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A new approach to real-time collison avoidance for dexterous 7-DOF arms and supportive simulation and experimental results are presented. The collision avoidance problem is formulated and solved as a force control problem.
Collision free robot manipulator maneuvers for transferring a payload from one location to another in the presence of obstacles are discussed. Such maneuvers are executed by means of two algorithms running in parallel. The first algorithm performs an on-line, one step ahead acceleration optimization, with an adaptive N-step ahead precaution measure to prevent overshooting the specified forbidden joint coordinates. The second algorithm, an on-line planning algorithm, at the same time, selects the current target state and the current forbidden joint coordinates for the first algorithm. Since the task is a point to point transfer movement, the problem is dealt in robot joint space directly and the kinematic problem can be bypassed completely. A simulation example using a 2 degree-of-freedom planner robot is given.
A hybrid robot teleoperation system is presented which makes use of the methodology of motion planning for whole-sensitive robots to assist the operator in generating collision-free motion in a master-slave robot arm manipulator system. The system combines operator commands with data from the sensitive skin to guarantee safe motion for the entire body of the robot arm. The arm avoids obstacles automatically and in real time and moves in a collision-free manner although no prior knowledge of the objects in the environment is available to the motion planning system and no constraints are imposed on the obstacle shapes. The operator is thus relieved of the task of providing safety of the robot arm and surrounding objects.
A data structure and update algorithm are presented for a prototype real-time collision avoidance safety system simulating a multirobot workspace. The data structure is a variant of the octree, which serves as a spatial index. An octree recursively decomposes 3D space into eight equal cubic octants until each octant meets some decomposition criteria. The N-objects octree, which indexes a collection of 3D primitive solids is used. These primitives make up the two (seven-degrees-of-freedom) robot arms and workspace modeled by the system. As robot arms move, the octree is updated to reflect their changed positions. During most update cycles, any given primitive does not change which octree nodes it is in. Thus, modification to the octree is rarely required. Cycle time for interpreting current arm joint angles, updating the octree to reflect new positions, and detecting/reporting imminent collisions averages 30 ms on an Intel 80386 processor running at 20 MHz.
A data structure and update algorithm are presented for a prototype real time collision detection safety system for a multi-robot environment. The data structure is a variant of the octree, which serves as a spatial index. An octree recursively decomposes 3-D space into eight equal cubic octants until each octant meets some decomposition criteria. The octree stores cylspheres (cylinders with spheres on each end) and rectangular solids as primitives (other primitives can easily be added as required). These primitives make up the two seven degrees-of-freedom robot arms and environment modeled by the system. Octree nodes containing more than a predetermined number N of primitives are decomposed. This rule keeps the octree small, as the entire environment for the application can be modeled using a few dozen primitives. As robot arms move, the octree is updated to reflect their changed positions. During most update cycles, any given primitive does not change which octree nodes it is in. Thus, modification to the octree is rarely required. Incidents in which one robot arm comes too close to another arm or an object are reported. Cycle time for interpreting current joint angles, updating the octree, and detecting/reporting imminent collisions averages 30 milliseconds on an Intel 80386 processor running at 20 MHz.
The Telerobot Testbed is a hierarchically distributed processing system which is linked together through a standard, commercial Ethernet. Standard Ethernet systems are primarily designed to manage non-real-time information transfer. Therefore, collisions on the net (i.e., two or more sources attempting to send data at the same time) are managed by randomly rescheduling one of the sources to retransmit at a later time interval. Although acceptable for transmitting noncritical data such as mail, this particular feature is unacceptable for real-time hierarchical command and control systems such as the Telerobot. Data transfer and scheduling simulations, such as token ring, offer solutions to collision management, but do not appropriately characterize real-time data transfer/interactions for robotic systems. Therefore, models like these do not provide a viable simulation environment for understanding real-time network loading. A real-time network loading model is being developed which allows processor-to-processor interactions to be simulated, collisions (and respective probabilities) to be logged, collision-prone areas to be identified, and network control variable adjustments to be reentered as a means of examining and reducing collision-prone regimes that occur in the process of simulating a complete task sequence.
Robust vision capability for intelligent control systems has been an elusive goal in image processing. The computationally intensive techniques a necessary for conventional image processing make real-time applications, such as object tracking and collision avoidance difficult. In order to endow an intelligent control system with the needed vision robustness, an adequate image enhancement subsystem capable of compensating for the wide variety of real-world degradations, must exist between the image capturing and the object recognition subsystems. This enhancement stage must be adaptive and must operate with consistency in the presence of both statistical and shape-based noise. To deal with this problem, we have developed an innovative algebraic approach which provides a sound mathematical framework for image representation and manipulation. Our image model provides a natural platform from which to pursue dynamic scene analysis, and its incorporation into a vision system would serve as the front-end to an intelligent control system. We have developed a unique polynomial representation of gray level imagery and applied this representation to develop polynomial operators on complex gray level scenes. This approach is highly advantageous since polynomials can be manipulated very easily, and are readily understood, thus providing a very convenient environment for image processing. Our model presents a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets.
Fast graphics workstations and increased computing power, together with improved interface technologies, have created new and diverse possibilities for developing and interacting with synthetic environments. A synthetic environment system is generally characterized by input/output devices that constitute the interface between the human senses and the synthetic environment generated by the computer; and a computation system running a real-time simulation of the environment. A basic need of a synthetic environment system is that of giving the user a plausible reproduction of the visual aspect of the objects with which he is interacting. The goal of our Shastra research project is to provide a substrate of geometric data structures and algorithms which allow the distributed construction and modification of the environment, efficient querying of objects attributes, collaborative interaction with the environment, fast computation of collision detection and visibility information for efficient dynamic simulation and real-time scene display. In particular, we address the following issues: (1) A geometric framework for modeling and visualizing synthetic environments and interacting with them. We highlight the functions required for the geometric engine of a synthetic environment system. (2) A distribution and collaboration substrate that supports construction, modification, and interaction with synthetic environments on networked desktop machines.
A new on-line control strategy for sensor-based collision avoidance of manipulators and supporting experimental results are presented in this article. This control strategy is based on nullification of virtual forces applied to the end-effector by a hypothetical spring-plus-damper attached to the object's surface. In the proposed approach, the real-time arm control software continuously monitors the object distance measured by the arm-mounted proximity sensors. When this distance is less than a preset threshold, the collision avoidance control action is initiated to inhibit motion toward the object and thus prevent collision. This is accomplished by employing an outer feedback loop to perturb the end-effector nominal motion trajectory in real-time based on the sensory data. The perturbation is generated by a proportional-plus-integral (PI) collision avoidance controller acting on the difference between the sensed distance and the preset threshold. This approach is computationally very fast, requires minimal modification to the existing manipulator positioning system, and provides the manipulator with an on-line collision avoidance capability to react autonomously and intelligently. A dexterous RRC robotic arm is instrumented with infrared proximity sensors and is operated under the proposed collision avoidance strategy. Experimental results are presented to demonstrate end-effector collision avoidance both with an approaching object and while reaching inside a constricted opening.
This paper describes the design and implementation of a real-time control system with multiple modes of operation for a mobile redundant dexterous manipulator. The paper describes the hardware and software components of the VME bus environment. Experimental results on real-time control of the Robotics Research arm using the manipulator control system are also presented in the paper.
An overview of Automation Technology Branch facilities and research is presented. Manipulator research includes dual-arm coordination studies, space manipulator dynamics, end-effector controller development, automatic space structure assembly, and the development of a dual-arm master-slave telerobotic manipulator system. Sensor research includes gravity-compensated force control, real-time monovision techniques, and laser ranging. Artificial intelligence techniques are being explored for supervisory task control, collision avoidance, and connectionist system architectures. A high-fidelity dynamic simulation of robotic systems, ROBSIM, is being supported and extended. Cooperative efforts with Oak Ridge National Laboratory have verified the ability of teleoperators to perform complex structural assembly tasks, and have resulted in the definition of a new dual-arm master-slave telerobotic manipulator. A bibliography of research results and a list of technical contacts are included.
This paper describes an integrated planning model for the U.S. air traffic system. The approach incorporates the dual objectives of monitoring collision risk while minimizing transportation costs. Specialized solution algorithms exploit the underlying structure of the model - especially for large-scale examples. The proposed formulation is tested with real-world data for the Indianapolis control sector. Additional experiments with a CRAY X-MP/24 supercomputer show that a full-scale model can be solved under real time conditions. Despite these advances, additional work is required in developing a practical system. Suggestions are made for combining advances in computer graphics and mathematical modeling.
The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.
Graphics simulation and real-time implementation of configuration control schemes for a redundant 7-DOF Robotics Research arm are described. The arm kinematics and motion control schemes are described briefly. This is followed by a description of a graphics simulation environment for 7-DOF arm control on the Silicon Graphics IRIS Workstation. Computer simulation results are presented to demonstrate elbow control, collision avoidance, and optimal joint movement as redundancy resolution goals. The laboratory setup for experimental validation of motion control of the 7-DOF Robotics Research arm is then described. The configuration control approach is implemented on a Motorola-68020/VME-bus-based real-time controller, with elbow positioning for redundancy resolution. Experimental results demonstrate the efficacy of configuration control for real-time control.
Remote manipulator control experiments have been conducted to investigate the problems related to the 'controlled collision' aspect of manipulation. A six degrees of freedom pantograph arm with a six-dimensional force-torque balance sensor and microprocessor-driven real-time graphics display are employed in the experiments. Among other results, it is found that the use of computer force-torque feedback loop for 'stop at contact' provides a nearly linear relation between load transfer to jigs at 'stop at contact', rate of motion, and preset force-torque threshold values for stop under limited motion and load conditions.
The AUTOMAN computer program develops automated maneuvering decisions for helicopters during air-to-air combat over hilly terrain. Recently, the capabilities of this program have been extended and enhanced significantly. The revised program was installed at the NASA Ames manned flight-simulation facility to drive a computer-generated image of an enemy helicopter, thereby providing an adversary for the human pilot. Maneuvers are selected by employing game theory. Enhancements include a guidance law for target acquisition when a firing opportunity arises; fire-control sequence logic; improved low-flying capabilities; line-of-sight computations for the cockpit field-of-view, terrain obstructions, and visual range limits; use of terrain for masking; air-to-air collision-avoidance maneuvers; decision on dispensing flares and chaff; and adjustable levels of pilot experience. The program was found to be extremely useful for both rotorcraft handling-quality evaluations and air-to-air combat training.
Scientific research associates advanced robotic system (SRAARS) is an intelligent robotic system which has autonomous learning capability in geometric reasoning. The system is equipped with one global intelligence center (GIC) and eight local intelligence centers (LICs). It controls mainly sixteen links with fourteen active joints, which constitute two articulated arms, an extensible lower body, a vision system with two CCD cameras and a mobile base. The on-board knowledge-based system supports the learning controller with model representations of both the robot and the working environment. By consecutive verifying and planning procedures, hypothesis-and-test routines and learning-by-analogy paradigm, the system would autonomously build up its own understanding of the relationship between itself (i.e., the robot) and the focused environment for the purposes of collision avoidance, motion analysis and object manipulation. The intelligence of SRAARS presents a valuable technical advantage to implement robotic systems for space exploration and space station operations.
Many researchers have shown that the development and evolution of electrical discharges within convective clouds is fundamentally related to the growth and dynamics of precipitation particles aloft. In the presence of strong updrafts above the freezing level collisions among mixed-phase particles (i.e., hail. ice, supercooled water) promote the necessary charge separation needed to initiate intra-cloud lightning. A precipitation core that descends below the freezing level is often accompanied by a change in the electrical structure of the cloud. Consequently, more Cloud-to-Ground (CG) than Intra-Cloud (IC) lightning flashes appear. Descending precipitation cores can also play a significant role in the evolution of mesoscale features at the surface (e.g., microbursts, downbursts) because of latent heat and mass loading effects of water and ice. For this reason, some believe that lightning and microbursts are fundamentally linked by the presence of ice particles in thunderstorms. Several radar and lightning studies of microburst thunderstorms from COHMEX in 1986 showed that the peak IC lightning systematically occurred ten minutes before the onset of a microburst. In contrast, most CG lightning occurred at the time of the microburst. Many of the preceding studies have been done using high-resolution research radars and experimental lightning detection systems in focused field projects. In addition, these studies could only determine the vertical origin or occurrence of IC lightning, and not a true three-dimensional representation. Currently, the WSR-88D radar system and a real-time, state-of-the-art lightning system (LDAR) at the Kennedy Space Center (KSC) in Florida provide an opportunity to extend these kinds of studies in a more meaningful operational setting.