This paper proposes a path-planning method for mobile robots in the presence of uncertainty. We analyze environment and control uncertainty and propose methods for incorporating each of them into the planning algorithm. We model the environment using the pyramid structure that encodes the information on occupancy probabilities for each pixel as well as the partial information on conditional probabilities among different pixels. This structure allows for efficient and accurate computation of collision probabilities in the presence of environment uncertainty. The control uncertainty is mainly characterized by its expansion in space and time and is accordingly modeled by a stochastic differential equation that mathematically captures this phenomenon. Models that we develop are inevitably approximate but experiments confirm that they can be used as a reasonable model for motion planning. We have conducted a series of experiments on the mobile platform and some of these results are presented.>

An attempt to achieve a high level of interaction between a real-time vision system capable of tracking moving objects in 3-D and a robot arm with gripper that can be used to pick up a moving object is described. The interplay of hand-eye coordination in dynamic grasping tasks such as grasping of parts on a moving conveyor system, assembly of articulated parts, or for grasping from a mobile robotic system is explored. The goal is to build an integrated sensing and actuation system that can operate in dynamic as opposed to static environments. The system built addresses three distinct problems in using robotic hand-eye coordination for grasping moving objects: fast computation of 3-D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system operates at approximately human arm movement rates. Experimental results in which a moving model train is tracked, stably grasped, and picked up by the system are presented. The algorithms developed to relate sensing to actuation are quite general and applicable to a variety of complex robotic tasks.>

The authors explore the requirements for grasping a moving object. This task requires proper coordination between at least three separate subsystems: real-time vision sensing, trajectory-planning/arm-control, and grasp planning. As with humans, the system first visually tracks the object's 3D position. Because the object is in motion, this must be done in real-time to coordinate the motion of the robotic arm as it tracks the object. The vision system is used to feed an arm control algorithm that plans a trajectory. The arm control algorithm is implemented into two steps: filtering and prediction and kinematic transformation computation. Once the trajectory of the object is tracked, the hand must intercept the object to actually grasp it. Experimental results are presented in which which a moving model train was tracked, stably grasped, and picked up by the system.>

A real-time tracking algorithm in conjunction with a predictive filter to allow real-time visual servoing of a robotic arm that is tracking a moving object is described. The system consists of two calibrated (but unregistered) cameras that provide images to a real-time, pipeline-parallel optic-flow algorithm that can robustly compute optic-flow and calculate the 3-D position of a moving object at approximately 5-Hz rates. These 3-D positions of the moving object serve as input to a predictive kinematic control algorithm that uses an alpha - beta - gamma filter to update the position of a robotic arm tracking the moving object. Experimental results are presented for the tracking of a moving model train in a variety of different trajectories.>

The structure of SYM, a program package for manipulator modeling, control law synthesis, and simulation, is described. SYM's research and educational aims are emphasized. The control law synthesis in symbolic form and the system simulation are discussed in detail. Several examples of SYM outputs which depict the main steps in the manipulator control system creation process, namely, manipulator structure as a 3-D scheme, control law definition form, and system simulation results as 2-D plots are presented.>

An algorithm is presented for generating numerically efficient manipulator models in symbolic form, i.e., as computer programs written in a high-level language such as C or FORTRAN. The algorithm does its job in two main steps. First, a suboptimal symbolic model, which is the input for the second step, is generated. The first step has been explained in the authors' previous papers (1989). The main emphasis in this study is put on the second step: generating analytical expressions of output variables in the form of so-called structural matrices and their reconstruction into a program again.>

The authors address the problem of the optimal evaluation of robot inverse dynamics on array processors. The inverse dynamics models used are the symbolic customized models with near-minimum numerical complexity, which are computer-generated given the robot arm parameters. Such models represent the input for a proposed scheduling algorithm that distributes the computation of the model over several multipliers and adders. The scheduling algorithm is automatic and minimizes the number of microcycles. The algorithm was tested on several standard robots, and processor efficiency up to 84% was achieved. Experimental results on a 30 MFLOPS array processor showed that the inverse dynamics of a three-link PUMA-like robot requires 25.5 mu s. For a six-link robot, the computation of the inverse dynamics and the control law takes about 100 mu s on this processor. Thus, the control of high-speed robots could be achieved by attaching low-cost array processors (10 MFLOPS) to the control processor of the robot controller.>