Manipulating a deformable linear object (DLO) such as wire, cable, and rope is a common yet challenging task due to their high degrees of freedom and complex deformation behaviors, especially in an environment with obstacles. Existing local control methods are efficient but prone to failure in complex scenarios, while precise global planners are computationally intensive and difficult to deploy. This paper presents an efficient, easy-to-deploy framework for collision-free DLO manipulation using mobile manipulators. We demonstrate the effectiveness of leveraging standard planning tools for high-dimensional DLO manipulation without requiring custom planners or extensive data-driven models. Our approach combines an off-the-shelf global planner with a real-time local controller. The global planner approximates the DLO as a series of rigid links connected by spherical joints, enabling rapid path planning without the need for problem-specific planners or large datasets. The local controller employs control barrier functions (CBFs) to enforce safety constraints, maintain the DLO integrity, prevent overstress, and handle obstacle avoidance. It compensates for modeling inaccuracies by using a state-of-the-art position-based dynamics technique that approximates physical properties like Young’s and shear moduli. We validate our framework through extensive simulations and real-world demonstrations. In complex obstacle scenarios—including tent pole transport, corridor navigation, and tasks requiring varied stiffness—our method achieves a 100% success rate over thousands of trials, with significantly reduced planning times compared to state-of-the-art techniques. Real-world experiments include transportation of a tent pole and a rope using mobile manipulators. We share our ROS-based implementation to facilitate adoption in various applications.

Manipulating deformable objects arises in daily life and numerous applications. Despite phenomenal advances in industrial robotics, manipulation of deformable objects remains mostly a manual task. This is because of the high number of internal degrees of freedom and the complexity of predicting its motion. In this paper, we apply the computationally efficient position-based dynamics method to predict object motion and distance to obstacles. This distance is incorporated in a control barrier function for the resolved motion kinematic control for one or more robots to adjust their motion to avoid colliding with the obstacles. The controller has been applied in simulations to 1D and 2D deformable objects with varying numbers of assistant agents, demonstrating its versatility across different object types and multi-agent systems. Results indicate the feasibility of real-time collision avoidance through deformable object simulation, minimizing path tracking error while maintaining a predefined minimum distance from obstacles and preventing overstretching of the deformable object. The implementation is performed in ROS, allowing ready portability to different applications.

Industrial robots are increasingly deployed in applications requiring an end effector tool to closely track a specified path, such as in spraying and welding. Performance and productivity present possibly conflicting objectives: tracking accuracy, path speed, and motion uniformity. Industrial robots are programmed through motion primitives consisting of waypoints connected by pre-defined motion segments, with specified parameters such as path speed and blending zone. The actual executed robot motion depends on the robot joint servo controller and joint motion constraints (e.g., velocity, acceleration limits) which are largely unknown to the users. Programming a robot to achieve the desired performance today is time-consuming and mostly manual, requiring tuning a large number of coupled parameters in the motion primitives. The performance also depends on the choice of additional param-eters: possible redundant degrees of freedom, location of the target curve, and the robot configuration. This paper presents a systematic approach to optimize robot motion parameters. The approach first selects the static parameters, then chooses the motion primitives, and finally iteratively updates the waypoints to minimize the tracking error. The ultimate performance objective is to maximize the path speed subject to the tracking accuracy and speed uniformity constraints over the entire path. We have demonstrated the effectiveness of this approach both in simulation and on physical systems for ABB and FANUC robots applied to two challenging example curves. Comparing with the baseline using the current industry practice, the optimized performance shows over 100% performance improvement.

Collaborative robots and space manipulators contain significant joint flexibility. It complicates the control design, compromises the control bandwidth, and limits the tracking accuracy. The imprecise knowledge of the flexible joint dynamics compounds the challenge. In this paper, we present a new control architecture for controlling flexible-joint robots. Our approach uses a multi-layer neural network to approximate unknown dynamics needed for the feedforward control. The network may be viewed as a linear-in-parameter representation of the robot dynamics, with the nonlinear basis of the robot dynamics connected to the linear output layer. The output layer weights are updated based on the tracking error and the nonlinear basis. The internal weights of the nonlinear basis are updated by online backpropagation to further reduce the tracking error. To use time scale separation to reduce the coupling of the two steps - the update of the internal weights is at a lower rate compared to the update of the output layer weights. With the update of the output layer weights, our controller adapts quickly to the unknown dynamics change and disturbances (such as attaching a load). The update of the internal weights would continue to improve the converge of the nonlinear basis functions. We show the stability of the proposed scheme under the "outer loop" control, where the commanded joint position is considered as the control input. Simulation and physical experiments are conducted to demonstrate the performance of the proposed controller on a Baxter robot, which exhibits significant joint flexibility due to the series-elastic joint actuators.

Geometric models are crucial for many robotics applications. Current robotic 3D reconstruction systems only focus on specific reconstruction goals which make them hard to adapt to different tasks. In this paper we present a next-best-view framework which allows robots to construct a geometric model incrementally through consecutive sensing actions. Instead of limiting the type and total number of sensors, in each sensing step we evaluate actions from all available sensors and pick the best to execute. Our framework is more comprehensive since the model building process can be designed to best accomplish different tasks. The system has been demonstrated in two experiments on 3D reconstruction and weld seam inspection, yielding promising results.

Fast and precise motion control is important for industrial robots in manufacturing applications. However, some collaborative robots sacrifice precision for safety, particular for high motion speed. The performance degradation is caused by the inability of the joint servo controller to address the uncertain nonlinear dynamics of the robot arm, e.g., due to joint flexibility. We consider two approaches to improve the trajectory tracking performance through feedforward compensation. The first approach uses iterative learning control, with the gradient-based iterative update generated from the robot forward dynamics model. The second approach uses dynamic inversion to directly compensate for the robot forward dynamics. If the forward dynamics is strictly proper or is non-minimum-phase (e.g., due to time delays), its stable inverse would be non-causal. Both approaches require robot dynamical models. This paper presents results of using recurrent neural networks (RNNs) to approximate these dynamical models - forward dynamics in the first case, inverse dynamics (possibly non-causal) in the second case. We use the bi-directional RNN to capture the noncausality. The RNNs are trained based on a collection of commanded trajectories and the actual robot responses. We use a Baxter robot to evaluate the two approaches. The Baxter robot exhibits significant joint flexibility due to the series-elastic joint actuators. Both approaches achieve sizable improvement over the uncompensated robot motion, for both random joint trajectories and Cartesian motion. The inverse dynamics method is particularly attractive as it may be used to more accurately track a user input as in teleoperation.

Building interior temperatures are affected by the outdoor air temperature. Note that outdoor weather patterns are somewhat repetitive in nature and historical records of outdoor temperature are readily accessible; we present a data-driven iterative learning approach to improve the room temperature tracking performance over time. By comparing the short-term temperature forecast with the past data, chains of (nonconsecutive) days exhibiting similar outside temperature patterns can be identified. The corresponding building operation record (heat input and temperature output trajectories) may then be used in the iterative learning control (ILC) to update the input based on the past temperature tracking error. Multizone buildings are strictly passive from the heat input to temperature output in all zones. This property assures the convergence of the ILC iteration if the update gain is suitably bounded, without the need of an accurate model. This means that for each chain, the zone temperature deviation from the specified profile will converge to zero as the number of days in the chain grows (i.e., as more iterations of ILC are performed). Using a six-zone physical test bed with programmable ambient temperatures, we demonstrate the practicality of the proposed approach in multiple experimental trials. Additional longer-duration simulations are performed based on the actual temperature recorded in Orlando, FL, USA and New York, NY, USA over a two-year period. In all cases, ILC is shown to improve the tracking error in the presence of ambient temperature fluctuations.

In multi-finger or multi-arm grasping with friction contacts, maintaining force closure during motion is critical. Violation of this condition would cause contact slippage and possibly loss of grasp. This issue is of particular importance in space robotics, where the loss of grasp could lead to catastrophic consequences. There has been ample literature on stable grasp and force closure under static conditions. This paper investigates multi-arm grasping during motion, where the inertial force from the load could adversely affect grasp stability. Our approach dynamically adjusts the squeeze force and commanded robot/load motion to maintain a safe force closure condition. For a specified motion trajectory, the squeeze force is updated to prevent slippage based on the estimated inertial force. When the required squeeze force is beyond what the manipulators can safely apply, the trajectory will be scaled to reduce the inertial force component. In addition to motion-induced disturbance force, contact between the load and other objects in the environment can also cause slippage. The slip prevention strategy is extended to this case as well. The application scenario is based on the dual-arm transportation and berthing of a load in a micro-gravity environment. For laboratory testing, we use a fixed-base dual-arm robot to grasp, transport, and berth an object on a planar air bearing table. We also extend the transportation tests to a more general spatial setting, and use the dynamic squeeze adjustment to grasp, lift, and transport an object. Experimental results show the proposed method is effective at avoiding contact slippage during motion and when the object is in contact with the environment.

Robotic manipulation of highly deformable materials is inherently challenging due to the need to maintain tension and the high dimensionality of the state of the material. Past work in this area mostly focuses on generating a detailed model for the material and its interaction with the robot, then using the model to construct a motion plan. In this paper, we take a different approach by using only sensor feedback to dictate the robot motion. We consider the collaborative manipulation of a deformable sheet between a person and a dual-armed robot (Baxter by Rethink Robotics). The robot is capable of contact sensing via joint torque sensors and is equipped with a head-mounted RGBd sensor. The robot senses contact force to maintain tension of the sheet, and in turn comply to the human motion. This is akin to handling a tablecloth with a partner but with one's eyes closed. To improve the response, we use the RGBd sensor to detect folds, and command the robot to move in an orthogonal direction to smooth them out. This is like handling cloth by looking at the cloth itself. Both controllers are able to follow human motion without excessive crimps in the sheet, but as expected, the hybrid controller combining force and vision outperforms the force controller alone in terms of tension force transient. The ability to quickly detect the state of the deformable material also enables more complex manipulation strategies in the future.

Buildings with shared spaces such as corporate office buildings, university dorms, etc., are occupied by multiple occupants who typically have different temperature preferences. Attaining a common temperature set-point that is agreeable to all users (occupants) in such a multi-occupant space is a challenging problem. Furthermore, the ideal temperature set-point should optimally trade off the building energy cost with the aggregate discomfort of all the occupants. However, the information on the comfort range (function) is held privately by each occupant. Using occupant-differentiated dynamically-adjusted penalty factor as feedback signals, we propose a distributed solution which ensures that a consensus is attained among all occupants upon convergence, irrespective of their ideal temperature preferences being in coherence or conflicting. Occupants are only assumed to be rational, in that they choose their own temperature set-points so as to minimize their individual energy cost plus discomfort. We establish the convergence of the proposed algorithm to the optimal temperature set-point vector that minimizes the sum of the energy cost and the aggregate discomfort of all occupants in a multizone building. Simulations with realistic parameter settings illustrate validation of our theoretical claims and provide insights on the dynamics of the system with a mobile user population.

We present a novel system to achieve coordinated task-based control on a dual-arm industrial robot for the general tasks of visual servoing and bimanual hybrid motion/force control. The industrial robot, consisting of a rotating torso and two seven degree-of-freedom arms, performs autonomous vision-based target alignment of both arms with the aid of fiducial markers, two-handed grasping and force control, and robust object manipulation in a tele-robotic framework. The operator uses hand motions to command the desired position for the object via Microsoft Kinect while the autonomous force controller maintains a stable grasp. Gestures detected by the Kinect are also used to dictate different operation modes. We demonstrate the effectiveness of our approach using a variety of common objects with different sizes, shapes, weights, and surface compliances.

Industrial robots are precise and efficient at performing repetitive tasks. However, robots lack the ability to recognize and manipulate objects. They rely on human operators to translate the desired task into a set of operations that it can perform. The research area of bin-picking aims to provide robots with the ability to manipulate randomly ordered objects in unstructured environments. This research focuses on developing a robust vision guided robotic block pick-up and stacking system. We use binary markers to aid in block identification and localization, a custom 3D-printed gripper for robust grasping, and planning algorithms to determine the grasp sequence. By integrating a low-cost webcam with an industrial robot, our system is able to observe the block locations in a random pile, determine the appropriate response necessary to grasp, and sequence to remove blocks from a pile.

This paper describes the algorithm development and experimental results of a vision-guided multiprobe microassembly system. The key focus is to develop the capabilities required for the construction of 3-D structures using only planar microfabricated parts. Instead of using grippers, multiple sharp-tipped probes are coordinated to manipulate parts by using vision feedback. This novel probe-based approach offers both stable part grasping and dexterous part manipulation. The light weight of the part and relatively slow motion means that only kinematics-based control is required. However, probe motions need to be carefully coordinated to ensure reliable and repeatable part grasping and manipulation. Machine vision with multiple cameras is used to guide the motion. No contact force sensor is used; instead, vision sensing of the probe bending is used for the grasp force control. By combining preplanned manipulation sequences and vision-based manipulation, repeatable spatial (in contrast with planar) manipulation and insertion of a submillimeter part have been demonstrated with an experimental testbed consisting of two actuated probes, a passive probe, an actuated die stage, and two cameras for vision feedback.

In our recent work, we have demonstrated effectiveness of the concept of multi-probe microassembly for manipulating and inserting microscale, sub-millimeter, parts to create three-dimensional microstructures. However, the approach has been based on trial-and-error manual teaching of grasp points to ensure a stable grasp during motion. As a result, the part orientation is restricted (nearly aligned with the world reference frame and lying flat) to ensure successful grasping and manipulation. In this paper, we developed a kinematics based hybrid motion and force control based only on vision feedback. We first conduct a systematic analysis of the bending of the probes while they are in contact with the part, to estimate the grasp force based on the vision feedback of the probe configuration. A Jacobian based controller is then used for position manipulation while maintaining the desired squeeze force. Experimental results with two probes and two camera are included to demonstrate the effectiveness of the controller to move the part to specified position and orientation while maintaining sufficient squeeze force to prevent part slippage.

This paper describes the operator assisted automated assembly of a 3-legged spatial platform by using a vision guided multi-probe assembly process. This is the first step towards the ultimate goal of building a microscale active spatial platform. Two issues are highlighted in this paper: contact management and vision feedback. Using multiple probes for part grasping and manipulation has the advantage of robustness and versatility as compared to micro-grippers. However, the contacts between the probes and the part need to be carefully managed to ensure a grasp that is stable for part pick-up and yet manipulable to allow part motion in a controlled fashion. By using vision guidance, the probes can be coordinated to grasp the parts and lift them off the die securely and reliably. We show that the contacts act as point contacts with friction, so when a part is pressed against a stationary probe, the part rotates about the axis between the contacts, changing its orientation so it may be inserted into a slot in the substrate. We have demonstrated that the three legs can be assembled in a fully automated fashion via multiple-camera vision feedback. The platform is at present assembled via tele-operation. The assembled microstructure measures 450 µm×600 µm. We are now working on the full automation of the assembly onto a substrate populated with MEMS actuators.

This paper focuses on the assignment of N discrete points among K geometrically constrained robots and determination of the order in which the points should be processed by the robots. This path planning problem is directly motivated by an industrial laser drilling system with two robots that are constrained to translate along a common line while satisfying collision avoidance constraints. The points lie on a planar base plate that translates normal to the axis of motion of the robots. The geometric constraints on the motions of the robots lead to constraints on points that can be processed simultaneously.We use a two step approach to solve the path planning problem: (1) Splitting Problem: Assign the points to the K robots, subject to geometric constraints, to maximize parallel processing of the points. (2) Ordering Problem: Find an order of processing the split points by formulating and solving a multidimensional Traveling Salesman Problem (TSP) in the if-tuple space with an appropriately defined metric to minimize the total travel cost. For K = 2, we solve the splitting problem optimally in O(N3) time by converting it to a maximum cardinality matching problem. Since this is too slow for large datasets, we also provide a greedy O(N log N) algorithm. We provide computational results showing that the greedy algorithm solution is very close to the optimal solution for large datasets. For the ordering problem we present local search based heuristics to improve the multidimensional TSP tour. We give computational results for the ordering problem and for the overall performance gain obtained (over a single robot system) by using our algorithm. Finally, we extend our approach to a K-robot system and give computational results for K = 4.

We consider a group of agents collaboratively transporting a flexible payload. The contact forces between the agents and the payload are modeled as gradients of nonlinear potentials that describe the deformations of the payload. The load-transport problem is then treated in a similar fashion to the formation-control problem. Decentralized control laws are developed such that without explicit communication, the agents and the payload converge to the same constant velocity; meanwhile, the contact forces are regulated. Experimental results illustrate the effectiveness of our designs.

Biological studies, drug discovery, and medical diagnostics benefit greatly from automated microscope platforms that can outperform even the most skilled human operators in certain tasks. However, the small field-of-view of a traditional microscope operating at high resolution poses a significant challenge in practice. The common approach of using a moving stage suffers from relatively low dynamic bandwidth and agitation to the specimen. This paper describes an automated microscope station based on the novel adaptive scanning optical microscope (ASOM), which combines a high-speed post-objective scanning mirror, a custom design scanner lens, and a microelectromechanical systems (MEMS) deformable mirror to achieve a greatly expanded field-of-view. After describing the layout and operating principle of the ASOM imaging subsystem, we present a system architecture for an automated microscope system suitable for the ASOM's unique wide field and high-speed imaging capabilities. We then describe a low-cost experimental prototype of the ASOM that demonstrates all critical optical characteristics of the instrument, including the calibration of the MEMS deformable mirror. Finally, we present initial biological (living nematode worms) imaging results obtained with the experimental apparatus and discuss the impact of the ASOM on biomedical imaging activities.

This paper focuses on the assignment of discrete points to two robots, in the presence of geometric and kinematic constraints between the robots. The individual points have differing processing times, and the goal is to identify an assignment of points to the robots so that the total processing time is minimized. The assignment of points to the robots is the first step in the path generation process for the robots. This work is motivated by an industrial microelectronics manufacturing system with two robots, with square footprints, that are constrained to translate along a common line while satisfying proximity and collision avoidance constraints. The N points lie on a planar base plate that can translate along the plane normal to the direction of motion of the robots. The geometric constraints on the motions of the two robots lead to constraints on points that can be processed simultaneously. We show that the point assignment for processing problem can be converted to a maximum weighted matching problem on a graph and solved optimally in O(N3) time. Since this is too slow for large datasets, we present a O(N2) time greedy algorithm and prove that the greedy solution is within a factor of 3/2 of the optimal solution. Finally, we provide computational results for the greedy algorithm on typical industrial datasets.

Manipulation and assembly tasks associated with micro-systems, biotechnology, and product miniaturization demand that robots increasingly operate at microscopic dimensions. This paper discusses a new microscope design, called the adaptive scanning optical microscope (ASOM), that is particularly suitable for observing robotic activities at the micron scale. The ASOM combines a custom designed scanner lens, high speed steering mirror, and MEMS deformable mirror to offer the advantages of a greatly expanded field of view, rapid image acquisition, and no agitation to the workspace or specimen. After briefly discussing the challenges of micro assembly and micro manipulation, we present the ASOM theory of operation and include simulated performance results. A low cost proof-of-concept experimental prototype of the ASOM is then described and used to demonstrate shape optimization of the MEMS deformable mirror for different field positions. Realtime tracking of multiple micromanipulators in a workspace and full area coverage are experimentally demonstrated. These results validate the ASOM concept and serve as a crucial step towards realizing a fully operational and high performance ASOM to enable the observation of micro-robotic activities over a large workspace

Due to their monolithic construction and superior wear and loss properties, flexure joints have been used to reduce the mechanism size and increase the positioning accuracy. The compliance of flexure joints, however, can affect the static and dynamic characteristics of the overall mechanism. To design mechanisms containing flexure joints, we have proposed a multi-objective optimization approach to take into account the multitude of performance metrics and design constraints. A Pareto frontier is first computed, and secondary design criteria, such as sensitivity and dynamic characteristics, are then applied to select the final design. To reduce the computation load and facilitate design iteration, a lumped spring approximation, the Paros-Weisbord model, is used to characterize the flexure joints and the pseudo-rigid-body model is used as an approximate description of mechanisms. This paper presents this approach applied to the design of a micro-gripper. The performance metrics are chosen to be the manipulability of the gripper opening and the decoupling of a stiffness matrix (reflecting the remote center of compliance criterion). Different design and initial fabrication results are included

Parallel mechanisms frequently possess an unstable type of singularity that has no counterpart in serial mechanisms. When the mechanism is at or near this type of singularity, it loses the ability to counteract external forces in certain directions. The determination of unstable singular configurations in parallel robots is challenging, and in the past, has been tackled by exhaustive numerical searches of the mechanism workspace using an accurate analytical model of the mechanism kinematics. This paper considers the singularity-determination problem from a geometric perspective for n-legged spatial parallel mechanisms. By using the constraints on the passive joint velocities, a necessary condition for an unstable singularity is derived.

With the rapid progress of the robotic technology, it is becoming increasingly common to have multiple robots working together for material transport, cooperative assembly, etc. To ensure the proper handling of the load, especially if it is fragile or needs to be moved rapidly, the constraint force needs to be carefully managed. Tight force coordination is possible if all robots share their force information and the grasp geometry is completely known. When this is not the case, a common approach is to use the leader/follower strategy, where the leader provides the position control for the load and other robots comply based on the individual contact force measurements. This paper considers an alternate decentralized motion and force control method, where all robots participate in the control of the load without sharing any position and force information. Under centralized squeeze force control, robot motion is not affected. However, when the force control is decentralized, a perturbation term is added to the motion control loop. We show that the nominal exponential stability of the motion loop preserves the closed loop stability in the presence of this perturbation. Simulation and experimental results are included to demonstrate the proposed approach.

Parallel mechanisms frequently possess an unstable type of singularity that has no counterpart in serial mechanisms. When the mechanism is at or near this type of singularity, it loses the ability to counteract external forces in certain directions. The determination of unstable singular configurations in parallel robots is challenging, and in the past has been tackled by exhaustive numerical searches of the mechanism workspace using an accurate analytical model of the mechanism kinematics. This paper considers the singularity determination problem from a geometric perspective for n-legged spatial parallel mechanisms. By using the constraints on the passive joint velocities, a necessary condition for unstable singularity is derived that identifies the reason for such singularities.

Parallel mechanisms frequently possess an unstable type of singularity that has no counterpart in serial mechanisms. When the mechanism is at or near this type of singularity, it loses the ability to counteract external forces in certain directions. The determination of unstable singular configurations in parallel robots is challenging, and in the past has been tackled by exhaustive numerical searches of the mechanism workspace using an accurate analytical model of the mechanism kinematics. This paper considers the singularity determination problem from a geometric perspective for n-legged spatial parallel mechanisms. By using the constraints on the passive joint velocities, a necessary condition for unstable singularity is derived that identifies the reason for such singularities.

In microsystems applications from micro-assembly to biological observation and manipulation, the optical microscope remains one of the most important tools. However, it suffers from the well known trade-off between resolution and field of view. Traditional solutions involve moving the sample under the microscope using a moving stage or moving the microscope itself, and switching between low and high magnification objective lenses. In this paper, we present a new optical microscope design that uses a 2-dimensional high speed, high precision steering mirror system to scan the sample. By stitching the images together as a mosaic, we have the potential to achieve both high resolution and large field of view. Working in coordination with a deformable mirror, this arrangement offers certain advantages over the current state of the art in demanding spatial-temporal observations. We describe the theory of operation, our design methodology, and present a preliminary simulated design. A reduced functionality experimental prototype has been constructed to demonstrate the basic efficacy of the concept and we demonstrate with both biological and micro-assembly examples.

We study power control in multicell CDMA wireless networks as a team optimization problem where each mobile attains its individual fixed target SIR level by transmitting with minimum possible power level. We derive conditions under which the power control problem admits a unique feasible solution. Using a Lagrangian relaxation approach similar to F. Kelly et al. (1998) we obtain two decentralized dynamic power control algorithms: primal and dual power update, and establish their global stability utilizing both classical Lyapunov theory and the passivity framework [J.T. Wen and M. Arcak, February 2004]. We show that the robustness results of passivity studies [(X. Fan et al., July 2004), (X. Fan et al., 2004)] as well as most of the stability and robustness analyses of F. Kelly et al. (1998) in the literature are applicable to the power control problem considered. In addition, some of the basic principles of call admission control are investigated from the perspective of the model adopted in this paper. We illustrate the proposed power control schemes through simulations.

Flexure joints are frequently used in precision-motion stages and microrobotic mechanisms due to their monolithic construction. The joint compliance, however, can affect the static and dynamic performance of the overall mechanism. In this paper, we consider the analysis and design of general platform-type parallel mechanisms containing flexure joints. Based on static performance measures such as task-space stiffness and manipulability, and constraints such as joint stress, mechanism size, and workspace volume, we pose the design problem as a multiobjective optimization. We first calculate the Pareto frontier, which can then be used to select the desired design parameters based on secondary criteria, such as performance sensitivity and dynamic characteristics. To facilitate design iteration, we apply the pseudo rigid-body approach with a lumped approximation of the flexure joints. A planar mechanism is used to illustrate the analysis and design techniques.

Flexure joints are frequently used in precision motion stages and micro-robotic mechanisms due to their monolithic construction. The joint compliance, however, can affect the static and dynamic performance of the overall mechanism. In this paper, we consider the analysis and design of general platform type parallel mechanisms containing flexure joints. We consider static performance measures such as task space stiffness and manipulability, while subject to constraints such as joint stress, mechanism size, workspace volume, and dynamic characteristics. Based on these performance measures and constraints, we adopt the multi-objective optimization approach. We first obtain the Pareto frontier, which can then be used to select the desired design parameters based on secondary criteria such as performance sensitivity. To simplify presentation, we consider only lumped approximation of flexure joints in the pseudo-rigid-body approach. A planar mechanism is included to illustrate the analysis and design techniques. Tools presented in this paper can also be applied to a broader class of compliant mechanisms, including robots with inherent joint flexibility as well as compliant robots for contact tasks.

This paper considers the localization and navigation of a mobile robot. The control strategy is based on a nonlinear model predictive control technique that utilizes the Newton method. The robot localization is obtained using information from odometric and ultrasonic sensors through a Kalman filter. Simulation and experimental results illustrate the efficacy of the proposed method.

Thermal bimorphs are a popular actuation technology in MEMS (micro-electro-mechanical systems). Their operating principle is based on differential thermal expansion induced by Joule heating. Thermal bimorphs, and other thermal flexure actuators have been used in many applications, from micro-grippers, to micro-optical mirrors. In most cases open-loop control is used to difficulties in fabricating positioning sensors together with actuator. In this paper we present several methods for extracting reduced-order thermal flexure actuator models based on experimental data, physical principles, and FEA simulation. We then use the models to generate optimal driving signals using input shaping techniques. Both simulation and experimental results are included to illustrate the efficacy of our approach. This framework can also be applied to other types of MEMS actuators, including electrostatic comb drives.

Network flow control regulates the traffic between sources and links based on congestion, and plays a critical role in ensuring satisfactory performance. In recent studies, global stability has been shown for several flow control schemes. By using a passivity approach, this paper presents a unifying framework which encompasses these stability results as special cases. In addition, the new approach significantly expands the current classes of stable flow controllers by augmenting the source and link update laws with passive dynamic systems. This generality offers the possibility of optimizing the controllers, for example, to improve robustness and performance with respect to time delay, unmodeled flows, and capacity variation.

Parallel mechanisms frequently contain an unstable type of singularity that has no counterpart in serial mechanisms. When the mechanism is at or near this type of singularity, it loses the ability to counteract external forces in certain directions. The determination of unstable singular configurations in parallel robots is challenging in general, and is usually tackled via an exhaustive search of the workspace using an accurate analytical model of the mechanism kinematics. This paper considers the singularity determination problem from a geometric perspective for planar and spatial three-legged parallel mechanisms. By using the constraints on the passive joint velocities, we derive a necessary condition for the unstable singularities. Using this condition, certain singularities can be found for certain type of platforms. As an example, new singular poses are discovered using this approach for a six-degree-of-freedom machining center.

There have been many innovations introduced in securing tissues, but suturing with secure knots remains the most popular and cost effective method. However, knot tying demands higher level of skill on the surgeon, especially in the minimally invasive context. This paper presents the analytical and experimental results of automatic knot tying using an inhouse surgical robot with emphasis on the placement of the knot and tension regulation. The problem is decomposed to two parts: knot placement and knot tension control. The knot sliding condition is first obtained based on a simplified model of half knotted suture, and motion trajectories for the robots are proposed to minimize tearing trauma of the tissue while properly place the knot. The second half the paper considers the automatic tension regulation in the knot, which is usually difficult for the surgeon to achieve accurately. By using a cost effective means for tension measurement and applying the explicit force control algorithm with active damping, we are able to achieve stable and robust tension regulation. Experimental results are presented to demonstrate efficacy of the proposed algorithm.

Motivated by the ever increasing demand for higher performance and lower cost, there have been growing efforts to conduct mechanical and control system designs concurrently rather than sequentially. Indeed, the area of mechatronics has sprung forth largely motivated by the desire to consider the mechanical control system design as an integrated process rather than disjointed pieces. In this paper, we present a case study of the optimal mechanical design of a rotary inverted pendulum by incorporating the control objectives involving disturbance tolerance and domain of attraction. These objectives are converted to two related metrics, controllability and unstable pole location of the open-loop system, which can be computed efficiently and do not depend on the chosen control algorithm. Since the optimization is multi-objective, and does not possess a unique optimum, a family of Pareto designs is generated. The original objectives in terms of disturbance rejection and domain of attraction are then evaluated in simulation and experimentation along the Pareto optimal solutions to provide the quantitative trade-offs of different designs.

This paper presents a new surgical robotic system called EndoBot for assisting surgeons in performing minimally invasive surgery (MIS). The EndoBot is designed for collaborative operation between the surgeon and the robot. The demand on the surgeon is higher during suturing task which is the primary tissue approximation method and has been known as one of the most difficult tasks in MIS. Our work is the first effort in autonomous robotic suturing for MIS. In this paper, we show the motion controller design for autonomous and shared control mode and discuss autonomous robotic-suturing algorithms.

Parallel mechanisms frequently contain an unstable type of singularity that has no counterpart in serial mechanisms. When the mechanism is at or near this type of singularity, it loses the ability to counteract external forces in certain directions. The paper considers an interesting alternative to redundant actuation through the application of additional kinematic constraint via passive joint braking in the close neighborhood of unstable poses. The brake has the advantage of not requiring additional actuator/sensor pairs or mechanism architecture redesign, and of being implemented only when necessary to stabilize the system. The kinematic analysis of braked mechanisms is supported with results using a six degree-of-freedom parallel machining mechanism.

Parallel mechanisms frequently contain an unstable type of singularity that has no counterpart in serial mechanisms. When the mechanism is at or near this type of singularity, it loses the ability to counteract external forces in certain directions. By altering the mechanism through, for example, additional kinematic linkages, the singularity can be modified or even removed. Another approach is to actuate certain unactuated degrees of freedom. The manipulability is guaranteed to improve over the original mechanism, but the mechanism is now over-actuated. By studying a recently proposed parallel machining center, we examine the effectiveness of singularity modification through redundant actuation. As a metric, we use the condition number of the recently developed manipulability ellipsoid for general parallel mechanisms.

This paper extends the kinematic manipulability concept commonly used for serial manipulators to general constrained rigid multibody systems. Examples of such systems include multiple cooperating manipulators, multiple fingers holding a payload, multileg walking robots, and variable geometry trusses. Explicit formulas for velocity and force manipulability ellipsoids are derived and their duality explained. Singularities are classified into two types: 1) unmanipulable singularity; 2) unstable singularity. The former is similar the singularities in serial chains where velocity manipulability ellipsoid is degenerate and force manipulability ellipsoid infinite. The latter is unique to parallel mechanisms, the velocity manipulability ellipsoid becomes infinite and force manipulability degenerate. In the case of multifinger grasp, these concepts correspond to unmanipulable or unstable grasps.

Extends the kinematic manipulability concept commonly used for serial manipulators to general constrained rigid multibody systems. Examples of such systems include multiple cooperating manipulators, multiple fingers holding a payload, multi-leg walking robots, and variable geometry trusses. Explicit formulas for velocity and force manipulability ellipsoids are derived and their duality explained. The concept of unstable grasp and manipulable grasp are also extended and illustrated with examples.

A modified magnetic field (MMF) has been used for the generation of robot motion plans of a linked manipulator such as a robot arm, in a complex workspace while veering around enclosing obstacle walls. The MMF method ensures global convergence and collision constraint satisfaction while allowing the application of this method as an online plan generation and control technique in both Cartesian and configuration spaces. Methods for using the MMF to perform both the planning and dynamic control of a robot arm around obstacles and singular configurations are developed in this paper and results shown for a specific workspace configuration.

This paper presents an algorithm for finding a kinematically feasible path for a nonholonomic system in the presence of obstacles. We first consider the path planning problem without obstacles by transforming it into a nonlinear least squares problem in an augmented space which is then iteratively solved. Obstacle avoidance is included as inequality constraints. Exterior penalty functions are used to convert the inequality constraints Into equality constraints. Then the same nonlinear least squares approach is applied. We demonstrate the efficacy of the approach by solving some challenging problems, including a tractor-trailer and a tractor with a steerable trailer backing in a loading dock. These examples demonstrate the performance of the algorithm in the presence of obstacles and steering and jackknife angle constraints.

A class of iterative methods have recently been proposed for the path planning of nonholonomic systems. These methods warp an initial path iteratively to an acceptable final path by using Newton-Raphson or gradient type of algorithms. Once a path is found off-line, a feedback controller is then used to follow the path. In this paper, we propose a modification of these off-line methods to transform them directly into a feedback controller. The main idea is to couple the iteration variable to the actual time, thus the control is executed during the path iteration, before the convergence. We show that this scheme guarantees the closed loop asymptotic stability when the system model is known, and possesses certain robustness when the model information is imperfect. By using interior penalty functions, inequality constraints can also be handled by the algorithm. Simulation results are included, showing promise of the approach.

For the problem of nonholonomic motion planning in the presence of obstacles, a path-iteration algorithm with an "exterior penalty" function has recently been proposed. The convergence of the iterative algorithm requires the avoidance of singular controls. With the inclusion of a penalty function, the set of singular control may increase substantially. In this paper, we propose a specific guideline for choosing the penalty function which does not introduce additional singular controls. In the case of the N-trailer system, this result can be used to guarantee the convergence of the path planning algorithm. To demonstrate the effectiveness of the proposed algorithm, simulation results are included.

This paper introduces a new feedback algorithm for steering a point robot through an obstacle field. The key innovation is the use of a circulatory field to rotate the robot path around the obstacles instead of the common potential field which repels the robot. This idea is motivated by a charged particle in a magnetic field generated by a current flowing around the obstacle. In constrast, the potential field approach is associated with a repulsive static electric field generated by charges of the same polarity as the robot, on the obstacle. The circulatory field does not generate any spurious local minimum as it does not change the total energy of the system. By combining with an attractive potential field associated with the desired destination, this method achieves global convergence while avoiding collisions with obstacles.

It is well known that the linear feedback of the quarternion of the attitude error and angular velocity globally stabilizes the attitude of a rigid body. In this note, we show that the angular velocity feedback can be replaced by a nonlinear filter of the quarternion, thus removing the need of direct angular velocity measurement. In contrast to other approaches, this design exploits the inherent passivity of the system; a dynamic observer reconstructing the velocity is not needed. An application of the proposed scheme is illustrated for the robot control problem. Simulation results are included to illustrate the theoretical results.

This paper presents a new approach to path planning for redundant manipulators. The path planning problem is posed as a finite time nonlinear control problem which can be solved by a Newton-Raphson type algorithm together with an exterior penalty function method. This technique is capable of handling various goal task definitions as well as incorporating both joint and task space constraints. The algorithm has shown promising results in planning joint path sequences to meet Cartesian goal planning and path following. In contrast to local approaches, this algorithm is less prone to problems such as singularities and local minima. Applications to planar 3R and 4R arms, cooperating 3R arms and a spatial 9 DOF arm are included.>

This paper presents an algorithm for finding a kinematically feasible path which satisfies a given set of nonholonomic constraints while enforcing both equality and inequality constraints on the configuration vector. The path planning problem is transformed from a finite time nonlinear control problem into a static root finding problem which is iteratively solved. By using an exterior penalty function method, the constrained root finding problem is converted to an unconstrained problem. Convergence of the algorithm depends upon a certain gradient operator being full rank. It has recently been shown that, in the absence of any configuration inequality constraints, the full rank condition is generic. In this paper, we show the full rank condition for a special inequality constraint case. An experiment in which the algorithm is applied to an actual double tractor-trailer vehicle is presented.>

In this paper, a two-time-scale neural controller applied to tile tracking control of rigid manipulators is introduced. Several fast learning rules and slow learning strategies are proposed. The stability properties of the closed loop system with the proposed two-time-scale neural controller are analyzed. The results show that the tracking error will be uniformly bounded and converge to a bounded region. If a sufficiently large leakage term is used in the fast learning rule, the ultimate bound of the tracking error depends only on the accuracy of the slow learning. Moreover, the feasibility of the proposed neural controller is demonstrated through the simulation of a two-link rigid robot manipulator.>

The effects of spinning motors on the motion of robot manipulators on a mobile space platform are evaluated. The complete dynamics of a geared manipulator on a space platform, which includes the dynamic coupling from motor to link and the gyroscopic forces from the spinning motors, is derived. The complete dynamic model is compared to a model using the common approximation to geared manipulators. The results show that the effect of gyroscopic forces is negligible from the viewpoint of robot control but significant from the viewpoint of platform control. The motor gyroscopic forces result in significant changes in platform orientation and manipulator tip position that are not accounted for in current models of robot manipulation in space. The changes in platform orientation and manipulator tip position are independent of the speed of the motion.>

A Newton-Euler analysis of a robot manipulator with gears or joint flexibilities is presented. A recursive order N form for the calculation of the manipulator forward dynamics that includes all gyroscopic forces and motor-link acceleration interactions is developed. The commonality between flexibly jointed manipulators and geared manipulators is discussed. For the flexible joint, the motor-link dynamics are described using a general function for the coupling between motor and link. The dynamic effects of motors and the accurate modeling of industrial manipulator dynamics investigated using a simulation of a PUMA 560 are also presented.>

A general family of asymptotically stabilizing tracking control laws is introduced for a class of nonlinear Hamiltonian systems. The inherent passivity property of this class of systems ad the passivity theorem are used to show the closed-loop input/output stability which is then related to the internal state space stability through an observability condition. Applications of these results include fully actuated robots, flexible joint robots, and robots with link flexibility.>

The dynamic equations of motion are presented for two or more cooperating manipulators on a freely moving mobile platform. The system of cooperating robot manipulators forms a closed kinematic chain where the force of interaction must be included in the formulation of robot and platform dynamics. The formulation includes the full dynamic interactions from arms to platform and arm tip to arm tip, and the possible translation and rotation of the platform. The equations of motion are shown to be identical in structure to the fixed-platform cooperative manipulator dynamics. The structure of the closed-chain dynamics, allows the use of any solution for the open topological tree of base and manipulator links. The number of degrees of freedom (DOF) of the system is sufficiently large to make recursive dynamic calculation methods potentially more efficient than closed-form solutions. A complete simulation with two 6-DOF manipulators of a free-floating platform is presented along a with a multiple-arm controller to position the common load.>

A recursive formulation is presented for the calculation of the inverse and forward dynamics of rigid robot manipulators with gear systems on each joint. The complete effects of the gear ratios and the gyroscopic effects of the spinning motor/gear are included in the recursive formulation. The forward dynamics solution recursively calculates the joint accelerations when given motor torques, and the number of computations grows linearly with the number of links.>

The dynamic equations of motion for two manipulators holding a common object on a freely moving mobile platform are developed. The full dynamic interactions from arms to platform and arm-tip to arm-tip are included in the formulation. The development of the closed chain dynamics allows for the use of any solution for the open topological tree of base and manipulator links. In particular, because the system has 18 degrees of freedom, recursive solutions for the dynamic simulation become more promising for efficient calculations of the motion. Simulation of the system is accomplished through a MATLAB program, and the response is visualized graphically using the SILMA Cimstation.>

A stability analysis for robot manipulators under the influence of external forces is presented. Several control objectives are considered: rejecting the external force as a source of disturbance, complying to the external force as a generalized mass-spring-damper system, and actively controlling the external force when a dynamic model for the environment is available. An explanation of instability is given for the case in which the environment has flexibility and the gains are inappropriately chosen. When the environment is stiff in the force control subspace, robust stability can be achieved via the integral force feedback.>

The authors address the problem of motion and force control of multiple robot arms manipulating an object. A general control paradigm that decouples the motion and force control problems is introduced. For motion control, different control strategies are constructed on the basis of control input variables. There are three natural choices: joint torques, arm tip force vectors, and the acceleration of a generalized coordinate. The first choice allows relatively model-independent control by exploiting the Hamiltonian structure of the open-loop system. The latter two require the full model information but produce simpler control design problems. The motion control determines the joint torque only to within a manifold, owing to the multiple-arm kinematic constraint. To resolve the nonuniqueness of the joint torques, two methods are introduced. If the arm and object models are available, the allocation of the desired end-effector control force to the joint actuators can be optimized. The other possibility is to control the internal force about some set point. Effective force regulation can be achieved with little model information.>

Due to the highly nonlinear structure of the robotic equation of motion, the exact time optimal control is so far unavailable. The high speed requirement in many applications renders PID controller unsatisfactory. Therefore, it is important to seek a suboptimal control strategy that is implementable and performs better than the PID controller. Two types of such controllers are examined in this paper. We will demonstrate the reasoning behind each approach and compare their respective strengths and flaws.