No abstract

When robots work together as a team, the members that perform each task should be the ones that promise to use the least resources to do the job.

When robots work together as a team, the members that perform each task should be the ones that promise to use the least resources to do the job. ABSTRACT | 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 marketbased multirobot coordination, a review and analysis of the state of the art in the field, and a discussion of remaining research challenges.

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.

Abstract-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.

No abstract

for their time, insights, and contributions. Parts of this article are based on Hallegatte et al. (2012).

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.