David W. Payton

Pheromone Robotics

We describe techniques for coordinating the actions of large numbers of small-scale robots to achieve useful large-scale results in surveillance, reconnaissance, hazard detection, and path finding. We exploit the biologically inspired notion of a “virtual pheromone,” implemented using simple transceivers mounted atop each robot. Unlike the chemical markers used by insect colonies for communication and coordination, our virtual pheromones are symbolic messages tied to the robots themselves rather than to fixed locations in the environment. This enables our robot collective to become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface. This leads to notions of world-embedded computation and world-embedded displays that provide different ways to think about robot colonies and the types of distributed computations that such colonies might perform.

Compound behaviors in pheromone robotics

Abstract We are pursuing techniques for coordinating the actions of large numbers of small-scale robots to achieve useful large-scale results in surveillance, reconnaissance, hazard detection, and path finding. Using the biologically inspired notion of “virtual pheromone” messaging, we describe how many coordinated activities can be accomplished without centralized control. By virtue of this simple messaging scheme, a robot swarm can become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface. We further describe a set of logical primitives for controlling the flow of virtual pheromone messages throughout the robot swarm. These enable the design of complex group behaviors mediated by messages exchanged between neighboring robots.

Intelligent real-time control of robotic vehicles

This article describes the joint research project between Ohio State University and Adaptive Machine Technology which employs a knowledge- based real-time control system to control the motion of the Adaptive Suspension Vehicle (ASV). The ASV is controlled by a multiprocessing computer with seven Intel 80386 microprocessors receiving information from over 100 vehicle sensors. The article describes efforts to develop new methods for partitioning reasoning efforts: allocating computing power on important problems and determining the importance of a given problem in relation to other problems

World embedded interfaces for human-robot interaction

Human interaction with large numbers of robots or distributed sensors presents a number of difficult challenges including supervisory management, monitoring of individual and collective state, and apprehending situation awareness. A rich source of information about the environment can be provided even with robots that have no explicit representations or maps of their locale. To do this, we transform a robot swarm into a distributed interface embedded within the environment. Visually, each robot acts like a pixel within a much larger visual display space so that any robot need only communicate a small amount of information from its current location. Our approach uses augmented reality techniques for communicating information to humans from large numbers of small-scale robots to enable situation awareness, monitoring, and control for surveillance, reconnaissance, hazard detection, and path finding.

Dynamic collaborator discovery in information intensive environments

As organizations continue to grow more complex, people have increasing difficulty remaining aware of others whose work could impact their own. Despite the rapid proliferation of our communication networks, we often hear the complaint “the right hand doesn’t know what the left hand is doing.” This is a particularly important issue within large organizations and could be reduced if people could more easily discover potential collaborators. As organizations continuously re-organize, it is often the case that one person comes to be working on a problem that someone else has already solved. Likewise, a particular customer need might be more readily satisfied with the efficient identification of the appropriate individuals to assemble into a team. Coordination between people with common interests is a particularly important issue within the military intelligence community. While focusing on a specific region or political arena, for example, the significance of a certain subtle detail may not be apparent to an analyst until it can be placed in a “big picture” view that spans multiple regional and political arenas. The analyst who can find the right people to contact to help assemble this larger view is most likely to succeed. The goal of “collaborator discovery” is to help people with similar interests find each other with minimal effort.

Coalitions for distributed sensor fusion

We address the problem of efficient use of communication bandwidth in a network of distributed sensors. Each sensor node has enough computational power to fuse its own estimates with those of other nodes using an optimal filter, but message passing is expensive. The goal is to give each node an identical tactical picture of the area of interest without compromising target track accuracy. In the current leading approach, each sensing node sends an Associated Measurement Report (AMR: a sensor reading matched to a track ID) to every other node. This paper considers a method of decimation of reporting nodes by choosing a subset (coalition) of those nodes that have the best information on the target. Only coalition nodes share AMRs with each other; then the coalition sends a report to non-coalition nodes. Simulations and analytical studies are used to support the promise of the coalition approach.

Amorphous Predictive Nets

This paper describes our approaches for coordinating the actions of extremely large numbers of distributed, loosely connected, embedded computing elements. In such networks, centralized control and information processing is impractical. If control and processing can be decentralized, the communications bottleneck is removed and the system becomes more robust. Since conventional computing paradigms provide limited insight into such decentralized control, we look to biology for inspiration.

Optimization by Self-Organized Criticality

Self-organized criticality (SOC) is a phenomenon observed in certain complex systems of multiple interacting components, e.g., neural networks, forest fires, and power grids, that produce power-law distributed avalanche sizes. Here, we report the surprising result that the avalanches from an SOC process can be used to solve non-convex optimization problems. To generate avalanches, we use the Abelian sandpile model on a graph that mirrors the graph of the optimization problem. For optimization, we map the avalanche areas onto search patterns for optimization, while the SOC process receives no feedback from the optimization itself. The resulting method can be applied without parameter tuning to a wide range of optimization problems, as demonstrated on three problems: finding the ground-state of an Ising spin glass, graph coloring, and image segmentation. We find that SOC search is more efficient compared to other random search methods, including simulated annealing, and unlike annealing, it is parameter free, thereby eliminating the time-consuming requirement to tune an annealing temperature schedule.

Behavior-based control for an eye-head system

Much of the recent interest in active vision has focussed on the development of novel methods for controlling fast pan/tilt camera mounts, called eye-head systems. Simple real-time processing of input images coupled with fast control has enabled interesting system behaviors. This paper describes the ongoing development of behavior-based control methods for a miniature eye-head system. We first describe the eye-head hardware and image processing system. We then define and present approaches for behavior-based control of the eye-head system. Finally, we discuss results from the use of simple behaviors for verging two cameras on moving objects.