The ability to communicate is crucial to leading an independent life. Unfortunately, individuals from developing communities who are deaf and hard of hearing tend to encounter difficulty communicating, due to a lack of educational resources. We present findings from a two-year deployment of Speak Up, a suite of voice-powered games to motivate speech therapy, at a school for the deaf in India. Using ethnographic methods, we investigated the interplay between Speak Up and local educational practices. We found that teachers' speech therapy goals had evolved to differ from those encoded in the games, that the games influenced classroom dynamics, and that teachers had improved their computer literacy and developed creative uses for the games. We used these insights to further enhance Speak Up by creating an explicit teacher role in the games, making changes that encouraged teachers to build their computer literacy, and adding an embodied agent.

Research in assistive systems for travelers who are blind/low vision (B/LV) has been largely focused on basic map information. We present NavCue, an intelligent system module for providing rich, multi-sensory, context-based information using speech guidance and robot physical gestures. This approach is motivated by our previous user studies with people who are blind or low vision. This rich information should enhance user location awareness and confidence when traveling through unfamiliar locations.

In this paper, we describe the process of making a robot useful as a guide robot for people who are blind or visually impaired. For this group, the interactive audio feature of a robot assumes a very high level of importance. We have introduced some features that will help to make the robot sound natural and be more comfortable. We first addressed the question of the speaker placement to help the user determine the size and distance of the robot. After the initial meeting, user data will be retained by the robot so that their communication evolves with every interaction. The robot will also ask the users if they need to take a rest after a specified interval depending upon the user's age and the distance they need to cover. The next time they visit, all this information will be used to make the interaction more natural and customized for each individual user.

Dynamic changes can present significant challenges for visually impaired travelers to safely and independently navigate urban environments. To address these challenges, we are developing the NavPal suite of technology tools [1]. NavPal includes a dynamic guidance tool [2] in the form of a smartphone app that can provide real-time instructions based on available map information to guide navigation in indoor environments. In this paper we enhance our past work by introducing a framework for blind travelers to add map/navigation information to the tool, and to invite trusted sources to do the same. The user input is realized through audio breadcrumb annotations that could be useful for future trips. The trusted sources mechanism provides invited trusted individuals or organizations an interface to contribute real-time information about the surrounding environment. We demonstrate the feasibility of our solution through a prototype Android smartphone-based outdoor navigation aid for blind travelers. An initial usability study with visually impaired adults informed the design and implementation of this prototype.

As robots are increasingly employed for distributed missions, an important problem that needs to be solved is the Autonomous Recharging Problem (ARP); that is, effectively planning and coordinating when, where, and how to recharge robots to maximize operational efficiency. In this paper, we define the ARP and clearly describe the relevant technical challenges that need to be addressed in order to solve the ARP. These challenges include individual awareness about energy for each robot, correlating task execution to its impact on battery life, designing and implementing static and mobile recharging stations, optimizing individual worker robots schedules to ensure extended operations, and coordinating a team of worker and recharging robots to effectively share available recharging resources. In this paper we also describe an initial implementation of a market-based solution to the ARP that scales with the number of tasks as well as with the number of robots, and schedules workers' tasks to coordinate with recharging tasks. The developed solution has been evaluated in simulation and on Pioneer P3DX mobile robots executing transportation tasks in an indoor environment. Results demonstrate that our approach consistently outperforms the state of the art in recharging strategies.

In earlier work closed-form trajectory bending was shown to provide an efficient and accurate out-of-core solution for loop-closing exactly sparse trajectories. Here we extend it to fuse exactly sparse trajectories, obtained from relative pose estimates, with absolute orientation data. This allows us to close-the-loop using absolute orientation data only. The benefit is that our approach does not rely on the observations from which the trajectory was estimated nor on the probabilistic links between poses in the trajectory. It therefore is highly efficient. The proposed method is compared against regular fusion and an iterative trajectory bending solution using a 5 km long urban trajectory. Proofs concerning optimality of our method are provided.

In this paper, we present an approach to bounded optimal planning and flexible execution for a robot team performing a set of spatially distributed tasks related by temporal ordering constraints such as precedence or synchronization. Furthermore, the manner in which the temporal constraints are satisfied impacts the overall utility of the team, due to the existence of both routing and delay costs. We present a bounded optimal offline planner for task allocation and scheduling in the presence of such cross-schedule dependencies, and a flexible, distributed online plan execution strategy. The integrated system performs task allocation and scheduling, executes the plans smoothly in the face of real-world variations in operation speed and task execution time, and ensures graceful degradation in the event of task failure. We demonstrate the capabilities of our approach on a team of three pioneer robots operating in an indoor environment. Experimental results demonstrate that the approach is effective for constrained planning and execution in the face of real-world variations.

While the needs and applications for computing technology have been growing, the enrollment and interest in Computer Science (CS) at the university level has not been growing in proportion. Moreover, the increasing prevalence of globalization requires a new set of skills for future technology leaders. These skills include the ability to work well in multidisciplinary and globally distributed teams, cross cultural boundaries with ease, create innovative solutions for problems that arise in unfamiliar settings, and think outside the box to solve a variety of problems while building effectively upon related work in the literature. These changes are also reflected in the growing interest among youth in global issues and how we can make a difference in underserved communities. This paper reports on our experience in designing and deploying an innovative internship that addresses these issues and seeks to enhance participation and education in CS through guided research projects in underserved communities.

Although para-social workers (PSW) carry the primary responsibility in providing essential services to the growing population of orphans and vulnerable children in Tanzania, they are often not paid for this work. Moreover, PSWs are unable to access governmental resources due to the lack of an efficient means of reporting their needs to relevant government officials in a timely manner. In this paper we describe a text message (SMS) based solution that harnesses the prevalence of mobile phones coupled with several Open Source tools to empower PSWs. Specifically, we build a more efficient mechanism for reporting summary data on orphans and vulnerable children to relevant government officials in a cost-effective and efficient manner. This paper reports on our needs assessment process, reviews the related work, describes the implementation and testing of our prototype solution, and concludes with a discussion of relevant future work.

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.

This paper addresses the challenges and benefits of undergraduate robotics education in technologically underserved communities. We present two robotics courses that the authors designed and taught in Qatar and Ghana. While different in context and setting, these courses share a similar structure and approach. We describe and analyze our experiences in the two case studies, and extract lessons that are relevant to others teaching robotics; especially in underserved communities. We also address the impact of these courses on the local communities and the broader academic community

Less than 3% of the 145 million blind people living in developing countries are literate. This low literacy rate is partly due to the lack of trained teachers and the challenges associated with learning to write Braille on a traditional slate and stylus. These challenges include writing from right to left, writing mirrored images of letters, and receiving significantly delayed feedback. Extensive conversations with the Mathru School for the Blind near Bangalore, India, revealed the need for a robust, low-power, low-cost Braille writing tutor. We present an iterative and participatory design process resulting in the creation and refinement of a prototype Braille writing tutor system. This system uses a novel input device to capture a student's activity on a slate using a stylus and uses a range of techniques to teach Braille writing skills to both beginner and advanced students. We report on lessons learned from the implementation of this project and from a six-week pilot study at the Mathru school, and outline future directions for improvement.

This paper presents a learning-enhanced marketbased task allocation approach for oversubscribed domains. In oversubscribed domains all tasks cannot be completed within the required deadlines due to a lack of resources. We focus specifically on domains where tasks can be generated throughout the mission, tasks can have different levels of importance and urgency, and penalties are assessed for failed commitments. Therefore, agents must reason about potential future events before making task commitments. Within these constraints, existing market-based approaches to task allocation can handle task importance and urgency, but do a poor job of anticipating future tasks, and are hence assessed a high number of penalties. In this work, we enhance a baseline market-based task allocation approach using regression-based learning to reduce overall incurred penalties. We illustrate the effectiveness of our approach in a simulated disaster response scenario by comparing performance with a baseline market-approach.

As we progress towards a world where robots play an integral role in society, a critical problem that remains to be solved is the pickup team challenge; that is, dynamically formed heterogeneous robot teams executing coordinated tasks where little information is known a priori about the tasks, the robots, and the environments in which they would operate. Successful solutions to forming pickup teams would enable researchers to experiment with larger numbers of robots and enable industry to efficiently and cost-effectively integrate new robot technology with existing legacy teams. In this paper, we define the challenge of pickup teams and propose the treasure hunt domain for evaluating the performance of pickup teams. Additionally, we describe a basic implementation of a pickup team that can search and discover treasure in a previously unknown environment. We build on prior approaches in market-based task allocation and plays for synchronized task execution, to allocate roles amongst robots in the pickup team, and to execute synchronized team actions to accomplish the treasure hunt task

There are some arguments that suggest women need academic handholding, such as a "female friendly" curriculum, in order for them to participate and be successful in computer science and related fields. Then there are other arguments that suggest we need to change the field to suit women or help women adjust to the field. In this paper we present a different perspective that shows none of these may be necessary. The "Women-CS Fit" is already there! Specifically, under certain cultural and environmental conditions we can see that women fit very well into computing fields and what we have been attributing to gender is actually the result of cultural and environmental conditions. The reasons for women participating in -- or not participating in -- the field of computer science have little to do with gender and a lot to do with culture. In other words, we need to recognize that this is a cultural issue, and an issue that concerns us all. Appropriate local interventions in the micro-culture can have large effect. This argument is illustrated in this paper by three case studies.

Market-based multirobot coordination approaches have received significant attention and are growing in popularity within the robotics research community. They have been successfully implemented in a variety of domains ranging from mapping and exploration to robot soccer. The research literature on market-based approaches to coordination has now reached a critical mass that warrants a survey and analysis. This paper addresses this need for a survey of the relevant literature by providing an introduction to market-based multirobot coordination, a review and analysis of the state of the art in the field, and a discussion of remaining research challenges

The growing demand for technological innovation to enable empowerment of developing communities requires new and creative educational initiatives. Thus, well designed higher educational initiatives geared towards appropriate technology for developing communities can have a significant global impact. This paper presents the challenges and benefits of three higher education initiatives in Sri Lanka, Ghana, and the USA that focus on innovating and implementing relevant technology for developing communities. The authors examine the potential intersections of robotics and its component technologies with education and sustainable development. The paper concludes with an assessment of factors that contribute to the success of higher educational initiatives designed to enable technology relevant to developing communities.

While market-based approaches, such as TraderBots, have shown much promise for efficient coordination of multirobot teams, the cost estimation mechanism and its impact on solution efficiency has not been investigated. This paper provides a first analysis of the cost estimation process in the TraderBots approach applied to a distributed sensing task. In the presented implementation, path costs are estimated using the D* path planning algorithm with optimistic costing of unknown map cells. The reported results show increased team efficiency when cost estimates reflect different environmental and mission characteristics. Thus, this paper demonstrates that market-based approaches can improve team efficiency if cost estimates take into account environmental and mission characteristics. These findings encourage future research on applying learning techniques for online modification of cost estimation and in market-based coordination.

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.

This paper presents a comparative study between three multirobot coordination schemes that span the spectrum of coordination approaches; a fully centralized approach that can produce optimal solutions, a fully distributed behavioral approach with minimal planned interaction between robots, and a market approach which sits in the middle of the spectrum. Several dimensions for comparison are proposed based on characteristics identified as important to multirobot application domains. Furthermore, simulation results are presented for comparisons along two of the suggested dimension: Number of robots in the team and Heterogeneity of the team. Results spanning different team sizes indicate that the market method compares favorably to the optimal solutions generated by the centralized approach in terms of cost, and compares favorably to the behavioral method in terms of computation time. All three methods are able to improve global cost by accounting for the heterogeneity of the robot team.

Sun-synchronous exploration is accomplished by reasoning about sunlight: where the Sun is in the sky, where and when shadows will fall, and how much power can be obtained through various courses of action. In July 2001 a solar-powered rover, named Hyperion, completed two sun-synchronous exploration experiments in the Canadian high arctic (75/spl deg/N). Using knowledge of orbital mechanics, local terrain, and expected power consumption, Hyperion planned a sun-synchronous route to visit designated sites while obtaining the necessary solar power for continuous 24-hour operation. Hyperion executed its plan and returned to its starting location with batteries fully charged after traveling more than 6 kilometers in barren, Mars-analog terrain. We describe the concept of sun-synchronous exploration. We overview the design of the robot Hyperion and the software system that enables it to operate sun-synchronously. We then discuss results from analysis of our first experiment in sun-synchronous exploration and conclude with observations.

Presents an approach to efficient multirobot mapping and exploration which exploits a market architecture in order to maximize information gain while minimizing incurred costs. This system is reliable and robust in that it can accommodate dynamic introduction and loss of team members in addition to being able to withstand communication interruptions and failures. Results showing the capabilities of our system on a team of exploring autonomous robots are given.

Multirobot coordination, if made efficient and robust, promises high impact on automation. The challenge is to enable robots to work together in an intelligent manner to execute a global task. The market approach has had considerable success in the multirobot coordination domain. This paper investigates the effects of introducing opportunistic optimization with leaders to enhance market-based multirobot coordination. Leaders are able to optimize within subgroups of robots by collecting information about their tasks and status, and re-allocating the tasks within the subgroup in a more profitable manner. The presented work considers the effects of a leader optimizing a single subgroup, and some effects of multiple leaders optimizing overlapping subgroups. The implementations were tested on a variation of the distributed traveling salesman problem. Presented results show that global costs can be reduced, and hence task allocation can be improved, utilizing leaders.

This paper describes the navigation system used on a prototype sun-synchronous robot. Sun-synchrony is a concept that will enable exploration missions by solar-powered rovers that could last months or years. This paper presents the navigation algorithms developed for traversing natural terrain robustly. The novel elements of this work are the refinements necessary to transform laboratory-demonstrated technologies into a form useful for robust, Sun-synchronous exploration. Results of afield experiment in the Canadian Arctic, where the robot traversed 6.1km, 90% autonomously, are also presented.