We consider the problem of coverage and exploration of an unknown dynamic environment using a mobile robot(s). The environment is assumed to be large enough such that constant motion by the robot(s) is needed to cover the environment. We present an e#cient minimalist algorithm which assumes that global information is not available (neither a map, nor GPS). Our algorithm deploys a network of radio beacons which assists the robot(s) in coverage. This network is also used for navigation. The deployed network can also be used for applications other than coverage. Simulation experiments are presented which show the collaboration between the deployed network and mobile robot(s) for the tasks of coverage/exploration, network deployment and maintenance (repair), and mobile robot(s) recovery (homing behavior). We present a theoretical basis for our algorithm on graphs and show the results of the simulated scenario experiments.

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.

We study the problem of exploring an unknown environment using a single robot. The environment is large enough (and possibly dynamic) that constant motion by the robot is needed to cover the environment. We term this the dynamic coverage problem. We present an efficient minimalist algorithm which assumes that global information is not available to the robot (neither a map, nor GPS). Our algorithm uses markers which the robot drops off as signposts to aid exploration. We conjecture that our algorithm has a cover time better than O(n log n), where the n markers that are deployed form the vertices of a regular graph. We provide experimental evidence in support of this conjecture. We show empirically that the performance of our algorithm on graphs is similar to its performance in simulation.

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.

While significant recent progress has been made in development of mobile robots for planetary surface exploration, there remain major challenges. These include increased autonomy of operation, traverse of challenging terrain, and fault-tolerance under long, unattended periods of use. We have begun work which addresses some of these issues, with an initial focus on problems of “high risk access, ” that is, autonomous roving over highly variable, rough terrain. This is a dual problem of sensing those conditions which require rover adaptation, and controlling the rover actions so as to implement this adaptation in a well understood way (relative to metrics of rover stability, traction, power utilization, etc.). Our work progresses along several related technical lines: 1) development a fused state estimator which robustly integrates internal rover state and externally sensed environmental information to provide accurate “configuration ” information; 2) kinematic and dynamical stability analysis of such configurations so as to determine “predicts ” for a needed change of control regime (e.g., traction control, active c.g. positioning, rover shoulder stance/pose); 3) definition and implementation of a behavior-based control architecture and action-selection strategy which autonomously sequences multi-level rover controls and reconfiguration. We report on these developments, both software simulations and hardware experimentation. Experiments include reconfigurable control of JPL's Sample Return Rover geometry and motion during its autonomous traverse over simulated Mars terrain.

We consider the dynamic sensor coverage problem in the absence of global localization information. In the regime where few sensors are available compared to the size of the space being explored, a successful strategy must e#ectively mobilize the sensors by mounting them on mobile robots. We present here an approach where mobile robots explore an uncharted environment, by deploying small communication beacons. These beacons act as local markers for preferred directions of further exploration. The robots never acquire a map of their surroundings, nor are localized, however they ensure timely coverage of all regions of the space by relying on the local instructions disseminated by the stationary communication beacons. Preliminary data from experiments suggest that our algorithm produces exploratory, patrol-like behavior, resulting in good spatial sensor coverage over time.

Behavior selection is typically a “built-in” feature of behavior-based architectures and hence not amenable to change. There are, however, circumstances where changing behavior selection strategies is useful and can lead to better performance. In this paper, we demonstrate that such dynamic changes of behavior selection mechanisms are beneficial in several circumstances. We first categorize existing behavior selection mechanisms along three dimensions and then discuss seven possible circumstances where dynamically switching among them can be beneficial. Using the agent architecture framework APOC, we show how instances of all (non-empty) categories can be captured and how additional architectural mechanisms can be added to allow for dynamic switching among them. In particular, we propose a generic architecture for dynamic behavior selection, which can integrate existing behavior selection mechanisms in a unified way. Based on this generic architecture, we then verify that dynamic behavior selection is beneficial in the seven cases by defining architectures for simulated and robotic agents and performing experiments with them. The quantitative and qualitative analyses of the results obtained from extensive simulation studies and experimental runs with robots verify the utility of the proposed mechanisms.

this paper we concentrate on the formal and theoretical results. Thus, in Section 6 we provide a mobile robot navigation example task for the purpose of characterizing the behavior coordination mechanisms that we can formulate using the presented multiple objective approach. We conclude with a discussion of results and directions for future work.

Contention scheduling (Cooper & Shallice 2000) is a wellknown action selection mechanism in cognitive science. It can account for normal action sequencing of daily routine actions in humans as well as for various errors exhibited by impaired human subjects. In this paper, we examine the potential of contention scheduling as an action selection mechanism for artificial agents, in particular robots. We first introduce the APOC architecture framework in order to summarize--in an "architecture-neutral" specification--the basic properties of the contention scheduling model. Then we analyze various aspects of contention scheduling that may cause problems in the context of the design of artificial agents and suggest modifications that may be able to overcome the difficulties, concluding that the contention scheduling model, as it stands, is not yet an appriopriate candidate for action selction in artificial agents.

We introduce a computationally efficient methodology for generating and recognizing free-space movements for humanoid robots. This methodology operates on exemplar-based representations of behaviors. Our method for actuating humanoid robots allows us to perform variations on a given behavior, resulting in a very humanlike movement appearance. Besides control, this method also facilitates classification of perceived human and humanoid robot movement. We demonstrate the method on a physically-simulated humanoid robot with 132 degrees-offreedom and evaluate our movement classification methodology on two data sets: human motion-capture data and joint-angle data sampled from the simulated robot.