Papers by Michael Goodrich
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
A robotic swarm can perform various tasks. However, a human is required to task the swarm. Human ... more A robotic swarm can perform various tasks. However, a human is required to task the swarm. Human control over the swarm can be enabled through a set of influential agents which can be either leaders or predators. In the presence of multiple tasks, the swarm may need to split into sub-swarms to accomplish the task and regroup as a swarm to execute larger tasks. The response of the swarm in the presence of influential agents depends on the swarm dynamics. A precise measure of influence using leaders or predators or a combination of leaders and predators to achieve the mission is not adequately studied. In this paper, we analyze the effect of using only leaders, only predators and a combination of leaders and predators on three swarm models namely, shepherding model, Couzin's model and a physicomimetic models while they perform foraging tasks and carry out Monte-Carlo simulations to evaluate the performance of the influential agents on different swarms. We also propose a novel way to split a swarm into smaller sub-swarms using influential agents. Our results show that the predator based swarm splitting and steering to a task based on shepherding model performs far better than any other combination of leaders and predators. This result is consistent even when the number of agents is increased to 500.
ACM Transactions on Human-Robot Interaction
As development of robots with the ability to self-assess their proficiency for accomplishing task... more As development of robots with the ability to self-assess their proficiency for accomplishing tasks continues to grow, metrics are needed to evaluate the characteristics and performance of these robot systems and their interactions with humans. This proficiency-based human-robot interaction (HRI) use case can occur before, during, or after the performance of a task. This article presents a set of metrics for this use case, driven by a four-stage cyclical interaction flow: (1) robot self-assessment of proficiency (RSA), (2) robot communication of proficiency to the human (RCP), (3) human understanding of proficiency (HUP), and (4) robot perception of the human’s intentions, values, and assessments (RPH). This effort leverages work from related fields including explainability, transparency, and introspection, by repurposing metrics under the context of proficiency self-assessment. Considerations for temporal level (a priori, in situ, and post hoc) on the metrics are reviewed, as are th...
Adaptive Agents and Multi-Agents Systems, May 4, 2015
The role of humans in aviation and other domains continues to shift from manual control to automa... more The role of humans in aviation and other domains continues to shift from manual control to automation monitoring. Studies have found that humans are often poorly suited for monitoring roles, and workload can easily spike in offnominal situations. Current workload measurement tools, like NASA TLX, use human operators to assess their own workload after using a prototype system. Such measures are used late in the design process and can result in expensive alterations when problems are discovered. Our goal in this work is to provide a quantitative workload measure for use early in the design process. We leverage research in human cognition to define metrics that can measure workload on belief-desire-intentions based multi-agent systems. These measures can alert designers to potential workload issues early in design. We demonstrate the utility of our approach by characterizing quantitative differences in the workload for a single pilot operations model compared to a traditional two pilot model.
Adaptive Agents and Multi-Agents Systems, May 5, 2014
Models of swarming and modes of controlling them are numerous; however, to date swarm researchers... more Models of swarming and modes of controlling them are numerous; however, to date swarm researchers have mostly ignored a fundamental problem that impedes scalable human interaction with large bio-inspired robot swarms, namely, how do you know what the swarm is doing if you can't observe every agent in the collective? We examine swarm models that exhibit multiple collective motion patterns from the same parameters. These multiple emergent behaviors provide increased expressivity, but at the cost of uncertainty about the swarm's actual behavior. Because bandwidth and time constraints limit the number of agents that can be observed in a swarm, it is desirable to be able to recognize and monitor the collective behavior of a swarm through limited samples from a small subset of agents. We present a novel framework for classifying the collective behavior of a bioinspired robot swarm using locally-based approximations of a swarm's global features. We apply this framework to two bio-inspired models of swarming that exhibit a flock and torus behavior and a swarm, torus, and flock behavior, respectively. We present both a formal metric of expressivity and a classifier that leverages local agent-level features to accurately recognize these collective swarm behaviors while sampling from only a small number of agents.
Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, 2017
An important part of expressing human intent is identifying acceptable tradeoffs among competing ... more An important part of expressing human intent is identifying acceptable tradeoffs among competing performance objectives. We present and evaluate a set of graphical user interfaces (GUIs), that are designed to allow a human to express intent by expressing desirable tradeoffs. The GUIs require an algorithm that identifies the set of Pareto optimal solutions to the multi-objective decision problem, which means that all the solutions are equally good in the sense that there are no other solutions better for every objective. Given the Pareto set, the GUIs provide different ways for a human to express intent by exploring tradeoffs between objectives; once a tradeoff is selected, the solution is chosen. The GUI designs are applied to interactive humanrobot path-selection for a robot in an urban environment, but they can be applied to other tradeoff problems. A user study evaluates GUI designs by requiring users to select a tradeoff that satisfies a specified mission intent. Results of the user study suggest that GUIs designed to support an artist's palette-metaphor can be used to express intent without incurring unacceptable levels of human workload.
Engineering Psychology and Cognitive Ergonomics: Performance, Emotion and Situation Awareness, 2017
Robot swarms modeled after hub-based colonies, such as ants and bees, potentially offer fault-tol... more Robot swarms modeled after hub-based colonies, such as ants and bees, potentially offer fault-tolerant capabilities at very favorable cost margins. However, relatively little is known about how to harness the potential of these swarms through command-and-control systems. In this paper, we study how to merge operator input with the underlying swarm behavior to maintain the fault-tolerant attributes of robot swarms while providing the operator with enough control to ensure that mission objectives are accomplished. We advocate that an effective mechanism for achieving this is shared control, wherein decision-making is shared between the human operator and the underlying swarm dynamics. We lay out characteristics of human-swarm systems that provide an effective balance between fault-tolerance and control, and we discuss preliminary designs of human-swarm systems for hub-based colonies based on these principles.
Unmanned Systems Technology XX, 2018
In goal-based tasks such as navigating a robot from location A to location B in a dynamic environ... more In goal-based tasks such as navigating a robot from location A to location B in a dynamic environment, human intent can mean to choose a specific trade-off between multiple competing objectives. For example, intent can mean to find a path that balances between "Go quickly" and "Go stealthily". Given human expectations about how a path balances such tradeoffs, the path should match the human's intent throughout the entire execution of the path even if the environment changes. If the path drifts from the human's intent because the environment changes, then a new robotic-path needs to be planned-referred to as path-replanning. We discuss here three system-initiated triggers (prompts) for path-replanning. The objective is to create an interactive replanning system that yields paths that consistently match human intent. The triggers are to replan (a) at regular time intervals, (b) when the current robotic path deviates from the user intent, and (c) when a better path can be obtained from a different homotopy class. Further, we consider one user-generated replanning trigger that allows the user to stop the robot anytime to put the robot onto a new route. These four trigger variants seek to answer two fundamental critical questions: When is a re-planned path acceptable to a human? and How should a planner involve a human in replanning?.
2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583)
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019
Algorithms used in networking, operation research and optimization can be created using bio-inspi... more Algorithms used in networking, operation research and optimization can be created using bio-inspired swarm behaviors, but it is difficult to mimic swarm behaviors that generalize through diverse environments. State-machine-based artificial collective behaviors evolved by standard Grammatical Evolution (GE) provide promise for general swarm behaviors but may not scale to large problems. This paper introduces an algorithm that evolves problem-specific swarm behaviors by combining multi-agent grammatical evolution and Behavior Trees (BTs). We present a BT-based BNF grammar, supported by different fitness function types, which overcomes some of the limitations in using GEs to evolve swarm behavior. Given human-provided, problem-specific fitness-functions, the learned BT programs encode individual agent behaviors that produce desired swarm behaviors. We empirically verify the algorithm's effectiveness on three different problems: single-source foraging, collective transport, and nest...
2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)
Journal of Human-Robot Interaction, 2015
The search for invariants is a fundamental aim of scientific endeavors. These invariants, such as... more The search for invariants is a fundamental aim of scientific endeavors. These invariants, such as Newton's laws of motion, allow us to model and predict the behavior of systems across many different problems. In the nascent field of Human-Swarm Interaction (HSI), a systematic identification of fundamental invariants is still lacking. Discovering and formalizing these invariants will provide a foundation for developing, and better understanding, effective methods for HSI. We propose two invariants underlying HSI for geometric-based swarms: (1) collective state is the fundamental percept associated with a bio-inspired swarm, and (2) a human's ability to influence and understand the collective state of a swarm is determined by the balance between the span and persistence. We provide evidence of these invariants by synthesizing much of our previous work in the area of HSI with several new results, including a novel user study where users manage multiple swarms simultaneously. We also discuss how these invariants can be applied to enable more efficient and successful teaming between humans and bio-inspired collectives and identify several promising directions for future research into the invariants of HSI.
SPIE Proceedings, 2016
In a problem where a human uses supervisory control to manage robot path-planning, there are time... more In a problem where a human uses supervisory control to manage robot path-planning, there are times when human does the path planning, and if satisfied commits those paths to be executed by the robot, and the robot executes that plan. In planning a path, the robot often uses an optimization algorithm that maximizes or minimizes an objective. When a human is assigned the task of path planning for robot, the human may care about multiple objectives. This work proposes a graphical user interface (GUI) designed for interactive robot path-planning when an operator may prefer one objective over others or care about how multiple objectives are traded off. The GUI represents multiple objectives using the metaphor of an artist's palette. A distinct color is used to represent each objective, and tradeoffs among objectives are balanced in a manner that an artist mixes colors to get the desired shade of color. Thus, human intent is analogous to the artist's shade of color. We call the GUI an "Adverb Palette" where the word "Adverb" represents a specific type of objective for the path, such as the adverbs "quickly" and "safely" in the commands: "travel the path quickly", "make the journey safely". The novel interactive interface provides the user an opportunity to evaluate various alternatives (that tradeoff between different objectives) by allowing her to visualize the instantaneous outcomes that result from her actions on the interface. In addition to assisting analysis of various solutions given by an optimization algorithm, the palette has additional feature of allowing the user to define and visualize her own paths, by means of waypoints (guiding locations) thereby spanning variety for planning. The goal of the Adverb Palette is thus to provide a way for the user and robot to find an acceptable solution even though they use very different representations of the problem. Subjective evaluations suggest that even non-experts in robotics can carry out the planning tasks with a great deal of flexibility using the adverb palette.
2015 Swarm/Human Blended Intelligence Workshop (SHBI), 2015
Distributed teams of agents can provide robust solutions to many problems of interest, and allowi... more Distributed teams of agents can provide robust solutions to many problems of interest, and allowing a human to influence and manage those agents can extend the range of problems that can be solved while improving the team's efficiency. Within this context, it is interesting to develop methods for interaction that are intuitive and that utilize haptic interaction so that the human manager need not be "heads down" in a graphical user interface. This paper presents a set of agent control algorithms that yield useful team performance and enable haptic-based management of team behaviors. A preliminary demonstration of the system is also presented.
A mission to Mars will be composed of several groups that need to interact effectively. Scientist... more A mission to Mars will be composed of several groups that need to interact effectively. Scientists on Earth, supervisors in a habitat on Mars, and a surface exploration team on Mars will all require different views of information. This position paper proposes a multiple perspective interface paradigm which implements augmented virtuality and multiple camera perspectives to clearly present these views.
Proceeding of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction - HRI '06, 2006
AIAA Infotech@Aerospace Conference, 2009
As unmanned air vehicle (UAV) utilization increases in Wilderness Search and Rescue (WiSAR) effor... more As unmanned air vehicle (UAV) utilization increases in Wilderness Search and Rescue (WiSAR) efforts, onboard sensors yielding more information will be desired. UAVs can assist WiSAR efforts by accelerating the ground search process through returning quality aerial footage of the terrain. This paper presents methods and prototype test results for acquiring video from multiple video sensors and fusing them into a single video stream using a Virtual Gimbal capable of rendering panoramic video. The panoramic video stream is the first of its kind to be constructed from video transmissions from a small UAV, and the first known video panorama to be used to quickly survey a region within a WiSAR context. The Virtual Gimbal comprises two video transmissions from a three camera array mounted in a downward-looking configuration on a UAV. This video stream has been shown to decrease the amount of time required to thoroughly survey a region by more than 40 percent. Common utilization of unmanned air vehicles (UAVs) include military surveillance and reconnaissance, battle damage assessment, convoy following, ordnance delivery, border patrol, and other emerging law enforcement applications. While such security and military applications certainly comprise a large sector of the UAV market, other data collection applications for UAVs are beginning to emerge. For example, recent development and studies have focused on using UAVs for high-resolution terrain mapping, fire-monitoring, consumer interest sampling, and wilderness search and rescue (WiSAR). Each of these applications for UAV deployment are intriguing, but this research was focused primarily on the use of UAVs to assist wilderness and search and rescue ground teams by providing an aerial perspective. The utility of using a small UAV platform for such purposes is fairly evident: UAVs afford greater safety for operators monitoring system progress remotely, allow various field-deployment scenarios because they are man-portable and hand-launchable, are able to fly safely at low altitudes, and are maintained at relatively low cost. Compared to individuals searching from the ground, UAVs are able to cover a wider variety of terrain safely and traverse the land more quickly. Small UAVs may also carry a payload, though the complexity and size of the payload are limited by the size of the UAV. Any system designed for the purpose of retrieving improved aerial surveillance invariably considers two competing variables-level of detail and degree of coverage. A high degree of detail can be surveyed if the UAV flies at low altitudes, but a thorough search of an area would require more time since the effective footprint of the camera would be smaller than if the UAV flew at higher altitudes. On the other hand, the region could be more rapidly searched from a higher altitude, though the level of detail in the video would be much coarser. Ideally, the competing demands for high detail and wider, rapid coverage can both be satisfied.
2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012
Every year there are numerous cases of individuals becoming lost in remote wilderness environment... more Every year there are numerous cases of individuals becoming lost in remote wilderness environments. Principles of search theory have become a foundation for developing more efficient and successful search and rescue methods. Measurements can be taken that describe how easily a search object is to detect. These estimates allow the calculation of the probability of detectionthe probability that an object would have been detected if in the area. This value only provides information about the search area as a whole; it does not provide details about which portions were searched more thoroughly than others. Ground searchers often carry portable GPS devices and their resulting GPS track logs have recently been used to fill in part of this knowledge gap. We created a system that provides a detection likelihood map that estimates the probability that each point in a search area was seen well enough to detect the search object if it was there. This map will be used to aid ground searchers as they search an assigned area, providing real time feedback of what has been "seen." The maps will also assist incident commanders as they assess previous searches and plan future ones by providing more detail than is available by viewing GPS track logs.
Infotech@Aerospace 2012, 2012
Unmanned Systems Technology XVI, 2014
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Papers by Michael Goodrich