New reactive behaviors that implement formations in multi-robot teams are presented and evaluated. The formation behaviors are integrated with other navigational behaviors to enable a robotic team to reach navigational goals, avoid hazards and simultaneously remain in formation. The behaviors are implemented in simulation, on robots in the laboratory and aboard DARPA's HMMWV-based Unmanned Ground Vehicles. The technique has been integrated with the Autonomous Robot Architecture (AuRA) and the UGV Demo II architecture. The results demonstrate the value of various types of formations in autonomous, human-led and communications-restricted applications, and their appropriateness in different types of task environments.

Potential function approaches to robot navigation provide an elegant paradigm for expressing multiple constraints and goals in mobile robot navigation problems [9]. As an example, a simple reactive navigation strategy can be generated by combining repulsion from obstacles with attraction to a goal. Advantages of this approach can also be extended to multi-robot teams. In this paper we present a new class of potential functions for multiple robots that enables homogeneous largescale robot teams to arrange themselves in geometric formations while navigating to a goal location through an obstacle field. The approach is inspired by the way molecules "snap" into place as they form crystals; the robots are drawn to particular "attachment sites" positioned with respect to other robots. We refer to these potential functions as "social potentials" because they are constructed with respect to other agents. Initial results, generated in simulation, illustrate the viability of the approach. 1 Int...

Abstract—The coordination of a large group of robots to solve a specified task is a difficult problem. Centralized approaches can be computationally intractable, brittle, and unresponsive to change. Distributed approaches are not as prone to these problems, but they can be highly sub-optimal. This work introduces a novel economic approach for coordinating robots based on the free market system. The free market approach defines revenue and cost functions across the possible plans for executing a specified task. The task is accomplished by dividing it into sub-tasks and allowing the robots to bid and negotiate to carry out these sub-tasks. Cooperation and competition emerge as the robots execute the task while trying to maximize their personal profits. Initial simulation results indicate the approach is successful at producing effective global plans for a team of several robots performing an interior sensing task. I.

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies or endorsements, either expressed or implied, of Carnegie Mellon University. The problem of efficient multirobot coordination has risen to the forefront of robotics research in recent years. Interest in this problem is motivated by the wide range of application domains demanding multirobot solutions. In general, multirobot coordination strategies assume either a centralized approach, where a single robot/agent plans for the group, or a distributed approach, where each robot is responsible for its own planning. Inherent to many centralized approaches are difficulties such as intractable solutions for large groups, sluggish response to changes in the local environment, heavy communication requirements, and brittle systems with single points of failure. The key advantage of centralized approaches is that they can produce globally optimal plans. While most distributed approaches can overcome the obstacles inherent to centralized approaches, they can only produce suboptimal plans. This work explores the development of a market-based architecture that will be inherently distributed, but will also opportunistically form centralized sub-groups to improve efficiency, and thus

Abstract not found

This paper describes research investigating behavioral specialization in learning robot teams. Each agent is provided a common set of skills (motor schema-based behavioral assemblages) from which it builds a taskachieving strategy using reinforcement learning. The agents learn individually to activate particular behavioral assemblages given their current situation and a reward signal. The experiments, conducted in robot soccer simulations, evaluate the agents in terms of performance, policy convergence, and behavioral diversity. The results show that in many cases, robots will automatically diversify by choosing heterogeneous behaviors. The degree of diversification and the performance of the team depend on the reward structure. When the entire team is jointly rewarded or penalized (global reinforcement), teams tend towards heterogeneous behavior. When agents are provided feedback individually (local reinforcement), they converge to identical policies. Introduction Individuals in n...

In the extreme environments posed by war fighting, fire fighting, and nuclear accident response, the cost of direct human exposure is levied in terms of injury and death. Robotic alternatives must address effective operations while removing humans from danger. This is profoundly challenging, as extreme environments inflict cumulative performance damage on exposed robotic agents. Sensing and perception are among the most vulnerable components. We present a distributed robotic system that enables autonomous reconnaissance and mapping in urban structures using teams of robots. Robot teams scout remote sites, maintain operational tempos, and successfully execute tasks, principally the construction of 3-D Maps, despite multiple agent failures. Using an economic model of agent interaction based on a free market architecture, a virtual platform (a robot colony) is synthesized where task execution does not directly depend on individual agents within the colony.

Behavior-based systems (BBS) have been effective in a variety of applications, but due to their limited use of representation they have not been applied much for more complex problems, such as ones involving temporal sequences, or hierarchical task representations. This paper presents a Hierarchical Abstract Behavior Architecture that allows for the representation and execution of complex, sequential, hierarchically structured tasks within a behavior-based framework. The architecture, obtained by introducing the notion of abstract behaviors, also enables reusability of behaviors across different tasks. The basis for task representation is the behavior network construct which encodes complex, hierarchical plan-like strategies. The approach is validated in experiments on a Pioneer 2DX mobile robot.

This paper presents techniques for exploiting redundancy in teams of mobile robots. In particular, we address tasks involving the kinematic coordination of several communicating robots. Teams are modeled as highly redundant spatial mechanisms for which multi-objective, concurrent controllers are constructed using a generalization of nullspace control. The goal is to develop a methodology in which the robustness and error suppression in a control theoretic substrate can be used to preserve critical properties in teams of reactive robots. The resulting "safe" control options can then be explored while guaranteeing global compliance with system specifications. The proposed architecture depends on a set of concurrent, low-dimensional control processes that interact in a welldefined manner. Cascaded null space projections and coordination templates are used to manage control interactions across platforms that actively maintain constraints for pairs of robots. Pairwise policies can then be combined to represent coordinated, multi-robot tasks. To illustrate the approach, we demonstrate a distributed control that maintains critical connectivity in line-of-sight communication networks.

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies or endorsements, either expressed or implied, of Carnegie Mellon University. The coordination of a large group of robots to solve a specified task is a difficult problem. Centralized approaches can be computationally intractable, brittle, and unresponsive to change. Distributed approaches are not as prone to these problems, but they can be highly sub-optimal. This work introduces a novel approach for coordinating robots based on the free market system. Market economies are a proven way to organize a large number of individuals into a productive group. The free market approach defines revenue and cost functions across the possible plans for executing a specified task. The task is accomplished by dividing it into sub-tasks and allowing the robots to bid and negotiate to carry out these sub-tasks. Cooperation and competition emerge as the robots execute the task while trying to maximize their personal profits. The result promises to be a highly robust multirobot team that can efficiently exploit resources and opportunistically deal with uncertainties in a dynamic environment. A detailed example of how this model could be applied to a foraging task is presented and the different characteristics of the approach are highlighted in the context of the example. The ability to scale this approach to