In this chapter, we demonstrate the e ectiveness of behavior-based control in facilitating the development and evaluation of multi-robot controllers that are: (1) robust to robot failures, and (2) easily modi ed to facilitate development of the controller variation that su ciently satis es the design requirements for the task. Our experimental focus here is distributed multi-robot collection, a class of tasks that includes de-mining and toxic waste clean-up. We demonstrate a basic, homogeneous multi-robot controller for the collection task, then show how to easily derive two heterogeneous, spatio-temporal variations with markedly di erent performance properties. We evaluate the desirability of these controllers with respect to design requirements involving inter-robot interference, time-to-completion, and energy expenditure. The data for evaluation come from experiments using four physical mobile robots performing the three variations of the collection task. 1

In behavior-based robotics the control of a robot is shared between a set of purposive perception-action units, called behaviors. Based on selective sensory information, each behavior produces immediate reactions to control the robot with respect to a particular objective, i.e., a narrow aspect of the robot's overall task such as obstacle avoidance or wall following. Behaviors with di erent and possibly incommensurable objectives may produce con icting actions that are seemingly irreconcilable. Thus a major issue in the design of behavior-based control systems is the formulation of e ective mechanisms for coordination of the behaviors' activities into strategies for rational and coherent behavior. This is known as the action selection problem (also refereed to as the behavior coordination problem) and is the primary focus of this overview paper. Numerous action selection mechanisms have been proposed over the last decade and the main objective of this document istogive a qualitative overview of these approaches. 2 1

Despite the relatively high volume of research conducted on evolutionary multiobjective optimization in the last few years, little attention has been paid to the decision making process that is required to select a final solution to the multiobjective optimization problem at hand. This paper reviews the most important preference handling approaches used with evolutionary algorithms, analyzing their advantages and disadvantages, and then, it proposes some of the potential areas of future research in this discipline. 1 Introduction Most real-world problems are multiobjective in nature, because they consider several objectives (or alternatives) that are to be optimized simultaneously. Normally, these objectives are non-commensurable (i.e., they are measured in different units), and are in conflict with each other. Multiobjective optimization problems (MOPs) have received considerable attention in Operations Research (see for example [23, 7, 27, 12]), and they have recently become a very ...

A manned Mars habitat will require a significant amount of infrastructure that can be deployed using robotic precursor missions. This infrastructure deployment will probably include the use of multiple, heterogeneous, mobile robotic platforms. Delays due to the long communication path to Mars limit the amount of teleoperation that is possible. A control architecture called CAMPOUT (Control Architecture for Multirobot Planetary Outposts) is currently under development at the Jet Propulsion Lab in Pasadena, CA. It is a three layer behavior-based system that incorporates the low level control routines currently used on the JPL SRR/FIDO/LEMUR rovers. The middle behavior layer uses either the BISMARC (Biologically Inspired System for Mapbased Autonomous Rover Control) or MOBC (Multi-Objective Behavior Control) action selection mechanisms. CAMPOUT includes the necessary group behaviors and communication mechanisms for coordinated/cooperative control of heterogeneous robotic platforms. We report the results of some ongoing work at the Jet Propulsion Lab in Pasadena, CA on the transport phase of a photovoltaic (PV) tent deployment mission.

We consider “collaborative control” systems, where multiple sources share control of a single robot. These sources could come from multiple sensors (sensor fusion), multiple control processes (subsumption), or multiple human operators. Reports suggest that such systems are highly fault tolerant, even with large numbers of sources. In this paper we develop a formal model, modeling sources with finite automata. A collaborative ensemble of sources generates a single stream of incremental steps to control the motion of a point robot moving in the plane. We first analyze system performance with a uniform ensemble of well-behaved deterministic sources. We then model malfunctioning sources that go silent or generate inverted control signals. We discover that performance initially improves in the presence of malfunctioning sources and remains robust even when a sizeable fraction of sources malfunction. Initial tests suggest similar results with non-deterministic (random) sources. The formal model may also provide insight into how humans can share control of an online robot.

This paper formulates analysis in terms of spatial interest functions and consensus regions, and presents system architecture, interface, and algorithms for processing voting data

We demonstrate the effectiveness of behavior-based control in facilitating the development and evaluation of multirobot controllers that are: (1) robust to robot failures, and (2) easily modified to facilitate development of the controller variation that sufficiently satisfies the design requirements for the task. Our experimental focus here is distributed multirobot collection, a class of tasks that includes de-mining and toxic waste clean-up. We demonstrate a basic multi-robot controller for the collection task, then show how to easily derive two spatio-temporal variations with markedly different performance properties. We evaluate the desirability of these controllers with respect to design requirements involving inter-robot interference, time-to-completion, and energy expenditure. The data for evaluation come from experiments using four physical mobile robots performing the three variations of the collection task.

This paper describes Version 3.0 of the system architecture, SDV interface, algorithms for automated goal selection, and metrics for collaboration and leadership. We report results from a July 2001 field test with 56 remote users. See: www.tele-actor.net

This paper describes the software framework that has been developed and that it is being used to build a control architecture for the navigation of a B21 mobile robot. The subsumption architecture and the multi-agent based controller architectures are analyzed and a software framework named SORGIN is presented as a structured programming tool that allows to deal with the complexity of mobile robotic systems. We show how a task that performs safe wandering with a privileged compass orientation has been defined using di#erent behavior bricks in the SORGIN framework.

The ability of a robot to improve its performance on a task can be critical, especially in poorly known and non-stationary environments where the best action or strategy is dependent upon the current state of the environment. In such systems, a good estimate of the current state of the environment is key to establishing high performance, however quantified. In this paper, we present an approach to state estimation in poorly known and non-stationary mobile robot environments, focusing on its application to a mine collection scenario where performance is quantified using reward maximization. The approach is based on the use of augmented Markov models (AMMs), a sub-class of semi-Markov processes. We have developed an algorithm for incrementally constructing arbitrary-order AMMs online. It is used to capture the interaction dynamics between a robot and its environment in terms of behavior sequences executed during the performance of a task. For the purposes of reward maximization in a non-stationary environment, multiple AMMs monitor events at different timescales and provide statistics used to select the AMM likely to have a good estimate of the environmental state. AMMs with redundant or outdated information are discarded, while attempting to maintain sucient data to reduce conformation to noise. This approach has been successfully implemented on a mobile robot performing a mine collection task. In the context of this task, we first present experimental results validating our reward maximization performance criterion. We then incorporate our algorithm for state estimation using multiple AMMs, allowing the robot to select appropriate actions based on the estimated state of the environment. The approach is tested first with a physical robot, in a non-stationary environment...