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In this paper, we present a methodology to assess system performance of human-robotic systems in achievement of collective tasks such as habitat construction, geological sampling, and space exploration.
A series of studies is conducted to develop a systematic approach to optimizing, both in terms of the distribution and scheduling of tasks, scenarios in which astronauts and robots accomplish a group of activities on the Moon, given an objective function (OF) and specific resources and constraints. An automated planning tool is developed as a key element of this optimization system.
Allocation scheme gives jobs to idle computers. Ideal resource-sharing algorithm should have following characteristics: Dynamics, decentralized, and heterogeneous. Proposed enhanced receiver-initiated dynamic algorithm (ERIDA) for resource sharing fulfills all above criteria. Provides method balancing workload among hosts, resulting in improvement in response time and throughput performance of total system. Adjusts dynamically to traffic load of each station.
Fifty six participants time shared a spacecraft environmental control system task with a realistic space robotic arm control task in either a manual or highly automated version. The former could suffer minor failures, whose diagnosis and repair were supported by a decision aid. At the end of the experiment this decision aid unexpectedly failed. We measured visual attention allocation and switching between the two tasks, in each of the eight conditions formed by manual-automated arm X expected-unexpected failure X monitoring- failure management. We also used our multi-attribute task switching model, based on task attributes of priority interest, difficulty and salience that were self-rated by participants, to predict allocation. An un-weighted model based on attributes of difficulty, interest and salience accounted for 96 percent of the task allocation variance across the 8 different conditions. Task difficulty served as an attractor, with more difficult tasks increasing the tendency to stay on task.
In situ analysis and visualization have grown increasingly popular for enabling direct access to data from high-performance computing (HPC) simulations. As a simulation progresses and interesting physical phenomena emerge, however, the data produced may become increasingly complex, and users may need to dynamically change the type and scale of in situ analysis tasks being carried out and consequently adapt the amount of resources allocated to such tasks. To date, none of the production in situ analysis frameworks offer such an elasticity feature, and for good reason: the assumption that the number of processes could vary during run time would force developers to rethink software and algorithms at every level of the in situ analysis stack. In this paper we present Colza, a data staging service with elastic in situ visualization capabilities. We demonstrate the use of Colza with the Deep Water Impact and the AMR-Wind simulations, coupling them with the ParaView Catalyst and Ascent in situ libraries, and show that Colza enables dynamic rescaling of these widely-used frameworks with no interruption to the simulation or staging service. Here, we highlight the challenges of enabling such elasticity, which requires overcoming these frameworks' reliance on MPI, using distinct engineering approaches, namely dependency injection and dependency overload. To the best of our knowledge, this work is the first to enable elastic in situ visualization capabilities for HPC applications on top of existing production analysis tools.
Allocation of decision making responsibility between pilot and computer is considered and a flight management task, designed for the study of pilot-computer interaction, is discussed. A queueing theory model of pilot decision making in this multi-task, control and monitoring situation is presented. An experimental investigation of pilot decision making and the resulting model parameters are discussed.
In a likelihood alarm display (LAD) information about event likelihood is computed by an automated monitoring system and encoded into an alerting signal for the human operator. Operator performance within a dual-task paradigm was evaluated with two LADs: a color-coded visual alarm and a linguistically coded synthetic speech alarm. The operator's primary task was one of tracking; the secondary task was to monitor a four-element numerical display and determine whether the data arose from a 'signal' or 'no-signal' condition. A simulated 'intelligent' monitoring system alerted the operator to the likelihood of a signal. The results indicated that (1) automated monitoring systems can improve performance on primary and secondary tasks; (2) LADs can improve the allocation of attention among tasks and provide information integrated into operator decisions; and (3) LADs do not necessarily add to the operator's attentional load.
Three experiments investigated subjects' ability to allocate attention and cope with task requirements under dichoptic versus binocular viewing conditions. Experiments 1 and 2 employed a target detection task in compound and noncompound stimuli, and Experiment 3 employed a relative-proximity judgment task. The tasks were performed in a focused attention condition in which subjects had to attend to the stimulus presented to one eye or field (under dichoptic and binocular viewing conditions, respectively) while ignoring the stimulus presented to the other eye or field, and in a divided attention condition in which subjects had to attend to the stimuli presented to both eyes or fields. Subjects' performance was affected by the interaction of attention conditions with task requirements, but it was generally the same under dichoptic and binocular viewing conditions. The more dependent the task was on finer discrimination, the more performance was impaired by divided attention. These results suggest that at least with discrete tasks and relatively short exposure durations, performance when each eye is presented with a separate stimulus is the same as when the entire field of stimulation is viewed by both eyes.
We consider a route planning problem in which two unmanned vehicles are required to complete a set of tasks present at distinct locations, referred to as targets, with minimum energy consumption. The mission environment is hazardous, and to ensure a safe operation, the UVs are required to communicate with each other at every target they visit. The problem objective is to determine the allocation of the tasks to the UVs and plan tours for the UVs to visit the targets such that the weighted sum of the distances traveled by the UVs and the distances traveled by the communicating signals between them is minimized. We formulate this problem as an Integer program and show that naively solving the problem using commercially available off-the-shelf solvers is insufficient in determining scalable solutions efficiently. To address this computational challenge, we develop an approximation and a heuristic algorithm, and employ them to compute high-quality solutions to a special case of the problem where equal weights are assigned to the distances traveled by the vehicles and the communicating signals. For this special case, we show that the approximation algorithm has a fixed approximation ratio of 3.75. We also develop lower bounds to the optimal cost of the problem to evaluate the performance of these algorithms on large-scale instances. We demonstrate the performance of these algorithms on 500 randomly generated instances with the number of targets ranging from 6 to 100, and show that the algorithms provide high-quality solutions to the problem swiftly; the average computation time of the algorithmic solutions is within a fraction of a second for instances with at most 100 targets. Finally, we show that the approximation ratio has a variable ratio for the weighted case of the problem. Specifically, if ρ denotes the ratio of the weights assigned to the distances representing the communication and travel costs, the algorithm has an a posteriori ratio of $3 + \frac{3ρ}{4}$ when ρ ≥ 1, and $\frac{3}{ρ}$ + $\frac{3}{4}$ when ρ ≤ 1.
A design description is given of a controller for cooperative robots. The background and motivation for multiple arm control are discussed. A set of programming primitives which permit a programmer to specify cooperative tasks are described. Motion primitives specify asynchronous motions, master/slave motions, and cooperative motions. In the context of cooperative robots, trajectory generation issues are discussed and the authors' implementation briefly described. The relations between programming and control in the case of multiple robots are examined. The allocation of various tasks among a multiprocessor computer is described.
When designing control laws for systems with constraints added to the tracking performance, control allocation methods can be utilized. Control allocations methods are used when there are more command inputs than controlled variables. Constraints that require allocators are such task as; surface saturation limits, structural load limits, drag reduction constraints or actuator failures. Most transport aircraft have many actuated surfaces compared to the three controlled variables (such as angle of attack, roll rate & angle of side slip). To distribute the control effort among the redundant set of actuators a fixed mixer approach can be utilized or online control allocation techniques. The benefit of an online allocator is that constraints can be considered in the design whereas the fixed mixer cannot. However, an online control allocator mixer has a disadvantage of not guaranteeing a surface schedule, which can then produce ill defined loads on the aircraft. The load uncertainty and complexity has prevented some controller designs from using advanced allocation techniques. This paper considers actuator redundancy management for a class of over actuated systems with real-time structural load limits using linear quadratic tracking applied to the generic transport model. A roll maneuver example of an artificial load limit constraint is shown and compared to the same no load limitation maneuver.
The paper describes the design of a controller for cooperative robots being designed at McGill University in a collaborative effort with the Jet Propulsion Laboratory. The first part of the paper discusses the background and motivation for multiple arm control. Then, a set of programming primitives, which are based on the RCCL system and which permit a programmer to specify cooperative tasks are described. The first group of primitives are motion primitives which specify asynchronous motions, master/slave motions, and cooperative motions. In the context of cooperative robots, trajectory generation issues will be discussed and the implementation described. A second set of primitives provides for the specification of spatial relationships. The relations between programming and control in the case of multiple robot are examined. Finally, the paper describes the allocation of various tasks among a set of microprocessors sharing a common bus.
An optimal control frequency response problem is defined within the context of the optimal pilot model. The problem is designed to specify pilot model control frequencies reflective of important aircraft system properties, such as control feel system dynamics, airframe dynamics, and gust environment, as well as man machine properties, such as task and attention allocation. This is accomplished by determining a bounded set of control frequencies which minimize the total control cost. The bounds are given by zero and the neuromuscular control frequency response for each control actuator. This approach is fully adaptive, i.e., does not depend upon user entered estimates. An algorithm is developed to solve this optimal control frequency response problem. The algorithm is then applied to an attitude hold task for a bare airframe fighter aircraft case with interesting dynamic properties.
The following subject areas are covered: object-oriented programming task; rule-based programming task; algorithms for resource allocation; connecting a Symbolics to a VAX; FORTRAN from Lisp; trees and forest task; software data structure conversion; software functionality modifications and enhancements; portability of resource allocation to a TI MicroExplorer; frontier of feasibility software system; and conclusions.
It is proposed that human decision making in many multi-task situations might be modeled in terms of the manner in which the human detects events related to his tasks and the manner in which he allocates his attention among his tasks once he feels events have occurred. A model of human event detection performance in such a situation is presented. An assumption of the model is that, in attempting to detect events, the human generates the probability that events have occurred. Discriminant analysis is used to model the human's generation of these probabilities. An experimental study of human event detection performance in a multiple process monitoring situation is described and the application of the event detection model to this situation is addressed. The experimental study employed a situation in which subjects simulataneously monitored several dynamic processes for the occurrence of events and made yes/no decisions on the presence of events in each process. Input to the event detection model of the information displayed to the experimental subjects allows comparison of the model's performance with the performance of the subjects.
It is pointed out that a controversy exists concerning the required role of man, and his attendant skills and levels of skills, for Sortie Lab operations. As a result, a study was conducted to generate a taxonomy of candidate crew roles which would: (1) be applicable across all experiments, and (2) be usable for Sortie scientists and engineers in determination of level of skill as well as type of skill. Nine basic roles were identified in the study, and the tasks associated with each were developed from a functional description of a generalized in-flight experiment. The functional analysis comprised the baseline for establishment of crew roles, with roles being defined as combinations of tasks, associated skills, and knowledges. A role classification scheme was developed in which the functions and tasks identified were allocated to each of the nine role types. This classification scheme is presented together with the significant results of the study.
A unified formulation of computer-aided, multi-task, decision making is presented. Strategy for the allocation of decision making responsibility between human and computer is developed. The plans of a flight management systems are studied. A model based on the queueing theory was implemented.
The SIFT instrumentation is called the "Window." This window was designed to collect internal data from SIFT while having minimal overhead. Window consists of Sender and Relay components. Sender is to be run on processors 0..5 and Relay will run on processor 6. Sender will gather values (currently 12) during the subframe allocated to a task and broadcast these values at the start of the next subframe. This timing was selected to guarantee Relay 3.2ms to collect and transmit the data.