When robots work together as a team, the members that perform each task should be the ones that promise to use the least resources to do the job.

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—Exploration of high risk terrain areas such as cliff faces and site construction operations by autonomous robotic systems on Mars requires a control architecture that is able to autonomously adapt to uncertainties in knowledge of the environment. We report on the development of a software/hardware framework for cooperating multiple robots performing such tightly coordinated tasks. This work builds on our earlier research into autonomous planetary rovers and robot arms. Here, we seek to closely coordinate the mobility and manipulation of multiple robots to perform examples of a cliff traverse for science data acquisition, and site construction operations including grasping, hoisting, and transport of extended objects such as large array sensors over natural, unpredictable terrain. In support of this work we have developed an enabling distributed control architecture called control architecture for multirobot planetary outposts (CAMPOUT) wherein integrated multirobot mobility and control mechanisms are derived as group compositions and coordination of more basic behaviors under a task-level multiagent planner. CAMPOUT includes the necessary group behaviors and communication mechanisms for coordinated/cooperative control of heterogeneous robotic platforms. In this paper, we describe CAMPOUT, and its application to ongoing physical experiments with multirobot systems at the Jet Propulsion Laboratory in Pasadena, CA, for exploration of cliff faces and deployment of extended payloads. Index Terms—Distributed control architecture, multiple mobile robots, robot outposts, tight coordination. I.

This paper presents an architecture that enables multiple robots to explicitly coordinate actions at multiple levels of abstraction. In particular, we are developing an extension to the traditional three-layered robot architecture that enables robots to interact directly at each layer -- at the behavioral level, the robots create distributed control loops; at the executive level, they synchronize task execution; at the planning level, they use market-based techniques to assign tasks, form teams, and allocate resources. We illustrate these ideas through applications in multi-robot assembly, multi-robot deployment, and multi-robot mapping.

Abstract — This paper describes a methodology for automatically synthesizing task solutions for heterogeneous multi-robot teams. In contrast to prior approaches that require a manual predefinition of how the robot team will accomplish its task (while perhaps automating who performs which task), our approach automates both the how and the who to generate task solution approaches that were not explicitly defined by the designer a priori. The advantages of this new approach are that it: (1) enables the robot team to synthesize new task solutions that use fundamentally different combinations of robot behaviors for different team compositions, and (2) provides a general mechanism for sharing sensory information across networked robots, so that more capable robots can assist less capable robots in accomplishing their objectives. Our approach, which we call ASyMTRe (Automated Synthesis of Multi-robot Task solutions through software Reconfiguration, pronounced “Asymmetry”), is based on mapping environmental, perceptual, and motor control schemas to the required flow of information through the multirobot system, automatically reconfiguring the connections of schemas within and across robots to synthesize valid and efficient multi-robot behaviors for accomplishing the team objectives. We validate this approach by presenting the results of applying our methodology to two different teaming scenarios: altruistic cooperation involving multi-robot transportation, and coalescent cooperation involving multi-robot box pushing. Index Terms — Multi-robot teams, behavior synthesis I.

In this paper we present a new class of tasks for multi-robot teams: those that require constant complex interaction between teammates. Much research has been done in the area of multi-robot coordination, but no existing framework meets the technical demands of such tasks. We have developed Hoplites in response to the need for a more capable framework. Hoplites is a market-based framework that couples planning with both passive and active coordination strategies. It enables robots to change coordination strategies as the needs of the task change. Further, it efficiently facilitates tight coordination between multiple robots. We compare the performances of Hoplites and existing coordination frameworks in a security sweep domain. Our results show that Hoplites significantly improves the quality of solutions found by the team, particularly in the most complex instances of the domain.

Abstract — This paper describes a general approach for automatically synthesizing task solutions for heterogeneous robot teams. In particular, our approach enables multiple robots to coalesce into teams to solve a task through tightly-coupled sensor sharing. Instead of designing special solution strategies for the team, our ASyMTRe approach enables the robot team to generate solutions autonomously according to the current robot team composition. In this paper, we first formulate the problems that the ASyMTRe approach addresses, and then present the anytime ASyMTRe configuration algorithm. We prove that the configuration algorithm is correct, and is guaranteed to find the optimal solution given enough time. Empirical results are also presented validating this analysis, and showing that the ASyMTRe configuration algorithm has good scalability and can quickly find a good solution with the solution quality increasing as additional planning time is available. By analyzing the configuration algorithm, we show that ASyMTRe is applicable to a large class of challenging multi-robot problems. I.

We are developing a coordinated team of robots to assemble structures. The assembly tasks are sufficiently complex that no single robot, or type of robot, can complete the assembly alone. Even with a group of multiple heterogeneous robots, each adding its unique set of capabilities to the system, the number of contingencies that must be addressed for a completely autonomous system is prohibitively large. Teleoperating a multiple robot system, at the other extreme, is difficult and performance may be highly dependent on the skill of the operator. We propose and evaluate an implementation of a framework that, ideally, provides the operator with a means to interact seamlessly with the autonomous control system. Using an architecture that incorporates sliding autonomy, the operator can augment autonomous control by providing input to help the system recover from unexpected errors and increase system efficiency. Our implementation is motivated by results from an extended series of experiments we are conducting with three robots that work together to dock both ends of a suspended beam.

Imagine the following situation. You give your favorite robot, named Pippi, the task to fetch a parcel that just arrived at your front door. While pushing the parcel back to you, she must travel through a door opening. Unfortunately, the parcel she is pushing is blocking her camera, giving her a hard time to see the door to cross. If she cannot see the door, she cannot safely push the parcel through the door opening. What would you as a human do in a similar situation? Most probably you would ask someone for help, someone to guide you through the door, as we ask for help when we need to park our car in a tight parking spot. Why not let the robots do the same? Why not let robots help each other? Luckily for Pippi, there is another robot, named Emil, vacuum cleaning the floor in the same room. Since Emil can view both Pippi and the door at the same time, he can guide Pippi through the door, enabling her to deliver the parcel to you. This work is about societies of autonomous robots in which robots can

Abstract — This paper presents an approach that enables heterogeneous robots to automatically form groups as needed to generate both strongly-cooperative and weakly-cooperative multi-robot task solutions in the same application. The fundamental contribution of this work is the layering of our lowlevel coalition formation algorithm for generating stronglycooperative task solutions, with high-level, traditional task allocation methods for weakly-cooperative task solutions. At the low level, coalitions that generate strongly-cooperative multi-robot task solutions are formed using our ASyMTRe-D approach that maps environmental sensors and perceptual and motor schemas to the required flow of information in the robot team, automatically reconfiguring the connections of schemas within and across robots to form efficient solutions. At the high level, a traditional task allocation approach is used to enable individual robots and/or coalitions to compete for weakly-cooperative task assignments through task allocation. We introduce the site clearing task to motivate the work, and then formalize the problem. We then present the approach of layering ASyMTRe-D with task allocation. We validate the approach on a team of robots with the site clearing task. We believe the resulting approach is a flexible system that can handle a broad range of realistic multi-robot applications beyond what is possible using other existing approaches. I.