Coordinated Task Execution Research Articles

Cognition and communication: situational awareness and tie preservation in disrupted task environments

AbstractIndividuals filling specialized, interdependent organizational roles achieve coordinated task execution through effective communication channels. Such channels enable regular access to information, opportunities, and assistance that may enhance one’s understanding of the task environment. However, the time and effort devoted to maintaining those channels may detract from one’s duties by turning attention away from the task environment. Disrupted task environments increase information requirements, thus creating a dilemma in which individuals must sustain benefits offered by important communication channels and relieve burdens imposed by ineffective channels. Using separable temporal exponential random graph models (STERGMs), this paper examines the relationship between situational awareness (SA) and the propensity to sustain or dissolve preexisting communication channels during 10 disruptive events experienced sequentially by a large, multifaceted military organization during a 2-week training exercise. Results provide limited evidence that increased SA detracts from tie preservation; instead SA begins to predict tie preservation during the second week of the exercise. Patterns of organizational adaptation reveal that, over time, improvised coordinative roles increasingly fall upon those with elevated SA. These results suggest that over successive disruptions, the benefits of information provided by communication channels within interdependent, role-specialized organizations begin to outweigh the costs of sustaining those channels.

A distributed three-channel wireless Functional Electrical Stimulation system for automated triggering of stimulation to enable coordinated task execution by patients with neurological disease

Functional Electrical Stimulation (FES) is a technique used to improve mobility and function for patients suffering some neurological related diseases such us Multiple Sclerosis (MS) and stroke. Some patients might require FES applied in more than one location depending on the extent of the neurological condition. Currently, this can be achieved using multi-channel FES systems. However, these systems can be bulky and impractical in daily usage. This research investigates using a wireless distributed FES system to overcome some of the limitations of the current multi-channel systems. A prototype of a three-channel FES system was built and tested. The prototype is used for drop foot stimulation and reciprocal arm swing stimulation while the user is walking, and for elbow extension and wrist/fingers opening stimulation if triggered while standing or sitting. A pilot study was designed to evaluate the reliability and repeatability of the system with 11 healthy volunteers without applying stimulation. This was followed by a case study with a hemiplegic person. The results indicate that the system can successfully detect and generate output responses appropriate to the input signals from the body sensors.

MAHRU-M: A mobile humanoid robot platform based on a dual-network control system and coordinated task execution

This paper introduces a mobile humanoid robot platform able to execute various services for humans in their everyday environments. For service in more intelligent and varied environments, the control system of a robot must operate efficiently to ensure a coordinated robot system. We enhanced the efficiency of the control system by developing a dual-network control system. The network system consists of two communication protocols: high-speed IEEE 1394, and a highly stable Controller Area Network (CAN). A service framework is also introduced for the coordinated task execution by a humanoid robot. To execute given tasks, various sub-systems of the robot were coordinated effectively by this system. Performance assessments of the presented framework and the proposed control system are experimentally conducted. MAHRU-M, as a platform for a mobile humanoid robot, recognizes the designated object. The object’s pose is calculated by performing model-based object tracking using a particle filter with back projection-based sampling. A unique approach is used to solve the human-like arm inverse kinematics, allowing the control system to generate smooth trajectories for each joint of the humanoid robot. A mean-shift algorithm using bilateral filtering is also used for real-time and robust object tracking. The results of the experiment show that a robot can execute its services efficiently in human workspaces such as an office or a home.

Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach

Recent years are seeing an increasing need for on-line monitoring of teams of cooperating agents, e.g., for visualization, or performance tracking. However, in monitoring deployed teams, we often cannot rely on the agents to always communicate their state to the monitoring system. This paper presents a non-intrusive approach to monitoring by 'overhearing', where the monitored team's state is inferred (via plan-recognition) from team-members' *routine* communications, exchanged as part of their coordinated task execution, and observed (overheard) by the monitoring system. Key challenges in this approach include the demanding run-time requirements of monitoring, the scarceness of observations (increasing monitoring uncertainty), and the need to scale-up monitoring to address potentially large teams. To address these, we present a set of complementary novel techniques, exploiting knowledge of the social structures and procedures in the monitored team: (i) an efficient probabilistic plan-recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting knowledge of the team's social behavior to predict future observations during execution (reducing monitoring uncertainty); and (iii) monitoring algorithms that trade expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any number of agents and their different activities to be represented in a single coherent entity. We present an empirical evaluation of these techniques, in combination and apart, in monitoring a deployed team of agents, running on machines physically distributed across the country, and engaged in complex, dynamic task execution. We also compare the performance of these techniques to human expert and novice monitors, and show that the techniques presented are capable of monitoring at human-expert levels, despite the difficulty of the task.