This paper presents an algorithm that solves the inverse kinematics problem of all six degrees of freedom manipulators, “general” or “special”. A manipulator is represented by a chain of characters that symbolizes the position of prismatic and revolute joints in the manipulator and the special geometry that may exist between its joint axes. One form of the loop closure equation is chosen and the Raghavan and Roth method is used to obtain symbolically a square matrix. The determinant of this matrix yields the characteristic polynomial of the manipulator in one of the kinematic variables. As an example of the use of this algorithm we present the solution to the inverse kinematics problem of the GMF Arc Mate welding manipulator. In spite of its geometry, this industrial manipulator has a non-trivial solution to its inverse kinematics problem.

In the field of multifingered mechanisms the control/command problem is mainly a problem o1 coordination. The problem is not only to coordinate joints of a chains but also to coordinate the different chains together.

This paper presents a general and efficient method for implementing the control/command of such systems, taking into account the force distribution problem. To solve this problem it is necessary to pay great attention to dynamic effects. To do this, we broke down the Inverse Dynamic Model (I.D.M.) problem into two main levels; One level is devoted to I.D.M. computation; it can be called the Finger Level (F.L.). As we wanted to divide up the work to be done as much as possible, we subdivided the Finger Level according to the number o1 kinematic chains. In addition, we considered a second level, the Coordinator. This level has to control all the chains using the Fingers-to-Object-Interaction Model (F.O.LM.).

Next, we will also introduce new grasping systems: Polyvalent Gripper Systems (P.G.S). There are a new solution to multicomponent assembly problems. As they can be equipped with several multifingered mechanisms, they can also use the control/command scheme.

Using a functional architecture such as the NASA/NIST Standard Reference Model for Telerobot Control System Architecture (NASREM) to implement specific applications is helpful because much of the work resides in the infrastructure and the infrastructure is the same for all applications. Rather than recreating this infrastructure, our approach is to develop the additional 20% of the code which tailors the infrastructure for the specific application. This paper describes the process by which a system based on NASREM is developed.

This paper presents a new neural networks-based method to solve the motion planning problem, i.e. to construct a collision-free path for a moving object among fixed obstacles. Our ‘navigator’ basically consists of two neural networks: The first one is a modified feed-forward neural network, which is used to determine the configuration space; the moving object is modelled as a configuration point in the configuration space. The second neural network is a modified bidirectional associative memory, which is used to find a path for the configuration point through the configuration space while avoiding the configuration obstacles. The basic processing unit of the neural networks may be constructed using logic gates, including AND gates, OR gates, NOT gate and flip flops. Examples of efficient solutions to difficult motion planning problems using our proposed techniques are presented.

The Flight Telerobotic Servicer (FTS) is a robotic device which will be used to build and maintain Space Station Freedom. The FTS is expected to evolve from its initial capability of teleoperation toward greater autonomy by taking advantage of advances in technology as they become available. In order to support this evolution, NASA has chosen the NASA/NIST Standard Reference model for Telerobot Control System Architecture (NASREM) as the FTS functional architecture. As a result of the definition of generic interfaces in NASREM, the system can be modified without major impact. Consequently, different approaches to solve a problem can be tested easily. This paper describes the implementation of NASREM in the NIST laboratory. The approach is to build a flexible testbed to enhance research in robot control, computer vision, and related areas. To illustrate the real-time aspects of the implementation, a sensory interactive motion control experiment will be described.

This paper presents a new formulation as well as numerical solution for the problem of finding a point-to-point trajectory with maximum load carrying capacities for flexible manipulators. For rigid manipulators, the major limiting factor in determining the maximum allowable load (mass and mass moment of inertia) is the joint actuator capacity, while the flexibility exhibited by light weight robots or by robots operating at a higher speed dictates the need for an additional constraint to be imposed for situations where precision tracking is required, that is, the allowable deformation at the end effector. The Lagrangian assumed mode method was used to model the manipulator and load dynamics, including both joint and deflection motions. An Iterative Linear Programming (ILP) method is then used to determine the maximum allowable load of elastic robot subject to both constraints, while a general computational procedure for the multiple-link case given arbitrary trajectories is presented in detail. Symbolic derivation and simulation by using a PC-based symbolic language MATHEMATICA® was carried out for a two-link planer robot and the results further confirm the necessity of the dual constraints.

Rough joint flexibility is the dominant source of compliance in today's commercial robots in future robots containing light weight flexible arms link flexibility may become most important. Hence this paper stresses link flexibility rather than joint flexibility.

This paper presents an absolute position estimation system for a mobile robot moving on a flat surface. In this system, a 3-D landmark with four coplanar points and a non-coplanar point is utilized to improve the accuracy of position estimation and to guide the robot during navigation. Applying theoretical analysis, we investigate the image sensitivity of the proposed 3-D landmark compared with the conventional 2-D landmark. In the camera calibration stage of the experiments, we employ a neural network as a computational tool. The neural network is trained from a set of learning data collected at various points around the mark so that the extrinsic and intrinsic parameters of the camera system can be resolved. The overall estimation algorithm from the mark identification to the position determination is implemented in a 32-bit personal computer with an image digitizer and an arithmetic accelerator. To demonstrate the effectiveness of the proposed 3-D landmark and the neural network-based calibration scheme, a series of navigation experiments were performed on a wheeled mobile robot (LCAR) in an indoor environment. The results show the feasibility of the position estimation system applicable to mobile robot's real-time navigation.

The virtual passive control approach has recently been shown to be an effective technique for robust compensation of linear as well as nonlinear systems, when the system is open-loop stable and behaves as a block second-order plant. Mechanical systems as well as current space structure designs possess this property whereby inertia, damping and stiffness parameters characterize the dynamics.

Implementation of such controllers using digital systems may lead to instability, however, unless discretization issues are addressed. This paper presents a discrete-time representation of the Virtual Passive Approach and develops the robust controller structure for linear systems. The algorithm is then applied to a model of an actual experimental testbed; the test article is a ten-bay truss structure located at NASA Langley's Spacecraft Dynamics Branch. Results illustrate the robustness properties of the algorithm and expose issues which need to be addressed in future research activities.

The problem of incremental terrain acquisition is addressed in this paper. Through a systematic planning of movements in an unknown terrain filled with polygonal obstacles, a sensor-based robot is shown to be able to incrementally build the entire terrain model; the model will be described in terms of visibility graph and visibility window. The terrain model is built area by area without any overlapping between explored areas. As a consequence, the terrain is obtained as a tessellation of disjoint star polygons. And the adjacency relations between star polygons are represented by a star polygon adjacency graph (SPAG graph). The incremental exploration process consists of two basic tasks: local exploration and exploration merging. Useful lemmas are derived for these two tasks and, then, the algorithms for the tasks are given. Examples are used to illustrate the algorithms. Two strategies for planning robot movements in the unknown terrain environment are suggested and compared. They are the depth-first search and the breadth-first search applied to the SPAG graph. Finally, the performance evaluation of the method and comparison with some existing methods are presented.

This paper presents a fibre optic proximity sensor suitable for robotic applications. The sensor characteristics are first identified through experiments. Then the signal processing problem is studied for general applications where no prior knowledge of the sensor environment is assumed. The nonlinear filtering is carried out by applying a Kalman Filter. Particular attention is paid to the target nature dependence of the sensor output. Experimental results are reported by using the sensors on a real robot system.

Automated methods are developed to classify a robot's kinematic type and select an appropriate library inverse-kinematic solution based on this classification. These methods automatically generate DenavitHartenberg joint frame parameters, given any frame representation that can mathematically be represented as a homogeneous transformation.

To reduce the number of closed-form inverse-kinematics solutions required for a broad class of serial robots, additional methods account for differences in robot zero state, base frame location, and joint polarity. Further generalization results from using joint frame decoupling to map lower degree-of-freedom robots into the inverse-kinematics solutions of higher degree-offreedom robots.

Traditionally, tactile sensors have been designed using compliant, rubber-like materials; when such a sensitized gripper grasps or otherwise manipulates an object, the normal strain deformation in the compliant material is sampled. The resulting information can be used to deduce simple local geometry of the contact, but the transduction process does not typically permit use of the individual strains in determining large-scale properties of the object (e.g., the inertia). Measurements of inertial parameters of grasped objects require accurate, low-hysteresis transduction that few tactile sensors currently provide.

An alternative is to work from the task specification, and determine the tactile information that is necessary to accomplish the task. Here, we consider how to sense the length and mass of a uniform object that is gripped in a gravitational field, and show the design and assessment of a new kind of tactile sensor that is based on the theory of the deformation of thin plates. Features of this design include its potentially rugged realization, and its high-accuracy measurement that is more typical of force sensors than of tactile sensors.

This paper presents a new approach for the design of variable structure control (VSC) of nonlinear systems. The approach is based on estimation of joint acceleration signals with introduction of load estimation with the asymptotic observer. The control system is insensitive to parameter variations for a chosen switching hypersurface in conditions when it is reached by the dynamic motion with the required dynamics. The parameter insensitive response provided by this control method is demonstrated on the model of the SCARA robot. Simulation results confirm the validity of accurate tracking capability and the robust performance.

Navigation and collision avoidance are major areas of research in mobile robotics that involve varying degrees of uncertainty. In general, the problem consists of achieving sensor based motion control of a mobile robot among obstacles in structured and/or unstructured environments with collision-free motion as the priority. A fuzzy logic based intelligent control strategy has been developed here to computationally implement the approximate reasoning necessary for handling the uncertainty inherent in the collision avoidance problem. The fuzzy controller was tested on a mobile robot system in an indoor environment and found to perform satisfactorily despite having crude sensors and minimal sensory feedback.

Assembly problems require that a robot with fewer actuated degrees of freedom manipulate an environment containing a greater number of unactuated degrees of freedom. From the perspective of control theory, these problems hold considerable interest because they are characterized by the presence of non-holonomic constraints that preclude the possibility of feedback stabilization. In this sense they necessitate the introduction of a hierarchical controller. This paper explores these issues in the simple instance when all of the pieces to be assembled are constrained to lie on a line. A hierarchical controller is devised for this problem and is shown to be correct: the closed loop system achieves any desired final assembly from all initial configurations that lie in its connected component in configuration space; the generated sequence of motions never causes collisions between two pieces. Further examination of this approach interprets the controller's mediation of conflicting subgoals as promoting an M-player game amongst the pieces to be assembled.

The paper presents an approach to automatic synthesis of program for robots in a flexible manufacturing cell (FMC). The system of program generation consists of two layers: Task-Level Programming Layer and Program Interpretation and Verification Layer. The first layer uses robot-independent planning techniques to create a work plan for robots (set of elementary actions) and program for each elementary action. The second layer uses robot-dependent planning methods to plan robot's trajectories and calculate the robot's motion times. A simulation model of whole FMC, which is created based on a description of FMC and program for robots, makes possible evaluation of efficiency of FMC work.

Motion Planning and control of mobile vehicles with nonholonomic constraints are in their infancy. A systematic approach for modeling and base; motion control of a mobile vehicle is presented. A nonlinear coordinate transformation that takes into account the complete dynamics with nonholonomic constraints is used in order to obtain a linear system in space coordinates. An input-output feedback linearization inner loop is subsequently designed to transform this system into a linear-point mass system in the coordinates corresponding to the control objectives. A rigorous yet simple approach to motion planning through optimization techniques is presented for these mobile vehicles. The resulting Cartesian trajectory generated from the motion planning algorithm is employed as the reference trajectory in the outer loop, which is designed based on a Lyapunov function candidate. The net result is a base motion controller that gives capabilities to these mobile vehicles not only for tracking a Cartesian trajectory but also to achieve a desired final orientation (docking angle).

This paper focuses on developing a planning strategy for robotic assembly. At first, channel and junction are defined by using half-spaces, and free-space inside the female part is approximately decomposed by channels and junctions. Then, a simple and efficient algorithm to find the assembly path of the male part is developed, in which any path between the shortest and safest paths can be easily found by just changing the clearance gap between the male and female parts. Next, the robot arm is considered in the path planning, in which a feasible grasp angle region is obtained to avoid a collision between the robot arm and the female part during the assembly process. An optimum grasping angle can be found in the feasible grasping angle region by applying a proper performance index. Finally, a simple robotic assembly using the algorithm is numerically demonstrated.

The paper proposes a concept of a movement diagnostics system of enhanced performances. The existing system, using electromyographic (EMG) and ground reaction force signals as inputs, has been applied primarily to sports locomotion evaluation and testing, providing suitable quantitative criteria. The proposed enhancement relies on movement simulation facility, incorporating subject-specific anatomical and functional data on the neuro-musculo-skeletal system of extremities. Reflecting the principles of anthropomorphic robotics, the computer graphics simulation is conceptualized to generate artificial (synthesized) movement patterns in virtual reality.

By comparing synthesized and measured signals the suitable feedback information is provided that may be used to enhance the overall system's performance. Using the power of computer technology and computer graphics, a user-friendly system of high ergonomic potentials is thus devised, in order to enhance the quality and efficacy of diagnostic procedure. The bioengineering principles used and the merging of measurement with simulation in the process of operation define a powerful instrument applicable to both healthy and pathological movement assessment.

A motion controller for an overhead Cartesian crane in three-dimensional Euclidean (3-D) space is designed under the constraint that the control action belong to a discrete set of assigned values. The design approach rests upon a two-step procedure: first, a constraintfree motion controller is determined that satisfies the required dynamic specifications; second, this controller is replaced with an equivalent controller satisfying the discrete action constraint. The first step is implemented by means of a heuristic 3-D extension of a well-proven 2-D controller, the second step by applying recent sliding mode results. Numerical simulations illustrate the properties of the resulting feedback system under both nominal and perturbed operating conditions.