M. Bernardine Dias

A comprehensive taxonomy for multi-robot task allocation

Task allocation is an important aspect of many multi-robot systems. The features and complexity of multi-robot task allocation (MRTA) problems are dictated by the requirements of the particular domain under consideration. These problems can range from those involving instantaneous distribution of simple, independent tasks among members of a homogenous team, to those requiring the time-extended scheduling of complex interrelated multi-step tasks for members of a heterogenous team related by several constraints. The existing widely used taxonomy for task allocation in multi-robot systems was designed for problems with independent tasks and does not deal with problems with interrelated utilities and constraints. While that taxonomy was a ground-breaking contribution to the MRTA literature, a survey of recent work in MRTA reveals that it is no longer a sufficient taxonomy, due to the increasing importance of interrelated utilities and constraints in realistic MRTA problems under consideration. Thus, in this paper, we present a new, comprehensive taxonomy, iTax, that explicitly takes into consideration the issues of interrelated utilities and constraints. Our taxonomy maps categories of MRTA problems to existing mathematical models from combinatorial optimization and operations research, and hence draws important parallels between robotics and these fields.

Robust multirobot coordination in dynamic environments

Robustness is crucial for any robot team, especially when operating in dynamic environments. The physicality of robotic systems and their interactions with the environment make them highly prone to malfunctions of many kinds. Three principal categories in the possible space of robot malfunctions are communication failures, partial failure of robot resources necessary for task execution (or partial robot malfunction), and complete robot failure (or robot death). This paper addresses these three categories and explores means by which the TraderBots approach ensures robustness and promotes graceful degradation in team performance when faced with malfunctions.

Robust Indoor Localization on a Commercial Smart Phone

Low-cost localization solutions for indoor environments have a variety of real-world applications ranging from emergency evacuation to mobility aids for people with disabilities. In this paper, we introduce a methodology for indoor localization using a commercial smart-phone combining dead reckoning and Wifi signal strength fingerprinting. Additionally, we outline an automated procedure for collecting Wifi calibration data that uses a robot equipped with a laser rangefinder and fiber optic gyroscope. These measurements along with a generated robot map of the environment are combined using a particle filter towards robust pose estimation. The uniqueness of our approach lies in the implementation of the complementary nature of the solution as well as in the efficient adaptation to the smart-phone platform. The system was tested using multiple participants in two different indoor environments, and achieved localization accuracies on the order of 5 meters; sufficient for a variety of navigation and context-aware applications.

Distributed robotic mapping of extreme environments

In the extreme environments posed by war fighting, fire fighting, and nuclear accident response, the cost of direct human exposure is levied in terms of injury and death. Robotic alternatives must address effective operations while removing humans from danger. This is profoundly challenging, as extreme environments inflict cumulative performance damage on exposed robotic agents. Sensing and perception are among the most vulnerable components. We present a distributed robotic system that enables autonomous reconnaissance and mapping in urban structures using teams of robots. Robot teams scout remote sites, maintain operational tempos, and successfully execute tasks, principally the construction of 3-D Maps, despite multiple agent failures. Using an economic model of agent interaction based on a free market architecture, a virtual platform (a robot colony) is synthesized where task execution does not directly depend on individual agents within the colony.

Information and Communication Technologies for Development

ICTD has become a truly global undertaking, bringing together north and south, rich and poor, rural and urban, researcher and practitioner, technologist and social scientist—all striving to work toward a better life for the least privileged.

A Layered Architecture for Coordination of Mobile Robots

This paper presents an architecture that enables multiple robots to explicitly coordinate actions at multiple levels of abstraction. In particular, we are developing an extension to the traditional three-layered robot architecture that enables robots to interact directly at each layer — at the behavioral level, the robots create distributed control loops; at the executive level, they synchronize task execution; at the planning level, they use market-based techniques to assign tasks, form teams, and allocate resources. We illustrate these ideas through applications in multi-robot assembly, multi-robot deployment, and multi-robot mapping.

Time-extended multi-robot coordination for domains with intra-path constraints

Many applications require teams of robots to cooperatively execute tasks. Among these domains are those in which successful coordination must respect intra-path constraints, which are constraints that occur on the paths of agents and affect route planning. This work focuses on multi-agent coordination for disaster response with intra-path precedence constraints, a compelling application that is not well addressed by current coordination methods. In this domain a group of fire truck agents attempt to address fires spread throughout a city in the wake of a large-scale disaster. The disaster has also caused many city roads to be blocked by impassable debris, which can be cleared by bulldozer robots. A high-quality coordination solution must determine not only a task allocation but also what routes the fire trucks should take given the intra-path precedence constraints and which bulldozers should be assigned to clear debris along those routes.This work presents two methods for generating time-extended coordination solutions—solutions where more than one task is assigned to each agent—for domains with intra-path constraints. Our first approach uses tiered auctions and two heuristic techniques, clustering and opportunistic path planning, to perform a bounded search of possible time-extended schedules and allocations. Our second method uses a centralized, non-heuristic, genetic algorithm-based approach that provides higher quality solutions but at substantially greater computational cost. We compare our time-extended approaches with a range of single task allocation approaches in a simulated disaster response domain.

How computer science serves the developing world

Information and communication technology for development can greatly improve quality of life for the worlds neediest people.

Improving child literacy in Africa: Experiments with an automated reading tutor

This paper describes a research endeavor aimed at exploring the role that technology can play in improving child literacy in developing communities. An initial pilot study and subsequent four-month-long controlled field study in Ghana investigated the viability and effectiveness of an automated reading tutor in helping urban children enhance their reading skills in English. In addition to quantitative data suggesting that automated tutoring can be useful for some children in this setting, these studies and an additional preliminary pilot study in Zambia yielded useful qualitative observations regarding the feasibility of applying technology solutions to the challenge of enhancing child literacy in developing communities. This paper presents the findings, observations and lessons learned from the field studies.

Sliding Autonomy for Peer-To-Peer Human-Robot Teams

The vision of humans and robots working together as peers to accomplish complex tasks has motivated many recent research endeavors with a variety of applications ranging from lunar construction to soccer. However, much of this research is still at an early stage, and many challenges still remain in realizing this vision. A key requirement for enabling robustness and efficiency in human-robot teams is the ability to dynamically adjust the level of autonomy to optimize the use of resources and capabilities as conditions evolve. While sliding autonomy is well defined and understood in applications where a single human is working with a single robot, it is largely unexplored when applied to teams of humans working with multiple robots. This paper highlights the challenges of enabling sliding autonomy in peer-to-peer human-robot teams and extends the current literature to identify and extend six key capabilities that are essential for overcoming these challenges. These capabilities are requesting help, maintaining coordination, establishing situational awareness, enabling interactions at different levels of granularity, prioritizing team members, and learning from interactions. We demonstrate the importance of several of these characteristics with results from a peer-to-peer human-robot team engaged in a treasure hunt task.