Reid G. Simmons

Collaborative multi-robot exploration

In this paper we consider the problem of exploring an unknown environment by a team of robots. As in single-robot exploration the goal is to minimize the overall exploration time. The key problem to be solved therefore is to choose appropriate target points for the individual robots so that they simultaneously explore different regions of their environment. We present a probabilistic approach for the coordination of multiple robots which, in contrast to previous approaches, simultaneously takes into account the costs of reaching a target point and the utility of target points. The utility of target points is given by the size of the unexplored area that a robot can cover with its sensors upon reaching a target position. Whenever a target point is assigned to a specific robot, the utility of the unexplored area visible from this target position is reduced for the other robots. This way, a team of multiple robots assigns different target points to the individual robots. The technique has been implemented and tested extensively in real-world experiments and simulation runs. The results given in this paper demonstrate that our coordination technique significantly reduces the exploration time compared to previous approaches.

The curvature-velocity method for local obstacle avoidance

We present a new method for local obstacle avoidance by indoor mobile robots that formulates the problem as one of constrained optimization in velocity space. Constraints that stem from physical limitations (velocities and accelerations) and the environment (the configuration of obstacles) are placed on the translational and rotational velocities of the robot. The robot chooses velocity commands that satisfy all the constraints and maximize an objective function that trades off speed, safety and goal-directedness. An efficient, real-time implementation of the method has been extensively tested, demonstrating reliable, smooth and speedy navigation in office environments. The obstacle avoidance method is used as the basis of more sophisticated navigation behaviors, ranging from simple wandering to map-based navigation.

Structured control for autonomous robots

To operate in rich, dynamic environments, autonomous robots must be able to effectively utilize and coordinate their limited physical and computational resources. As complexity increases, it becomes necessary to impose explicit constraints on the control of planning, perception, and action to ensure that unwanted interactions between behaviors do not occur. This paper advocates developing complex robot systems by layering reactive behaviors onto deliberative components. In this structured control approach, the deliberative components handle normal situations and the reactive behaviors, which are explicitly constrained as to when and how they are activated, handle exceptional situations. The Task Control Architecture (TCA) has been developed to support this approach. TCA provides an integrated set of control constructs useful for implementing deliberative and reactive behaviors. The control constructs facilitate modular and evolutionary system development: they are used to integrate and coordinate planning, perception, and execution, and to incrementally improve the efficiency and robustness of the robot systems. To date, TCA has been used in implementing a half-dozen mobile robot systems, including an autonomous six-legged rover and indoor mobile manipulator. >

A task description language for robot control

Robot systems must achieve high level goals while remaining reactive to contingencies and new opportunities. This typically requires robot systems to coordinate concurrent activities, monitor the environment, and deal with exceptions. We have developed a new language to support such task-level control. The language, TDL, is an extension of C++ that provides syntactic support for task decomposition, synchronization, execution monitoring, and exception handling. A compiler transforms TDL into pure C++ code that utilizes a platform-independent task management library. This paper introduces TDL, describes the task tree representation that underlies the language, and presents some aspects of its implementation and use in an autonomous mobile robot.

Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling

We propose a model independent procedure for verifying properties of discrete event systems. The dynamics of such systems can be very complex, making them hard to analyze, so we resort to methods based on Monte Carlo simulation and statistical hypothesis testing. The verification is probabilistic in two senses. First, the properties, expressed as CSL formulas, can be probabilistic. Second, the result of the verification is probabilistic, and the probability of error is bounded by two parameters passed to the verification procedure. The verification of properties can be carried out in an anytime manner by starting off with loose error bounds, and gradually tightening these bounds.

Second generation expert systems

Second generation expert systems have been a very active field of research during the last years. Much work has been carried out to overcome drawbacks of first generation expert systems. This book presents an overview and new contributions from people who have played a major role in this evolution. It is divided in several sections that cover the main topics of the subject: combining multiple reasoning paradigms; knowledge level modelling; knowledge acquisition in second generation expert systems; explanation of reasoning; and architectures for second generation expert systems. This book can serve as a reference book for researchers and students and will also be a help for practitioners involved in KBS developments. This book surveys the main approaches for designing Second Generation Expert Systems. It also summarises benefits of these approaches for knowledge acquisition, explanation, etc. The book can serve as a reference book for researchers and students and is also of help for practitioners involved in KBS developments.

The role of expressiveness and attention in human-robot interaction

This paper presents the results of an experiment in human-robot social interaction. Its purpose was to measure the impact of certain features and behaviors on peoples willingness to engage in a short interaction with a robot. The behaviors tested were the ability to convey expression with a humanoid face and the ability to indicate attention by turning towards the person that the robot is addressing. We hypothesized that these features were minimal requirements for effective social interaction between a human and a robot. We will discuss the results of the experiment and their implications for the design of socially interactive robots.

Natural person-following behavior for social robots

We are developing robots with socially appropriate spatial skills not only to travel around or near people, but also to accompany people side-by-side. As a step toward this goal, we are investigating the social perceptions of a robots movement as it follows behind a person. This paper discusses our laser-based person-tracking method and two different approaches to person-following: direction-following and path-following. While both algorithms have similar characteristics in terms of tracking performance and following distances, participants in a pilot study rated the direction-following behavior as significantly more human-like and natural than the path-following behavior. We argue that the path-following method may still be more appropriate in some situations, and we propose that the ideal person-following behavior may be a hybrid approach, with the robot automatically selecting which method to use.

Ambler: an autonomous rover for planetary exploration

The authors are building a prototype legged rover, called the Ambler (loosely an acronym for autonomous mobile exploration robot) and testing it on full-scale, rugged terrain of the sort that might be encountered on the Martian surface. They present an overview of their research program, focusing on locomotion, perception, planning, and control. They summarize some of the most important goals and requirements of a rover design and describe how locomotion, perception, and planning systems can satisfy these requirements. Since the program is relatively young (one year old at the time of writing) they identify issues and approaches and describe work in progress rather than report results. It is expected that many of the technologies developed will be applicable to other planetary bodies and to terrestrial concerns such as hazardous waste assessment and remediation, ocean floor exploration, and mining.<<ETX>>

Approaches for heuristically biasing RRT growth

This paper presents several modifications to the basic rapidly-exploring random tree (RRT) search algorithm. The fundamental idea is to utilize a heuristic quality function to guide the search. Results from a relevant simulation experiment illustrate the benefit and drawbacks of the developed algorithms. The paper concludes with several promising directions for future research.