Chris V. Jones

Analysis of Dynamic Task Allocation in Multi-Robot Systems

Dynamic task allocation is an essential requirement for multi-robot systems operating in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of tasks, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of the collective behavior. Specifically, we analyze the effect that the number of observations and the choice of the decision function have on the performance of the system. The mathematical models are validated in a multi-robot multi-foraging scenario. The models predictions agree very closely with results of embodied simulations.

From local to global behavior in intelligent self-assembly

In this paper we present a method by which to organize the interactions of autonomous assembly agents in intelligent self-assembly (ISA). Each assembly agent has limited and local sensing and local rule-based control. A transition rule set (TRS) compiler is presented which takes a desired goal structure as input and gives as output a set of rules. When each assembly agent utilizes these rules, the desired goal structure is assembled. We show that the TRS compiler is scalable, efficient, and is capable of producing rules for a large class of goal structures.

Adaptive division of labor in large-scale minimalist multi-robot systems

A large-scale minimalist multi-robot system (LMMS) is one composed of a group of robots each with limited capabilities in terms of sensing, computation, and communication. Such systems have received increased attention due to their empirically demonstrated performance and beneficial characteristics, such as their robustness to environmental perturbations and individual robot failure and their scalability to large numbers of robots. However, little work has been done in investigating ways to endow such a LMMS with the capability to achieve a desired division of labor over a set of dynamically evolving concurrent tasks, important in many task-achieving LLMS. Such a capability can help to increase the efficiency and robustness of overall task performance as well as open new domains in which LMMS can be seen as a viable alternative to more complex control solutions. In this paper, we present a method for achieving a desired division of labor in a LMMS, experimentally validate it in a realistic simulation, and demonstrate its potential to scale to large numbers of robots and its ability to adapt to environmental perturbations.

Automatic synthesis of communication-based coordinated multi-robot systems

To enable the successful deployment of task-achieving multi-robot systems (MRS), coordination mechanisms must be utilized in order to effectively mediate the interactions between the robots and the task environment. Over the past decade, there have been a number of elegant experimentally demonstrated MRS coordination mechanisms. Most of these mechanisms have been task-specific in nature, typically providing only empirical insights into coordination design and little in the way of systematic techniques to assist in the design of coordinated MRS for new task domains. To fully realize the potentials of MRS, formally-grounded systematic techniques amenable to analysis are needed in order to facilitate the design of coordinated MRS. We address this problem by presenting a formal framework for describing and reasoning about coordination in a MRS. Using this principled foundation, we are developing a suite of general methods for automatically synthesizing the controllers of robots constituting a MRS such that the given task is performed in a coordinated fashion. This paper presents a method for the automatic synthesis of a specific type of controller, one that is stateless but capable of inter-robot communication. We also present a graph coloring-based approach for minimizing the number of necessary unique communication messages. The synthesis of such communicative controllers provides a means for assessing the uses and limitations of communication in MRS coordination. We present experimental validation of our formal approach of controller synthesis in a multi-robot construction domain through physically-realistic simulations and in real-robot demonstrations.

Mobile human-robot teaming with environmental tolerance

We demonstrate that structured light-based depth sensing with standard perception algorithms can enable mobile peer-to-peer interaction between humans and robots. We posit that the use of recent emerging devices for depth-based imaging can enable robot perception of non-verbal cues in human movement in the face of lighting and minor terrain variations. Toward this end, we have developed an integrated robotic system capable of person following and responding to verbal and non-verbal commands under varying lighting conditions and uneven terrain. The feasibility of our system for peer-to-peer HRI is demonstrated through two trials in indoor and outdoor environments.

Practical problems in sliding scale autonomy: a case study

Sliding scale autonomy has been suggested as a practical way of organizing robot controls. In this paper, we investigate practical problems in sliding scale autonomy. We take a constructionist approach of building systems with sliding scale autonomy and identifying the challenges involved. We implement robot systems capable of operating semi-autonomously with user guidance. We describe the techniques used to construct the system, the problems discovered along the way, and the improvements achieved. We discuss key parts of two robotic systems we have constructed. We describe our user interface, obstacle avoidance, and localization as implemented on the robot systems.

Synthesis and Analysis of Non-Reactive Controllers for Multi-Robot Sequential Task Domains

In this paper we present a macroscopic model for the analysis of homogeneous task-directed multi-robot systems (MRS). The model is used to compute the probability that a given MRS will correctly execute a given sequential Markovian task. We consider distributed MRS composed of non-communicative robots that maintain a limited amount of non-transient internal state. The model shares a common formal framework with our past work on the development of systematic methods for the synthesis of MRS. As such, it can be used to improve design procedures by providing an analytical approach to the evaluation of design decisions. The unified nature of the modeling and synthesis methods are part of our ongoing work toward a general, comprehensive and principled MRS design methodology. We apply the model to the analysis of system performance in a multi-robot construction task domain. Our past work on synthesis methods provides robot controllers for this task domain; the model provides quantitative predictions of system performance. We discuss the assumptions and limitations inherent in the model and discuss how the complementary synthesis and analysis methods may be more fully integrated.

Ergodic Dynamics by Design: A Route to Predictable Multi-Robot Systems

We define and discuss a class of multi-robot systems possessing ergodic dynamics and show that they are realizable on physical hardware and useful for a variety of tasks while being amenable to analysis. We describe robot controllers synthesized to possess these dynamics and also physics-based methodologies that allow macroscopic structures to be uncovered and exploited for task execution in systems with large numbers of robots.