The mass balancing of robotic manipulators has been shown to have favorable effects on the dynamic characteristics. In actual practice, however, since conventional manipulators have flexibility at their joints, the improved dynamic properties obtainable for rigid manipulators may be influenced by those joint flexibilities. This paper investigates the effects of the joint flexibility on the dynamic properties and the controlled performance of a balanced robotic manipulator. The natural frequency distribution and damping characteristics were investigated through frequency response analyses. To evaluate the dynamic performance a series of simulation studies of the open loop dynamics were made for various trajectories, operating velocities, and joint stiffnesses. These simulations were also carried out for the balanced manipulator with a PD controller built-in inside motor control loop. The results show that, at low speed, the joint flexibility nearly does not influence the performance of the balanced manipulator, but at high speed it tends to render the balanced manipulator susceptible to vibratory motion and yields large joint deformation error.

In this paper we present a series of haptic exploratory procedures, or EPs, implemented for a multi-fingered, articulated, sensate robot hand. These EPs are designed to extract specific tactile and kinesthetic information from an object via their purposive invocation by an intelligent robotic system. Taken together, they form an active robotic touch perception system to be used both in extracting information about the environment for internal representation and in acquiring grasps for manipulation. The theory and structure of this robotic haptic system is based upon models of human haptic exploration and information processing.

The haptic system presented utilizes an integrated robotic system consisting of a PUMA 560 robot arm, a JPL/Stanford robot hand, with joint torque sensing in the fingers, a wrist force/torque sensor, and a 256 element, spatially-resolved fingertip tactile array. We describe the EPs implemented for this system and provide experimental results which illustrate how they function and how the information which they extract may be used. In addition to the sensate hand and arm, the robot also contains structured-lighting vision and a Prolog-based reasoning system capable of grasp generation and object categorization. We present a set of simple tasks which show how both grasping and recognition may be enhanced by the addition of active touch perception.

The accurate modelling of robot dynamics is essential for the design of model-based robot controllers. However, dynamic models have very complicated features which can be attributed to several reasons. For example, the continuously-varying arm configuration, uncertain effects of load handling on the dynamic stability of the arm, and the high degree of non-linearity and coupling exhibited between the different links. Hence, the accurate modelling of these effects will play an important role in the design of robust controllers. Towards this end, an efficient and fast method for the on-line tuning of robot dynamic parameters must be devised. This work proposes to solve this problem as follows. First, a simplified dynamic model of the robot is developed. The model allows for direct and straightforward extraction and regrouping of dynamic parameters. The resulting dynamic parameters are formulated as a regression equation which is linear in the dynamic parameters. Finally, the algorithm is executed using a Transputer development system to speed up the computation and meet real-time constraints. The efficiency of the approach is demonstrated by a case study.

In this paper, we discuss the accurate and robust sliding mode tracking control for highly nonlinear robot manipulators using a disturbance observer. Due to the modeling error or environmental uncertainties, sliding mode control may present a significant chattering problem by using a conventional Sliding Mode Control (SMC) which can obtain the desired tracking performance, because the controller design is carried in the uncertainty space of the system parameters.

To solve this chattering problem, the efficient compensation of the disturbance observer has been introduced. Thus the design of the proposed SMC is not influenced by the modeling error or parameter uncertainties.

To ensure bounded stability, the proposed sliding mode controls have been analysed in a preliminary lemma and two main theorems. The stability of the proposed control method is proved in this paper, and the efficiency of the control algorithms has been demonstrated by simulations for a position tracking control of a two-link robot subject to parameter and payload uncertainties.

A new method of optimal cost trajectory planning based on a graph searching algorithm is presented. Various heuristic functions which play the key role in the construction of effective algorithms for solving such problems are proposed. Some numerical examples are given based on the kinematic and dynamic models of a IRb-6 ASEA robot. This method rooted in AI has less computational complexity than a dynamical programming method applying to the same optimal-cost trajectory planning problem.

We have suggested a novel approach to autonomously navigate a full sized autonomous vehicle that separately treats vehicle control and obstacle detection. In this paper we discuss the vehicle control that has enabled our autonomous vehicle to travel at speeds upto 20mph. We point out the limitations of existing schemes that restrict their consideration to kinematic models and show that it is possible to obtain an increase in performance through the use of approximate dynamical models that capture first–order effects. Our approach combines such a modeling philosophy with accurate feedback in world coordinates from sensors that have only recently become available. Experimental results of our implementation on NavLab, a modified van at CMU, are presented.

A fuzzy qualitative model of a robot sensor, presented in this paper, is for locating 3-D objects, and the location information is used to guide the movements of an industrial robot to pick up the objects. The sensor consists of a rigid platform mounted on a flexible column. Each object to be located is held rigidly with respect to the platform. The static deflections of the column and natural frequencies of vibration of the dynamic system comprising the object, platform and column are measured and processed using a mathematical model of the system to determine the location of the object. In practice, the frequency measurements have low repeatability, which leads to inconsistent location information. Also, when the orientation is in the region 80°–90° relative to a reference axis of the sensor, the mathematical model becomes ill-conditioned. In this paper, a fuzzy qualitative model of the sensor is described. The fuzzy model is designed to yield the orientation in the region where the mathematical model is unusable. Different stages for constructing the fuzzy model are described. The on-line implementation of the model is outlined and the experimental results obtained are presented.

An algorithm for kinematic motion planning of redundant planar robots, having revolute joints, in an unknown dynamic environment is presented. Distance ranging sensors, mounted on the body of each manipulator link, are simulated here to estimate the proximity of an obstacle. The sensory data is analyzed through a fuzzy controller which estimates whether a collision is imminent, and if so, employs a geometric approach to compute the joint movements necessary to avoid the collision. Obstacles can sometimes move uncompromisingly in the environment attempting a deliberate collision. Strategies to deal with such cases are presented and recovery procedures to circumvent the obstacle from tight corners are suggested. Cases of link overlap have been avoided by considering each link as a body which is sensed as an obstacle by every other link of the same manipulator. Suitable examples are presented to demonstrate the algorithm.