Schedulability Analysis of Task Sets with Upper- and Lower-Bound Temporal Constraints

Increasingly, real-time systems must handle the self-suspension of tasks (that is, lower-bound wait times between subtasks) in a timely and predictable manner. A fast schedulability test that does not significantly overestimate the temporal resources needed to execute self-suspending task sets would be of benefit to these modern computing systems. In this paper, a polynomial-time test is presented that is known to be the first to handle nonpreemptive self-suspending task sets with hard deadlines, where each task has any number of self-suspensions. To construct the test, a novel priority scheduling policy is leveraged, the jth subtask first, which restricts the behavior of the self-suspending model to provide an analytical basis for an informative schedulability test. In general, the problem of sequencing according to both upper-bound and lower-bound temporal constraints requires an idling scheduling policy and is known to be nondeterministic polynomial-time hard. However, the tightness of the schedulability test and scheduling algorithm are empirically validated, and it is shown that the processor is able to effectively use up to 95% of the self-suspension time to execute tasks.Boeing Scientific Research LaboratoriesNational Science Foundation (U.S.). Graduate Research Fellowship (Grant 2388357

Fast methods for scheduling with applications to real-time systems and large-scale, robotic manufacturing of aerospace structures

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 113-117).Across the aerospace and automotive manufacturing industries, there is a push to remove the cage around large, industrial robots and integrate right-sized, safe versions into the human labor force. By integrating robots into the labor force, humans can be freed to focus on value-added tasks (e.g. dexterous assembly) while the robots perform the non-value-added tasks (e.g. fetching parts). For this integration to be successful, the robots need to ability to reschedule their tasks online in response to unanticipated changes in the parameters of the manufacturing process. The problem of task allocation and scheduling is NP-Hard. To achieve good scalability characteristics, prior approaches to autonomous task allocation and scheduling use decomposition and distributed techniques. These methods work well for domains such as UAV scheduling when the temporospatial constraints can be decoupled or when low network bandwidth makes inter-agent communication difficult. However, the advantages of these methods are mitigated in the factory setting where the temporospatial constraints are tightly inter-coupled from the humans and robots working in close proximity and where there is sufficient network bandwidth. In this thesis, I present a system, called Tercio, that solves large-scale scheduling problems by combining mixed-integer linear programming to perform the agent allocation and a real-time scheduling simulation to sequence the task set. Tercio generates near optimal schedules for 10 agents and 500 work packages in less than 20 seconds on average and has been demonstrated in a multi-robot hardware test bed. My primary technical contributions are fast, near-optimal, real-time systems methods for scheduling and testing the schedulability of task sets. I also present a pilot study that investigates what level of control the Tercio should give human workers over their robotic teammates to maximize system efficiency and human satisfaction.by Matthew C. Gombolay.S.M

Human-machine collaborative optimization via apprenticeship scheduling

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.Cataloged from PDF version of thesis.Includes bibliographical references (pages 195-217).I envision a future where intelligent service robots become integral members of human-robot teams in the workplace. Today, service robots are being deployed across a wide range of settings; however, while these robots exhibit basic navigational abilities, they lack the ability to anticipate and adapt to the needs of their human teammates. I believe robots must be capable of autonomously learning from humans how to integrate into a team ' la a human apprentice. Human domain experts and professionals become experts over years of apprenticeship, and this knowledge is not easily codified in the form of a policy. In my thesis, I develop a novel computational technique, Collaborative Optimization Via Apprenticeship Scheduling (COVAS), that enables robots to learn a policy to capture an expert's knowledge by observing the expert solve scheduling problems. COVAS can then leverage the policy to guide branch-and-bound search to provide globally optimal solutions faster than state-of-the-art optimization techniques. Developing an apprenticeship learning technique for scheduling is challenging because of the complexities of modeling and solving scheduling problems. Previously, researchers have sought to develop techniques to learn from human demonstration; however, these approaches have rarely been applied to scheduling because of the large number of states required to encode the possible permutations of the problem and relevant problem features (e.g., a job's deadlines, required resources, etc.). My thesis gives robots a novel ability to serve as teammates that can learn from and contribute to coordinating a human-robot team. The key to COVAS' ability to efficiently and optimally solve scheduling problems is the use of a novel policy-learning approach - apprenticeship scheduling - suited for imitating the method an expert uses to generate the schedule. This policy learning technique uses pairwise comparisons between the action taken by a human expert (e.g., schedule agent a to complete task [tau]i at time t) and each action not taken (e.g., unscheduled tasks at time t), at each moment in time, to learn the relevant model parameters and scheduling policies demonstrated in training examples provided by the human experts. I evaluate my technique in two real-world domains. First, I apply apprenticeship scheduling to the problem of anti-ship missile defense: protecting a naval vessel from an enemy attack by deploying decoys and countermeasures at the right place and time. I show that apprenticeship scheduling can learn to defend the ship, outperforming human experts on the majority of naval engagements (p < 0.011). Further, COVAS is able to produce globally optimal solutions an order of magnitude faster than traditional, state-of-the-art optimization techniques. Second, I apply apprenticeship scheduling to learn how to function as a resource nurse: the nurse in charge of ensuring the right patient is in the right type of room at the right time and that the right types of nurses are there to care for the patient. After training an apprentice scheduler on demonstrations given by resource nurses, I found that nurses and physicians agreed with the algorithm's advice 90% of the time. Next, I conducted a series of human-subject experiments to understand the human factors consequences of embedding scheduling algorithms in robotic platforms. Through these experiments, I found that an embodied platform (i.e., a physical robot) engenders more appropriate trust and reliance in the system than an un-embodied one (i.e., computer-based system) when the scheduling algorithm works with human domain experts. However, I also found that increasing robot autonomy degrades human situational awareness. Further, there is a complex interplay between workload and workflow preferences that must be balanced to maximize team fluency. Based on these findings, I develop design guidelines for integrating service robots with autonomous decision-making capabilities into the human workplace.by Matthew C. Gombolay.Ph. D

Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams

In manufacturing, advanced robotic technology has opened up the possibility of integrating highly autonomous mobile robots into human teams. However, with this capability comes the issue of how to maximize both team efficiency and the desire of human team members to work with these robotic counterparts. To address this concern, we conducted a set of experiments studying the effects of shared decision-making authority in human–robot and human-only teams. We found that an autonomous robot can outperform a human worker in the execution of part or all of the process of task allocation (p<0.001 for both), and that people preferred to cede their control authority to the robot $$(p<0.001)$$ (p<0.001). We also established that people value human teammates more than robotic teammates; however, providing robots authority over team coordination more strongly improved the perceived value of these agents than giving similar authority to another human teammate $$(p< 0.001)$$(p<0.001). In post hoc analysis, we found that people were more likely to assign a disproportionate amount of the work to themselves when working with a robot $$(p<0.01)$$(p<0.01) rather than human teammates only. Based upon our findings, we provide design guidance for roboticists and industry practitioners to design robotic assistants for better integration into the human workplace

Acute flaccid myelitis: cause, diagnosis, and management

Acute flaccid myelitis (AFM) is a disabling, polio-like illness mainly affecting children. Outbreaks of AFM have occurred across multiple global regions since 2012, and the disease appears to be caused by non-polio enterovirus infection, posing a major public health challenge. The clinical presentation of flaccid and often profound muscle weakness (which can invoke respiratory failure and other critical complications) can mimic several other acute neurological illnesses. There is no single sensitive and specific test for AFM, and the diagnosis relies on identification of several important clinical, neuroimaging, and cerebrospinal fluid characteristics. Following the acute phase of AFM, patients typically have substantial residual disability and unique long-term rehabilitation needs. In this Review we describe the epidemiology, clinical features, course, and outcomes of AFM to help to guide diagnosis, management, and rehabilitation. Future research directions include further studies evaluating host and pathogen factors, including investigations into genetic, viral, and immunological features of affected patients, host-virus interactions, and investigations of targeted therapeutic approaches to improve the long-term outcomes in this population