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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.
The paper presents experimental results that demonstrate a new approach to real-time collision avoidance for 7-DOF arms.
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.
The Nation uses water-level data for a variety of practical purposes, including nautical charting, maritime navigation, hydrography, coastal engineering, and tsunami and storm surge warnings. Long-term applications include marine boundary determinations, tidal predictions, sea-level trend monitoring, oceanographic research, and climate research. Accurate and timely information concerning sea-level height, tide, and ocean current is needed to understand their impact on coastal management, disaster management, and public health. Satellite altimeter data products are currently used by hundreds of researchers and operational users to monitor ocean circulation and to improve scientists understanding of the role of the oceans in climate and weather. The NOAA (National Oceanic and Atmospheric Administration) National Ocean Service has been monitoring sea-level variations for many years. NOAA s PORTS (Physical Oceanographic Real-Time System) DST (decision support tool), managed by the Center for Operational Oceanographic Products and Services, supports safe and cost-efficient navigation by providing ship masters and pilots with accurate real-time information required to avoid groundings and collisions. This report assesses the capacity of NASA s satellite altimeter data to meet societal decision support needs through incorporation into NOAA s PORTS. NASA has a long heritage of collecting data for ocean research, including its current Terra and Aqua missions. Numerous other missions provide additional important information for coastal management issues, and data collection will continue in the coming decade with such missions as the OSTM (Ocean Surface Topography Mission). OSTM will provide data on sea-surface heights for determining ocean circulation, climate change, and sea-level rise. We suggest that NASA incorporate OSTM altimeter data (C- and Ku-band) into NOAA s PORTS DST in support of NASA s Coastal Management National Application with secondary support to the Disaster Management and Public Health National Applications.
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.
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.
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 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.
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.
Global connectivity enables effective coordination in Multi-Agent Systems (MAS). Solving these connection problems under hardware constraints is an NP-hard non-Euclidean Degree Constrained Minimum Spanning Tree (DCMST) problem. Prior MAS controllers coordinate team movement for task completion and collision avoidance; some considering Line-of-Sight (LOS) maintenance but prioritizing flexibility over guarantees. Evolutionary Algorithms (EA) have been shown to find good solutions for DCMST, but their performance degrades with larger populations required to support a large MAS. We present a method based on edge graph attention networks, trained offline to reduce online computation times. Empirical comparisons with greedy polynomial-time solvers and EA show that our method leverages latent graph information to consistently find constraint-satisfying solutions in less time.
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.
Safe and optimal motion control for connected and automated vehicles (CAVs) poses a fundamental optimization challenge at the intersection of system complexity, environmental uncertainty, and stringent real-time constraints. Existing surveys address this challenge in isolation – focusing either on specific control techniques or individual uncertainty sources – without providing a unified framework that characterizes the trade-offs among computational tractability, performance verifiability, and adaptive generalization across paradigms. This review addresses that gap by presenting a cohesive analytical framework concentrated on the decision-making and trajectory optimization layers of the CAV autonomy stack. We systematically analyze three major optimization paradigms – first-principles model-based optimization, data-driven methods, and hybrid synergistic architectures – evaluating each against four core complexity axes: problem formulation, constraint handling, optimality guarantees, and robustness. Key applications including platooning, trajectory planning, collision avoidance, and cooperative control are examined to reveal recurring methodological patterns and critical operational constraints that limit real-world performance. Our synthesis identifies verifiable hybrid architectures, incentive-aligned multi-agent cooperation, and hardware-algorithm co-design as the defining research frontiers, and distills a targeted agenda for developing CAV control systems that are simultaneously safe, computationally efficient, and deployable in the full complexity of real-world traffic environments.
A responsibility of the Trajectory Operations Officer is to ensure that the International Space Station (ISS) avoids colliding with debris. United States Space Command (USSPACECOM) tracks and catalogs a portion of the debris in Earth orbit, but only objects with a perigee less than 600 km and a radar cross section (RCS) greater than 10 cm-objects that, in fact, represent only a small fraction of the objects in Earth orbit. To accommodate for this, the ISS uses shielding to protect against collisions with smaller objects. This study provides a better understanding of how quickly, and to what degree, USSPACECOM projections tend to converge to the final, true miss distance. The information included is formulated to better predict the behavior of miss distance data during real-time operations. It was determined that the driving components, in order of impact on miss distance fluctuations, are energy dissipation rate (EDR), RCS, and inclination. Data used in this analysis, calculations made, and conclusions drawn are stored in Microsoft Excel log sheets. A separate log sheet, created for each conjunction, contains information such as predicted miss distances, apogee and perigee of debris orbit, EDR, RCS, inclination, tracks and observations, statistical data, and other evaluation/orbital parameters.
Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.
One problem, which is comparatively new in the field of GPS applications, is the determination of the relative trajectories of space vehicles. Applications include the docking of spacecraft, collision avoidance in the area of space stations, and trajectory reconstruction of multiple payloads. The required precision in any of these applications will vary, according to the requirements of the task and abilities of GPS to cope with the environment and the dynamics. This paper describes the post-mission reconstruction of the relative trajectories of four GPS receivers attached to four payloads jettisoned from a Black Brant XII rocket. This vehicle was launched by the National Aeronautics and Space Administration (NASA) in January 1999 from the Poker Flats Research Range near Fairbanks, Alaska. The Black Brant XII is a sub-orbital rocket designed to carry payloads of 100 to 500 kg into the upper atmosphere. Flight time is generally in the order of 10-20 minutes. In this experiment, a GPS receiver and antenna was attached to each of the four payloads. One of the GPS receivers was assigned as the "base station", while the other 3 receivers were designated as remotes. GPS time, code and phase measurements were telemetered to a ground station for real-time processing and storage. The object of the mission was to re-compute the position and velocity of the remote units with respect to the base station during the launch phase and after the payloads separated. During the launch segment the 3 baseling distances between the 4 antennas are known from plans and are constant values until each payload is released. On the fly ambiguity determination was used to establish local coordinates from the base antenna to each of the other 3 GPS units during flight. Distance computations were made from the GPS-derived coordinates and compared to plan distances. Using this methodology an error analysis of the relative GPS accuracies has been presented and in addition a description given of the respective payload behaviour following separation from the vehicle.