Motion Control in Dynamic Multi-Robot Environments
This paper presents the motion control system used by CMUnited-98, the small-size league champion at RoboCup-98. The robotic soccer team consists of five robots that aim at achieving specific goals while navigating in a limited space shared with the five other opponent robots. We introduce our motion control algorithm, which allows a general differential-driven robot to accurately reach a target point with a desired orientation in an environment with multiple moving obstacles. We describe how the features of our motion controller help to build interesting and robust behaviors. We also briefly compare our system to other motion control techniques and include descriptions and illustrations of the performance of our fully-implemented motion control algorithm.

title: Task Decomposition and Dynamic Role Assignment for Real-Time Strategic Teamwork
abstract: Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this paper, we introduce periodic team synchronization domains, as timecritical environments in which agents act autonomouslywith limited communication, but they can periodically synchronize in a full-communication setting. We present a team agent structure that allows for an agent to capture and reason about team agreements. We achieve collaboration between agents through the introduction of formations. A formation decomposes the task space defining a set of roles. Homogeneous agents can flexibly switch roles within formations, and agents can change formations dynamically, according to pre-defined triggers to be evaluated at run-time. This flexibility increases the performance of the overall team. Our team structure further includes pre-planning for frequent situations. We fully implemented this approach in the domain of robotic soccer. Our simulator team made it to the semi-finals of the RoboCup-97 competition, in which 29 teams participated. It achieved a total score of 67-9 over six different games, and successfully demonstrated its flexible team structure. Using the same team structure, our small robot team won the RoboCup-97 small-robot competition, in which 4 teams participated. It achieved a total score of 13-1 over 4 games and also demonstrated its flexible team structure.

title: A Formalization of Equilibria for Multiagent Planning
abstract: Planning has traditionally focused on single agent systems. Although planning domain languages have been extended to multiagent domains, solution concepts have not. Previous solution concepts either focus on planning for teams of agents with a single goal, or on a single agent in an environment containing other agents with unknown and unpredictable behavior. In reality other agents are usually acting to achieve their own explicit goals, which may not be the same or even related. In game theory the notion of an equilibria provides a framework for thinking about self-interested, utility maximizing agents. We define a formalization of the multiagent nondeterministic planning problem and introduce a notion of equilibria inspired by the game theoretic concept. As far as we know, this is the first solution framework that explicitly accounts for the various goals of all agents. In addition to the formalization we also demonstrate how this framework applies in a number of different domains.

title: The Need for Different Domain-independent Heuristics
abstract: PRODIGY's planning algorithm uses domain-independent search heuristics. In this paper, we support our belief that there is no single search heuristic that performs more efficiently than others for all problems or in all domains. The paper presents three different domain-independent search heuristics of increasing complexity. We run PRODIGY with these heuristics in a series of artificial domains (introduced in (Barrett & Weld 1994)) where in fact one of the heuristics performs more efficiently than the others. However, we introduce an additional simple domain where the apparently worst heuristic outperforms the other two. The results we obtained in our empirical experiments lead to the main conclusion of this paper: planning algorithms need to use different search heuristics in different domains. We conclude the paper by advocating the need to learn the correspondence between particular domain characteristics and specific search heuristics for planning efficiently in complex domains.

title: Task Decomposition, Dynamic Role Assignment, and Low-Bandwidth Communication for Real-Time Strategic Teamwork
abstract: Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team synchronization (PTS) domains as time-critical environments in which agents act autonomously with low communication, but in which they can periodically synchronize in a full-communication setting. The two main contributions of this article are a flexible team agent structure and a method for inter-agent communication. First, the team agent structure allows agents to capture and reason about team agreements. We achieve collaboration between agents through the introduction of formations. A formation decomposes the task space defining a set of roles. Homogeneous agents can flexibly switch roles within formations, and agents can change formations dynamically, according to pre-defined triggers to be evaluated at run-time. This flexibility increases the performance of the overall team. Our teamwork structure further includes pre-planning for frequently occurring situations. Second, the communication method is designed for use during the low-communication periods in PTS domains. It overcomes the obstacles to inter-agent communication in multi-agent environments with unreliable, single-channel, high-cost, low-bandwidth communication. We fully implemented both the flexible teamwork structure and the communication method in the domain of simulated robotic soccer, and conducted controlled empirical experiments to verify their effectiveness. In addition, our simulator team made it to the semi-finals of the RoboCup-97 competition, in which 29 teams participated. It achieved a total score of 67–9 over six different games, and successfully demonstrated its flexible teamwork structure and inter-agent communication.

title: Real-Time Randomized Path Planning for Robot Navigation
abstract: Mobile robots often must find a trajectory to another position in their environment, subject to constraints. This is the problem of planning a path through a continuous domain Rapidly-exploring random trees (RRTs) are a recently developed representation on which fast continuous domain path planners can be based. In this work, we build a path planning system based on RRTs that interleaves planning and execution, first evaluating it in simulation and then applying it to physical robots. Our planning algorithm, ERRT (execution extended RRT), introduces two novel extensions of previous RRT work, the waypoint cache and adaptive cost penalty search, which improve replanning efficiency and the quality of generated paths. ERRT is successfully applied to a real-time multi-robot system. Results demonstrate that ERRT is significantly more efficient for replanning than a basic RRT planner, performing competitively with or better than existing heuristic and reactive real-time path planning approaches. ERRT is a significant step forward with the potential for making path planning common on real robots, even in challenging continuous, highly dynamic domains.

title: CMRoboBits: Creating an Intelligent AIBO Robot
abstract: CMRoboBits is a course offered at Carnegie Mellon University that introduces students to all the concepts needed to create a complete intelligent robot. In particular, the course focuses on the areas of perception, cognition, and action by using the Sony AIBO robot as the focus for the programming assignments. This course shows how an AIBO and its software resources make it possible for students to investigate and work with an unusually broad variety of AI topics within a single semester. While material presented in this article describes using AIBOs as the primary platform, the concepts presented in the course are not unique to the AIBO and can be applied on different kinds of robotic hardware.

title: Automatic Clustering of Faces in Meetings
abstract: Meetings are an integral part of business life for any organization. In previous work, we have developed a physical awareness system called CAMEO (camera assisted meeting event observer) to record and process the audio/visual information of a meeting. An important task in meeting understanding is to know who and how many people are attending the meeting. In this paper, we present an automatic approach to detect, track, and cluster people's faces in long video sequences. This is a challenging problem due to the appearance variability of people's faces (illumination, expression, pose,...). Two main novelties are presented: a robust real-time adaptive subspace face tracker which combines color and appearance. A temporal subspace clustering algorithm. The effectiveness and robustness of the proposed system is demonstrated over a data set of long videos (i.e. 1 hour).