Nidhi Kalra

Market-Based Multirobot Coordination: A Survey and Analysis

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

Replanning with RRTs

We present a replanning algorithm for repairing rapidly-exploring random trees when changes are made to the configuration space. Instead of abandoning the current RRT, our algorithm efficiently removes just the newly-invalid parts and maintains the rest. It then grows the resulting tree until a new solution is found. We use this algorithm to create a probabilistic analog to the widely-used D* family of deterministic algorithms, and demonstrate its effectiveness in a multirobot planning domain

Hoplites: A Market-Based Framework for Planned Tight Coordination in Multirobot Teams

In this paper we address tasks for multirobot teams that require solving a distributed multi-agent planning problem in which the actions of robots are tightly coupled. The uncertainty inherent in these tasks also necessitates persistent tight coordination between teammates throughout execution. Existing approaches to coordination cannot adequately meet the technical demands of such tasks. In response, we have developed a market-based framework, Hoplites, that consists of two novel coordination mechanisms. Passive coordination quickly produces locally-developed solutions while active coordination produces complex team solutions via negotiation between teammates. Robots use the market to efficiently vet candidate solutions and to choose the coordination mechanism that best matches the current demands of the task. In experiments, Hoplites significantly outperforms even its nearest competitors, particularly in the most complex instances of a domain. We also present implementation results on a team of mobile robots.

A Comparative Study of Market-Based and Threshold-Based Task Allocation

In this paper we compare the costs and benefits of market-based and thresholdbased approaches to task allocation in real world conditions, where information and communication may be limited or inaccurate. We have performed extensive comparative experiments in an event-handling domain. Our results indicate that when information is accurate, market-based approaches are more efficient; when it is not, threshold-based approaches offer the same quality of allocation at a fraction of the expense. Additionally, both approaches are robust to low communication and task perception ranges in our experimental domain.

A Generalized Framework for Solving Tightly-coupled Multirobot Planning Problems

In this paper, we present the generalized version of the Hoplites coordination framework designed to efficiently solve complex, tightly-coupled multirobot planning problems. Our extensions greatly increase the flexibility with which teammates can both plan and coordinate with each other; consequently, we can apply Hoplites to a wider range of domains and plan coordination between robots more efficiently. We apply our framework to the constrained exploration domain and compare Hoplites in simulation to competing distributed and centralized approaches. Our results demonstrate that Hoplites significantly outperforms both approaches in terms of the quality of solutions produced while remaining computationally competitive with much simpler approaches. We further demonstrate features such as scalability and validate our approach with field results from a team of large autonomous vehicles performing constrained exploration in an outdoor environment

Design of a braille writing tutor to combat illiteracy

Less than 3% of the 145 million blind people living in developing countries are literate (Helander, Prejudice and dignity: An introduction to community-based rehabilitation. New York: UNDP 1998). 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 Educational Trust 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 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 6-week pilot study at Mathru, and outline future directions for improvement.