on the supporting surfaces. A grasp/contact matrix describing the complex interactions between contact forces and CoM behavior is developed. Based on this model, a new torque-based approach for the control of internal forces is suggested and illustrated on the Asimo humanoid robot. The new controller is integrated into the framework for whole-body-prioritized multitasking, thus enabling the unified control of CoM maneuvers, operational tasks, and internal-force behavior. The grasp/contact matrix is also proposed to analyze and plan internal force and CoM control policies that comply with frictional properties of the links in contact.

Determining design parameters is often a challenging procedure, especially in human-friendly robot design due to competition between robot safety and performance. Presenting an analytical model of hybrid actuation for human-friendly robot development, this paper proposes design methodologies to improve performance factors such as range of motion, payload, and acceleration while maintaining the safety factor of effective inertia. The optimized parameters for various design requirements have been provided for 1DOF and 2DOF applications. Comparison between current design parameters and the optimized parameters for a current platform shows the performance improvement. In future work this research will be extended to systems with higher degrees of freedom.

This paper presents a methodology for the modeling and control of internal forces and moments produced during multi-contact interactions between humanoid robots and the environment. The approach is based on the virtual linkage model which provides a physical representation of the internal forces and moments acting between the various contacts. The forces acting at the contacts are decomposed into internal and resulting forces and the latter are represented at the robot's center of mass. A grasp/contact matrix describing the complex interactions between contact forces and center of mass behavior is developed. Based on this model, a new torque-based approach for the control of internal forces is suggested and illustrated on the Asimo humanoid robot. The new controller is integrated into the framework for whole-body prioritized multitasking enabling the unified control of operational tasks, postures, and internal forces.

This paper presents a new control architecture for compliant motion control and safe physical interaction between humanoid robot and human. One of the key technologies in this framework is the torque transformer, which enables the implementation of joint torque control on the traditional joint position controlled robots. In this framework, the torque control is accomplished by converting desired joint torque command into instantaneous increments of joint velocity command. Through the transformer, the Operational Space Formulation was applied to account for the dynamics of the system on the current joint position controlled robots. This approach was experimentally implemented on the physical humanoid robot, HONDA ASIMO's upper body control. The ZMP based stable balance controller of ASIMO was integrated to control the lower body of the robot. In this framework, dynamics control by the torque transformer and stable position based balance controller were connected and coordinated together on the current position controlled humanoid robot. The paper presents modeling process of the torque transformer, whole body controller and the results of the implementation which demonstrate the effectiveness of this approach.

The control approaches based on the task function formalism, and particularly those structured as a prioritized hierarchy of tasks, enable complex behaviors with elegant properties of robustness and portability to be built. However, it is difficult to consider a straightforward integration of tasks described by unilateral constraints in such frameworks. Indeed, unilateral constraints exhibit irregularities that prevent the insertion of unilateral tasks at any priority level, other than the lowest, of a hierarchy. In this paper, we present an original method to generalize the hierarchy-based control schemes to account for unilateral constraints at any priority level. We develop our method first for task sequencing using only the kinematics description; then, we expand it to the task description, using the operational space formulation. The method applies in robotics and computer graphics animation. Its practical implementation is exemplified by realizing a real-manipulator visual servoing task and a humanoid avatar reaching task; both experiments are achieved under the unilateral constraints of joint limits.

This paper presents a new approach for estimating physical properties of deformable models from experimental measurements. In contrast to most previous work, we introduce a new method based on particle filters which identifies the different stiffness properties for spring-based models. This approach addresses some important limitations encountered with gradient descent techniques which often converge towards ill solutions or remain fixed in local minima conditions.

Joint position control is a dominant paradigm in industrial robots. While it has been successful in various industrial tasks, joint position control is severely limited in performing advanced robotic tasks, especially in unstructured dynamic environments. This paper presents the concept of torque-to-position transformer designed to allow the implementation of joint torque control techniques on joint position-controlled robots. Robot torque control is essentially accomplished by converting desired joint torques into instantaneous increments of joint position inputs. For each joint, the transformer is based on the knowledge of the joint position servo controller and the closed-loop frequency response of that joint. This transformer can be implemented as a software unit and applied to any conventional position-controlled robot so that torque command to the robot becomes available. This approach has been experimentally implemented on the Honda ASIMO robot arm. The paper presents the results of this implementation which demonstrate the effectiveness of this approach.

The control approaches based on tasks, and particularly based on a hierarchy of tasks, enable to build complex behaviors with some nice properties of robustness and portability. However it is difficult to consider directly unilateral constraints in such a framework. Unilateral constraints presents some strong irregularities (in particular at the level of their derivative) that prevents the insertion of unilateral-based tasks at the high-priority level of a hierarchy. In this paper, we present an original method to generalize the hierarchy-based control schemes to take unilateral constraint into account at the top-priority level. We develop our method first at the kinematic level then directly at the dynamic level using the operational space. The method is then validated on a various set of robots by realizing a visual servoing under the constraint of joint limits.

Safety is a critical characteristic for robots designed to operate in human environments. This paper presents the concept of hybrid actuation for the development of human-friendly robotic systems. The new design employs inherently safe pneumatic artificial muscles augmented with small electrical actuators, human-bone-inspired robotic links, and newly designed distributed compact pressure regulators. The modularization and integration of the robot components enable low complexity in the design and assembly. The hybrid actuation concept has been validated on a two-degree-of-freedom prototype arm. The experimental results show the significant improvement that can be achieved with hybrid actuation over an actuation system with pneumatic artificial muscles alone. Using the Manipulator Safety Index (MSI), the paper discusses the safety of the new prototype and shows the robot arm safety characteristics to be comparable to those of a human arm.

This paper introduces a new method that enables compliant joint control on a traditional joint position controlled system by using Torque to Position Transformer. In this method, torque control is accomplished by converting desired joint torques into instantaneous increments of joint position command. For each joint, the transformer was modeled based on the identification of the individual motor controller. This framework was experimentally implemented on the Honda ASIMO which is controlled by the traditional position controlled system. Decoupled task dynamics by the Operational Space Control was applied to realize compliant posture control and accurate task control. The paper presents the experimental results of the implementation which demonstrate the effectiveness of this approach. This proposed approach provides higher performance in compliant and safe motion which is required for motion in contact with the environment and human.

Today most robots interact with the surroundings only with their end-effectors. However there are many benefits to utilizing contact along the entire length of robot body and links especially for human-like robots. Existing control strategies for link contact require knowledge of the contact point. In an uncertain environment, locating link contact point is difficult for most robots as they do not possess skin capable of sensing. We propose a probabilistic approach to link contact estimation based on geometric considerations and compliant motions. Since for many robots, link geometry is also uncertain, we broaden our approach to simultaneously estimate link shape and environment contact. Our experimental results demonstrate that efficiency of control is significantly improved by link contact estimation

Recently, on the base of distributed macro-mini actuation approach (DM2), a new robotic manipulator with hybrid actuation, air muscles-DC motor, has been developed. Among existing actuators, the hybrid actuation employs air muscles because they represent an advantageous tradeoff of performance and safety, due to their power/weight ratio and inherent compliance. The air muscles, however, are limited in bandwidth and their behavior is highly nonlinear. In order to overcome these limitations, the paper presents a torque control strategy based on a pair of differentially connected force-controlled air muscles. This controller was implemented and evaluated on a single joint testbed, first by itself and then as macro component into the Macro-Mini control strategy.

This paper presents a framework for the dynamical formulation and control of humanoid systems. In this framework unactuated virtual joints are used to describe the humanoid's configuration with respect to the inertial frame. The dynamics of the system are then formulated in a general manner that considers arbitrary contact with the environment. A control structure is implemented for both motion and contact forces that accounts for under-actuation due to the virtual joints. A strategy is also implemented to address transitions between different contact states. Simulation results are presented that demonstrate this overall framework for many behaviors such as standing, walking, jumping, and hand manipulation with walking

We consider the problem of autonomously estimating position and orientation of an object from tactile data. When initial uncertainty is high, estimation of all six parameters precisely is computationally expensive. We propose an efficient Bayesian approach that is able to estimate all six parameters in both unimodal and multimodal scenarios. The approach is termed scaling series sampling as it estimates the solution region by samples. It performs the search using a series of successive refinements, gradually scaling the precision from low to high. Our approach can be applied to a wide range of manipulation tasks. We demonstrate its portability on two applications: (1) manipulating a box and (2) grasping a door handle

This paper applies a task-level approach to the control of holonomically constrained shoulder models. These models include a biomechanical representation based on human physiology and a robotic design based on a parallel-serial structure. Both models involve complex kinematically coupled motion between the shoulder girdle and the humerus. This coupled motion has a significant impact on the resulting humeral pointing dynamics associated with arm movement. The constrained task-level control approach implemented here characterizes and exploits the kinematically coupled nature of these systems by casting the constrained dynamics into a task-level control framework. Examples are presented which illustrate the effectiveness of this approach

Tomorrow's humanoids will operate in human environments, where efficient manipulation and locomotion skills, and safe contact interactions are critical design factors. We report here our recent efforts into these issues, materialized into a whole-body control framework. This framework integrates task-oriented dynamic control and control prioritization allowing to control multiple task primitives while complying with physical and movement-related constraints. Prioritization establishes a hierarchy between control spaces, assigning top priority to constraint-handling tasks, while projecting operational tasks in the null space of the constraints, and controlling the posture within the residual redundancy. This hierarchy is directly integrated at the kinematic level, allowing the program to monitor behavior feasibility at runtime. In addition, prioritization allows us to characterize the dynamic behavior of the individual control primitives subject to the constraints, and to synthesize operational space controllers at multiple levels. To complete this framework, we have developed free-floating models of the humanoid and incorporate the associated dynamics and the effects of the resulting support contacts into the control hierarchy. As part of a long term collaboration with Honda, we are currently implementing this framework into the humanoid robot Asimo

This paper discusses robotic telemanipulation with Kalman active observers and online stiffness estimation. Operational space techniques, feedback linearization, discrete state space methods, augmented states, and stochastic design are used to control a robotic manipulator with a haptic device. Stiffness estimation only based on force data (measured, desired, and estimated forces) is proposed, avoiding explicit position information. Stability and robustness to stiffness errors are discussed, as well as real-time adaptation techniques. Telepresence is analyzed. Experiments show high performance in contact with soft and hard surfaces.

This paper presents a compliant motion control framework for multiple contacts distributed over multiple links. The one link multi-contact control approach implemented in our previous work has been extended to contacts over multiple links. Experimental results demonstrate three point contact control on two links of a PUMA560 manipulator. A robust force control design is implemented with a Kalman estimator and full state feedback method to compensate for the modeling errors of the manipulator and environment.

This paper presents an operational space control approach for the general class of holonomically constrained multibody systems. As a point of departure, the general formulation of constrained dynamical systems is addressed using multiplier and minimization approaches. The constrained dynamics problem is interpreted with respect to its underlying symmetry with task space dynamics. A framework for constrained operational space control is then presented which casts the general formulation of constrained multibody systems into a task space setting. This provides a means of exploiting natural task-level control structures within the constrained environment. A set of examples illustrate this control implementation.

The possibility of controlling humanoid robots in free-space opens new fields of application involving free-floating behaviors. Recently, we presented a prioritized task-oriented control framework for the control of multiple motion primitives while complying with physical constraints imposed by the robot’s body and environment. We adapt here this framework to the control of free-floating robots.

This paper discusses the design of a telemanipulation system for haptic telepresence using Kalman active observers (AOBs). A robotic manipulator is controlled by the human operator through a haptic device. Free space, contact and impact experiments are presented, highlighting the capabilities of compliant motion control with AOBs. Telepresence and stability are analyzed taking into account the control design, the system stiffness and a spring-damper-mass model of the human arm. Haptic manipulation experiments on soft and hard surfaces are presented.

This paper presents a novel two-layer fuzzy-navigator that incorporates sensing, control and planning to guide real-time self-reaction of a mobile robot in FMC/FMS. It can identify and solve the local minima problem during the robot's movement, which mimic the way that a human might understand his trapped state by recollecting some of similar experiences he had experienced before. The first-layer-controller does the regular navigation, while the remembrance is provided by the second-layer local-minima-solver, which can also analyze the information of sensor readings, impart an understanding of the robot's local environment and correlate the same with human heuristic experiences of a similar environment. The robot's local environment is identified in terms of fuzzy-rule, sensor- information and landmark-weight-vector. When the robot reaches a dangerous landmark-weigh-value during movement, it understands its entanglement in a loop and takes suitable actions to pull the robot out of its trap. The simulation results prove the validity of the proposed method.

Dynamic capability equations (DCE) provide a new description of robot acceleration and force capabilities. These refer to a manipulator's ability to accelerate its end-effector and to apply forces to the environment at the end-effector. The key features in the development of these equations are that they combine the analysis of end-effector accelerations, velocities, and forces, while addressing the difference in units between translational and rotational quantities. The equations describe the magnitudes of translational and rotational acceleration and force guaranteed to be achievable in every direction, from a particular configuration, given the limitations on the manipulator's motor torques. They also describe the effect of velocities on these capabilities contributed by the Coriolis and centrifugal forces, as well as the reduction of actuator torque capacity due to motor speed. This article focuses on nonredundant manipulators with as many actuators as degrees of freedom.

The paper describes the formulation of multi-contact compliant motion control. It extends our previous work to non-rigid environments. The contact forces are controlled through active observers (AOB), based on the Kalman filter theory. Noise characteristics enter in the control design and are estimated on-line. Experimental results are provided.

Many successful robotic manipulator designs have been introduced. However, there remains the challenge of designing a manipulator that possesses the inherent safety characteristics necessary for human-centered robotics. In this paper, we present a new actuation approach that has the requisite characteristics for inherent safety while maintaining the performance expected of modern designs. By drastically reducing the effective impedance of the manipulator while maintaining high frequency torque capability, we show that the competing design requirements of performance and safety can be successfully integrated into a single manipulation system.

Mass-spring systems have widely and effectively been used for modeling in real-time deformable objects. Easier to implement and faster than finite elements, these systems, on the other side, suffer from several drawbacks when coming to render physically believable behaviors. Neither isotropic or anisotropic materials can be controlled easily and the large number of springs and mass points composing the model makes it fastidious to define parameters to control elongation, flexion and torsion at a macroscopic level. Another weakness is that most of the materials found in nature maintain a constant or quasi-constant volume during deformations; unfortunately, mass-spring models do not have this property. In this paper, we extend the current state-of-the-art in soft tissue simulation by introducing a six-degree of freedom macroscopic elastic sphere described by mass, inertia and volumetric properties. Spheres are placed along the medial axis transform of the object whose centers are connected by a skeleton composed of a set of three-dimensional elastic links. Spheres represent internal mass, volume and control the global deformation of the object. The surface is modeled by setting point masses on the mesh nodes and damped springs on the mesh edges. These nodes are connected to the skeleton by individual elastic links, which control volume conservation and transfer forces between the surface and volumetric model. Using this framework we also present an efficient method to approximate collision detection between multiple bodies in real-time.

This article presents a performance measure, the actuation efficiency, which describes the imbalance between the end-effector accelerations achievable in different directions of non-redundant robotic manipulators. A key feature of the proposed measure is that in its development the unitary differences between linear and angular accelerations are treated in a physically meaningful manner. The measure also indicates oversized actuators, since this contributes to the imbalance in achievable accelerations. The development of this measure is based on the formulation of the Dynamic Capability Hypersurface. The shape of this hypersurface is a weak indicator of the level of imbalance in achievable end-effector accelerations.

The paper discusses compliant motion control using Active Observers (AOBs) applied in robotic manipulators. Stochastic estimation strategies for haptic manipulation are introduced. Stability and robustness analysis is made as a function of stiffness mismatches. Real time adaptation is discussed.

Applications in mobile manipulation require sophisticated motion execution skills to address issues like redundancy resolution, reactive obstacle avoidance, and transitioning between different motion behaviors. The elastic strip framework is an approach to reactive motion generation providing an integrated solution to these problems. Novel techniques within the elastic strip framework are presented, allowing task-consistent obstacle avoidance and task-consistent motion behavior. General transition criteria and methods are presented, permitting the suspension and resumption of task execution to ensure other desired motion behavior, such as obstacle avoidance. Task execution has to be suspended when kinematic constraints or changes in the environment render task-consistent motion behavior infeasible. Task execution is resumed as soon as it is consistent with other desired motion behaviors.

The Operational Space Formulation provides a framework for the analysis and control of manipulator systems with respect to the behavior of their end-effectors. Its application to aircraft canopy polishing is shown using a mobile manipulator. The mobile manipulator end-effector maintains a desired force normal to the canopy surface of unknown geometry in doing a compliant polishing motion, while, at the same time, its mobile base moves around the shop floor, effectively increasing the mobile manipulator's workspace. The mobile manipulator consists of a PUMA 560 mounted on top of a Nomad XR4000. Implementation issues are discussed and simultaneous motion and force regulation results are shown.

This article presents two methods for selecting actuators based on the dynamic loading criteria, which yield a manipulator with a desired level of dynamic performance. Here, dynamic performance is measured in terms of a robot's acceleration and force capabilities, which describe its ability to accelerate the end-effector and to apply forces to the environment, given the limitations on its actuator torques. The dynamic capability equations are used to model the relationship between actuator torque capacities and the acceleration and force capabilities, because they treat linear and angular quantities in a consistent and physically meaningful way. This article discusses actuator selection for a single configuration, as well as for multiple configurations.

We address the problem of applying reactive navigation methods for collision avoidance to systems where the dynamics cannot be neglected: mobile robots with slow dynamic capabilities, or systems working at high speeds. Rather than embedding the motion constraints when designing a navigation method, we propose to introduce the robot dynamic constraints directly into the spatial representation. In this space the dynamic capabilities of the robot are implicitly represented. With minor modifications, standard reactive navigation methods can be used in this space implicitly taking into account the robot dynamic constraints. To validate this framework, we show experimental results using two reactive navigation methods whose original formulation do not take the robot dynamic constraints into account (the nearness diagram navigation and the potential field method).

Concerns telerobotic microsurgery. Existing systems offer tremor elimination and scaling of motions. This article goes beyond the strict master/slave scheme to the enhancement of the operator's capabilities during teleoperation. It is focused on the implementation of three types of constraints for the operators' movements: constrained movement (along a curve or on a predefined surface); virtual obstacle avoidance; and geometric constraints to limit the robots workspace. Constraints for the operator's movements can be implemented mechanically. Our approach uses a haptic master robot, and consists in adding constraint forces to its control scheme. Constraint forces are computed according to attractive or repulsive potential fields placed around constraints. This article presents the principles of these control enhancements which, we believe, will raise dramatically the level of safety and precision that a surgeon can achieve, but those principles can also be applied to a wide variety of teleoperation applications.

Presents a performance measure, the actuation efficiency, which describes isotropy in acceleration capability for non-redundant manipulators. It measures the imbalance between the end-effector accelerations achievable in different directions. Prior to this, no measure of this characteristic was adequate for a six degree-of-freedom manipulator because its end-effector motions are referenced to a mix of linear and angular coordinates. The proposed measure addresses both linear and angular accelerations. It also indicates oversized actuators, since this contributes to the imbalance in achievable accelerations. The development of this measure is based on the formulation of the motion isotropy hypersurface. The shape of this hypersurface is a weak indicator a acceleration isotropy.

Real-time replanning is a prerequisite for motion execution in unpredictably changing environments. This paper presents a framework that allows real-time replanning in high-dimensional configuration spaces. Initially, a planning operation generates a path. The path is augmented by a set of paths homotopic to it. This set is represented implicitly by a volume of free space in the work space. Effectively, this corresponds to delaying part of the planning operation for the homotopic paths until motion execution. During execution reactive control algorithms are used to select a valid path from the set of homotopic paths, using proximity to the environment as a simple and effective heuristic and thereby significantly pruning the search in the configuration space. Experimental results are presented to validate the real-time performance of this framework in high-dimensional configuration spaces.

This paper uses Gauss' principle of least constraint to derive the "natural" dynamic equations for redundant manipulators. This approach is the fastest way to the result that the operational space inertia matrix of the manipulator is the natural weighting matrix for the projection used in solving the redundancy problem. Force-controlled robots form a special case of redundant robots, such that the results can be applied straightforwardly to solve the long-standing problem of the "non-invariance" of the selection matrices in the hybrid force/position control paradigm.

The augmented object model provided the basis for effective cooperation between multiple robots. These robots were assumed to have a single serial-chain structure. In this paper we discuss the augmented object model in the context of mechanisms involving multiple branches such as humanoid robots and parallel mechanisms. An application of the proposed model in the dynamic modeling of a holonomic mobile base and experimental results using real-time dynamic simulation are presented to illustrate the effectiveness of the proposed approach.

This paper discusses intuitive and efficient ways to model and control the dynamics of highly redundant branching mechanisms using the operational space formulation. As the complexity of mechanisms increases, their modeling and control become increasingly difficult. The operational space formulation provides a natural framework for these problems since its basic structure provides dynamic decoupling among multiple tasks and posture behaviors. Efficient recursive algorithms are presented for the computation of the operational space dynamics of branching mechanisms with multiple operational points. The application of these algorithms results in a significant increase in the interactivity and usability of dynamic control of complex branching mechanisms. The experimental results are presented using real-time dynamic simulation.

Presents a general framework for the dynamic simulation and haptic exploration of complex virtual environments. This work builds on previous developments in simulation, haptics and operational space control. The relations between the dynamic models used in simulation and the models originally developed for robotic control are also presented. This framework has been used to develop a simulator that can model complex interactions between generalized articulated mechanical systems and permits direct "hands-on" interaction with the virtual environment through a haptic interface.

Many applications in mobile robotics require the safe execution of a collision-free motion to a goal position. Planning approaches are well suited for achieving a goal position in known static environments, while real-time obstacle avoidance methods allow reactive motion behavior in dynamic and unknown environments. This paper proposes the global dynamic window approach as a generalization of the dynamic window approach. It combines methods from motion planning and real-time obstacle avoidance to result in a framework that allows robust execution of high-velocity, goal-directed reactive motion for a mobile robot in unknown and dynamic environments. The global dynamic window approach is applicable to nonholonomic and holonomic mobile robots.

Presents a general first-order kinematic model of frictionless rigid-body contact for use in hybrid force/motion control. It is formulated in an invariant manner by treating motion and force vectors as members of two separate but dual vector spaces. These more general kinematics allow us to model tasks that cannot be described using the Raibert-Craig model; a single Cartesian frame in which directions are either force- or motion-controlled is not sufficient. The model can be integrated with the object and manipulator dynamics in order to model both the kinematics and dynamics of contact. These equations of motion can be used to design force and motion controllers in the appropriate subspaces. To guarantee decoupling between the controllers, it is possible to apply projection matrices to the controller outputs that depend solely on the kinematic model of contact, not a dynamic one. Experimental results show a manipulation that involves controlling the force in two separate face-vertex contacts while performing motion. These multi-contact compliant motions often occur as part of an assembly and cannot be described using the Raibert-Craig model.

This paper describes the implementation of a robot control architecture designed to combine a manipulation task design environment with a motion controller that uses the operational space formulation to define and implement arm trajectories and object manipulation. The ProVAR desktop manipulation system is an assistive robot for individuals with a severe physical disability, such as quadriplegia as a result of a high-level spinal cord injury. ProVAR allows non-technical operators access to the robot's capabilities through a direct-manipulation simulation/preview user interface. The novel interface concept is based on two built-in characters to play the roles of helpful consultant and down-to-earth robot arm. This team-based interface concept was chosen to maximize user performance and comfort in controlling the inherently complex mechatronic technology. This paper describes our design decisions and rationale.

This paper describes an efficient recursive algorithm for the computation of the extended operational space inertia matrix of an n-link branching (tree-like) redundant robotic mechanism with multiple operational points. The proposed algorithm behaves linearly with respect to n in practice. Therefore, as the number of links increases, this algorithm performs significantly better than the existing O(n/sup 3/) symbolic method. The experimental results of this algorithm are presented using real-time dynamic simulation.

In many robotic applications motions must be executed robustly in dynamic and partially unknown environments. Despite this requirement most motion planning algorithms assume the environment to be known and changes to be predictable. The planning problem in dynamic environments can be decomposed into a planning and an execution phase. In this paper we describe a new framework for the execution of motion plans for robots with many degrees of freedom in dynamic environments. An initial valid trajectory is incrementally modified according to changes in the environment to maintain a collision free path. This framework achieves real-time performance for robots with many degrees of freedom. It is particularly well suited for redundant systems and mobile manipulation, since it allows motion specification of a subset of the degrees of freedom of the robot, greatly simplifying the task of robot programming.

The study of acceleration capability is concerned with the responsiveness of a manipulator to controller commands. We present a general model for the analysis of end-effector linear and angular accelerations that accounts for the velocity effects. The separate treatment of linear and angular motion directly addresses the inhomogeneities of end-effector motions, avoiding the use of indeterminate scaling factors. The velocity effects considered are the Coriolis and centrifugal forces, as well as the relationships associated with actuator's speed-torque performance curves. This study results in a characterization referred to as the "motion isotropy hypersurface" which describes the relationships between isotropic end-effector linear and angular velocities and accelerations. The utility of this surface and its associated information is demonstrated in a design application involving the PUMA 560 manipulator.

This article investigates the problem of redundant manipulator design for optimal dynamic performance as applied to the design of macro/mini structures. The dynamic performance of a manipulator is characterized by the inertial and acceleration properties of the end-effector. However, for redundant manipulators the characteristics of motions in the end effector null space must also be considered. This article presents a methodology for analyzing the performance requirements for the null space motions. The analysis results in a decomposition of the overall design problem into a set of smaller subproblems. Optimization techniques are then used to determine the design parameters which improve manipulator dynamic performance. The decomposition greatly reduces the search space of the overall optimization. Here this methodology is presented along with the models and measures upon which it is based. The approach is illustrated in. The selection of design parameters for a simple six-degree-of-freedom planar mechanism.

We present a haptic rendering framework that allows the tactile display of complex virtual environments. This framework allows surface constraints, surface shading, friction, texture and other effects to be modeled solely by updating the position of a representative object, the "virtual proxy". This abstraction reduces the task of the haptic servo control loop to the minimization of the error between user's position and that of the proxy. This framework has been implemented in a system that is able to haptically render virtual environments of a complexity that is near and often in excess of the capabilities of current interactive graphic systems.

Force feedback coupled with visual display allows people to interact intuitively with complex virtual environments. For this synergy of haptics and graphics to flourish, however, haptic systems must be capable of modeling environments with the same richness, complexity and interactivity that can be found in existing graphic systems. To help meet this challenge, we have developed a haptic rendering system that allows for the efficient tactile display of graphical information. The system uses a common high-level framework to model contact constraints, surface shading, friction and texture. The multilevel control system also helps ensure that the haptic device will remain stable even as the limits of the renderer's capabilities are reached.

Investigates the problem of manipulator design for increased dynamic performance. Optimization techniques are used to determine the design parameters which improve manipulator performance. The dynamic performance of a manipulator is characterized by the inertial and acceleration properties of the end-effector. Our study of inertial and acceleration properties have provided separate descriptions of the characteristics associated with linear and angular motions. This allows a more physically meaningful interpretation of these properties and provides simple models for their analysis. The article presents these models, discusses the design optimization criteria, and formulates the optimization problem. The approach is illustrated in the selection of design parameters of a parallel mechanism.

Mobile manipulation capabilities are key to many new applications of robotics in space, underwater construction, and service environments. This article discusses the ongoing effort at Stanford University for the development of multiple mobile manipulation systems and presents the basic models and methodologies for their analysis and control. This work builds on four methodologies we have previously developed for fixed-base manipulation: the operational space formulation for task-oriented robot motion and force control; the dextrous dynamic coordination of macro/mini structures for increased mechanical bandwidth of robot systems; the augmented object model for the manipulation of objects in a robot system with multiple arms; and the virtual linkage model for the characterization and control of internal forces in a multi-arm system. We present the extension of these methodologies to mobile manipulation systems and propose a new decentralized control structure for cooperative tasks. The article also discusses experimental results obtained with two holonomic mobile manipulation platforms we have designed and constructed at Stanford University.

This paper extends the operational space formulation to the important class of serial-to-parallel chain (branching) manipulators. The various models and concepts developed in operational space, such as dynamically consistent force/torque decomposition for control of redundant manipulators and the augmented object model for cooperative manipulator systems, are shown to extend directly. Dynamic modeling and experimental results for a free-flying, two-arm space robot are presented to validate this extension.

The study of the acceleration properties at the end effector is important in the analysis, design, and control of robot manipulators. In previous efforts aimed at addressing this problem, the end-effector acceleration has been treated as a vector combining both the linear and angular accelerations. The methodology presented in this article provides characterizations of these two different types of accelerations and describes the relationship between them. This work is an extension of our previous studies on manipulator inertial and acceleration properties. The treatment relies on the ellipsoid expansion model, a simple geometric approach to efficiently analyze end-effector accelerations. Results of the application of this analysis to the PUMA 560 manipulator are discussed.

This paper presents a general strategy for manipulator control at kinematic singularities. When a manipulator is in the neighborhood of singular configurations, it is treated as a redundant mechanism in the subspace orthogonal to the singular directions of the end-effector. Control in this subspace is based on operational forces, while null space joint torques are used to deal with the control in the singular directions. Decoupled behavior is guaranteed by using the dynamically consistent force/torque relationship. Two different types of kinematic singularities are identified and strategies dealing with these singularities are developed. Experimental results of the implementation of this approach on a PUMA 560 manipulator are presented.

Dynamic decoupling, motion and force control of manipulators rely on the ability of the actuation system to provide accurate joint torques. However, this ability is considerably restricted by the nonlinearities and friction inherent in the actuator-transmission systems of most industrial robots. This paper is concerned with the development of high-performance torque controlled joints and focuses on two basic issues: sensor design and torque control. The first part of the paper describes a conceptually new type of torque sensor that uses contactless inductive transducers. The new sensor provides a substantial increase in accuracy over conventional strain gauge sensors, achieves higher mechanical robustness, and presents lower sensitivity to electrical noise. The second part of the paper presents an analysis of the effect that the manipulator's transmission and structural properties have on the joint torque controller design. Two manipulators with very different mechanical characteristics are used in this analysis: the PUMA 560 manipulator and Artisan, an eleven-degree-of-freedom manipulator currently under development at Stanford. The experimental results obtained with a prototype link of Artisan are presented and compared to those previously obtained with the PUMA.>

A dynamic simulation package, which can accurately model the interactions between robots and their environment, has been developed. This package creates a virtual environment where various controllers and workcells may be tested. The simulator is divided in two parts: local objects that compute their own dynamic equations of motion, and a global coordinator that resolves interactive forces between objects. This simulator builds upon previous work on dynamic simulation of simple rigid bodies and extends it to correctly model and efficiently compute the dynamics of multi-link robots.>

The authors note a need to overcome the deficiencies inherent in conventional manipulator mechanisms. Joint torque controllability, optimal dynamic characteristics, motion redundancy, and fine manipulation ability are among the basic characteristics that would be desirable attributes of advanced robot systems. The paper discusses the limitations of current robot technology and describes the ongoing effort at Stanford University for the development of high-performance force-controlled robot systems to provide the advanced capabilities needed for carrying out dextrous manipulation tasks.>

The design of a joint torque sensor for a PUMA 500 and its characteristics are described. Using this sensor, a joint torque servomechanism is designed and implemented. A model of the actuator-transmission-load system, including flexibility, is developed and verified using both time- and frequency-domain techniques. Compensators based on this model are designed and tested. Experimental results obtained from pure torque control and joint motion tracking are presented. These results demonstrate a significant reduction of the effective friction (97%) and a substantial improvement in fine motion control.>

A framework for the analysis and control of manipulator systems with respect to the dynamic behavior of their end-effectors is developed. First, issues related to the description of end-effector tasks that involve constrained motion and active force control are discussed. The fundamentals of the operational space formulation are then presented, and the unified approach for motion and force control is developed. The extension of this formulation to redundant manipulator systems is also presented, constructing the end-effector equations of motion and describing their behavior with respect to joint forces. These results are used in the development of a new and systematic approach for dealing with the problems arising at kinematic singularities. At a singular configuration, the manipulator is treated as a mechanism that is redundant with respect to the motion of the end-effector in the subspace of operational space orthogonal to the singular direction.

To provide COSMOS, a dynamic model based manipulator control system, with an improved dynamic model, a PUMA 560 arm was disassembled; the inertial properties of the individual links were measured; and an explicit model incorporating all of the non-zero measured parameters was derived. The explicit model of the PUMA arm has been obtained with a derivation procedure comprised of several heuristic rules for simplification. A simplified model, abbreviated from the full explicit model with a 1% significance criterion, can be evaluated with 805 calculations, one fifth the number required by the recursive Newton-Euler method. The procedure used to derive the model is laid out; the measured inertial parameters are presented, and the model is included in an appendix.

In this paper we present a unified approach for the control of manipulator motions and active forces based on the operational space formulation. The end-effector dynamic model is used in the development of a control system in which the generalized operational space end-effector forces are selected as the command vector. This formulation provides a framework for natural and efficient integration of both end-effector force and motion control. A "generalized position and force specification matrix" is used for the specification of tasks that involve simultaneous motion and force operations. Flexibility in the force sensor, end-effector, and environment, and problems related to impact are discussed. The real-time operational space control system, COSMOS, has been recently implemented in the NYMPH multiprocessor system. Results of experiments involving contact and force step input response are presented.

In this paper, we discuss the issue of locating objects through multiple sensory information. Sensor measurements are subject to limitations of sensor precision and accuracy. Although errors in position estimates are affected only by the errors of sensor measurements, errors in orientation estimates are also dependent on the dimensions over which the measurement has been made. The concept of good measurement is used in selecting and weighting partial estimates of the position and orientaion. The problem of finding the best estimate of the position and orientation is formulated as a linear system of these multiple estimates. The best estimate is then obtained by solving this system in a weighted least square sense. This method has been implemented for a manipulator end-effector instrumented with centroid and matrix tactile sensors.

This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the "artificial potential field" concept. In this approach, collision avoidance, traditionally considered a high level planning problem, can be effectively distributed between different levels of control, allowing real-time robot operations in a complex environment. We have applied this obstacle avoidance scheme to robot arm using a new approach to the general problem of real-time manipulator control. We reformulated the manipulator control problem as direct control of manipulator motion in operational space-the space in which the task is originally described-rather than as control of the task's corresponding joint space motion obtained only after geometric and kinematic transformation. This method has been implemented in the COSMOS system for a PUMA 560 robot. Using visual sensing, real-time collision avoidance demonstrations on moving obstacles have been performed.