Gal A. Kaminka

Multi-robot perimeter patrol in adversarial settings

This paper considers the problem of multi-robot patrol around a closed area with the existence of an adversary attempting to penetrate into the area. In case the adversary knows the patrol scheme of the robots and the robots use a deterministic patrol algorithm, then in many cases it is possible to penetrate with probability 1. Therefore this paper considers a non-deterministic patrol scheme for the robots, such that their movement is characterized by a probability p. This patrol scheme allows reducing the probability of penetration, even under an assumption of a strong opponent that knows the patrol scheme. We offer an optimal polynomial-time algorithm for finding the probability p such that the minimal probability of penetration detection throughout the perimeter is maximized. We describe three robotic motion models, defined by the movement characteristics of the robots. The algorithm described herein is suitable for all three models.

GameBots: a flexible test bed for multiagent team research

GameBots [1] is a virtual reality platform that allows the creation and evaluation of intelligent agents that interact with a rich 3D continuous dynamic environment. As opposed to previous test beds that focus on a single task and environment (such as soccer simulation [4]), GameBots does not define a single benchmark task. Instead, the GameBots platform comes with a wide variety of predefined tasks and environments and allows anyone to extend these in various ways, or create new challenges. This enables multiagent systems (MAS) and artificial intelligence researchers to explore a wide variety of algorithms and techniques, in areas such as spatial navigation, learning, dynamic resource allocation, multiagent planning, plan-recognition, collaboration, distributed adversarial planning, and human-machine teamwork. GameBots is composed of two components. The first of these is a freely-available open source extension of the commercial Unreal Tournament game engine [3]. It defines a socket-based API allowing anyone to create agents that can participate in any Unreal Tournament games. The second component is a set of development tools, sample source code, and nonviolent graphics (replacements for the default graphics) that form a basic development environment to help users get started in using GameBots. Gal A. Kaminka, Manuela M. Veloso, Steve Schaffer,

Multi-Robot Area Patrol under Frequency Constraints

This paper discusses the problem of generating patrol paths for a team of mobile robots inside a designated target area. Patrolling requires an area to be visited repeatedly by the robot(s) in order to monitor its current state. First, we present frequency optimization criteria used for evaluation of patrol algorithms. We then present a patrol algorithm that guarantees maximal uniform frequency, i.e., each point in the target area is covered at the same optimal frequency. This solution is based on finding a circular path that visits all points in the area, while taking into account terrain directionality and velocity constraints. Robots are positioned uniformly along this path, using a second algorithm. Moreover, the solution is guaranteed to be robust in the sense that uniform frequency of the patrol is achieved as long as at least one robot works properly.

Multi-robot area patrol under frequency constraints

Patrolling involves generating patrol paths for mobile robots such that every point on the paths is repeatedly covered. This paper focuses on patrolling in closed areas, where every point in the area is to be visited repeatedly by one or more robots. Previous work has often examined paths that allow for repeated coverage, but ignored the frequency in which points in the area are visited. In contrast, we first present formal frequency-based optimization criteria used for evaluation of patrol algorithms. Then, we present a patrol algorithm that guarantees maximal uniform frequency, i.e., each point in the target area is covered at the same optimal frequency. This solution is based on finding a circular path that visits all points in the area, while taking into account terrain directionality and velocity constraints. Robots are positioned uniformly along this path in minimal time, using a second algorithm. Moreover, the solution is guaranteed to be robust in the sense that uniform frequency of the patrol is achieved as long as at least one robot works properly. We then present a set of algorithms for handling events along the patrol path. The algorithms differ in the way they handle the event, as a function of the time constraints for handling them. However, all the algorithms handle events while maintaining the patrol path, and minimizing the disturbance to the system.

Redundancy, Efficiency and Robustness in Multi-Robot Coverage

Area coverage is an important task for mobile robots, with many real-world applications. Motivated by potential efficiency and robustness improvements, there is growing interest in the use of multiple robots in coverage. Previous investigations of multi-robot coverage focuses on completeness and eliminating redundancy, but does not formally address robustness, nor examine the impact of the initial positions of robots on the coverage time. Indeed, a common assumption is that non-redundancy leads to improved coverage time. We address robustness and efficiency in a family of multi-robot coverage algorithms, based on spanning-tree coverage of approximate cell decomposition. We analytically show that the algorithms are robust, in that as long as a single robot is able to move, the coverage will be completed. We also show that non-redundant (non-back tracking) versions of the algorithms have a worst-case coverage time virtually identical to that of a single robot—thus no performance gain is guaranteed in non-redundant coverage. Moreover, this worst-case is in fact common in real-world applications. Surprisingly, however, redundant coverage algorithms lead to guaranteed performance which halves the coverage time even in the worst case.

On redundancy, efficiency, and robustness in coverage for multiple robots

Motivated by potential efficiency and robustness gains, there is growing interest in the use of multiple robots for coverage. In coverage, robots visit every point in a target area, at least once. Previous investigations of multi-robot coverage focus on completeness of the coverage, and on eliminating redundancy, but do not formally address robustness. Moreover, a common assumption is that elimination of redundancy leads to improved efficiency (coverage time). We address robustness and efficiency in a novel family of multi-robot coverage algorithms, based on spanning-tree coverage of approximate cell decomposition of the work-area. We analytically show that the algorithms are robust, in that as long as a single robot is able to move, the coverage will be completed. We also show that non-redundant (non-backtracking) versions of the algorithms have a worst-case coverage time virtually identical to that of a single robot-thus no performance gain is guaranteed in non-redundant coverage. Surprisingly, however, redundant coverage algorithms lead to guaranteed performance which halves the coverage time even in the worst case. We present a polynomial-time redundant coverage algorithm, whose coverage time is optimal, and which is able to address robots heterogeneous in speed and fuel. We compare the performance of all algorithms empirically and show that the use of the optimal algorithm leads to significant improvements in coverage time.

Constructing spanning trees for efficient multi-robot coverage

This paper discusses the problem of building efficient coverage paths for a team of robots. An efficient multirobot coverage algorithm should result in a coverage path for every robot, such that the union of all paths generates a full coverage of the terrain and the total coverage time is minimized. A method, underlying several coverage algorithms, suggests the use of spanning trees as base for creating coverage paths. Current studies assume that the spanning tree is given, and try to make the most out of the given configuration. However, overall performance of the coverage is heavily dependent on the given spanning tree. This paper tackles the open challenge of constructing a coverage spanning tree that minimizes the time to complete coverage. We argue that the choice of the initial spanning tree has far reaching consequences concerning the coverage time, and if the tree is constructed appropriately, it could considerably reduce the coverage time of the terrain. Therefore the problem studied here is finding spanning trees that would decrease the coverage time of the terrain when used as base for multi-robot coverage algorithms. The main contributions of this paper are twofold. First, it provides initial sound discussion and results concerning the construction of the tree as a crucial base for any efficient coverage algorithm. Second, it describes a polynomial-time tree construction algorithm that, as shown in extensive simulations, dramatically improves the coverage time even when used as a basis for a simple, inefficient, coverage algorithm

Towards robust on-line multi-robot coverage

Area coverage is an important task for mobile robots, with many real-world applications. In many cases, the coverage has to be completed without the use of a map or any a priori knowledge about the area, a process referred-to as on-line coverage. Previous investigations of multi-robot on-line coverage focused on the improved efficiency gained from the use of multiple robots, but did not formally addressed the potential for greater robustness. We present a novel multi-robot on-line coverage algorithm, based on approximate cell decomposition. We analytically show that the algorithm is complete and robust, in that as long as a single robot is able to move, the coverage would be completed. We analyze the assumptions underlying the algorithm requirements and present a number of techniques for executing it in real robots. We show empirical coverage-time results of running the algorithm in two different environments and several group sizes

Learning the Sequential Coordinated Behavior of Teams from Observations

The area of agent modeling deals with the task of observing other agents and modeling their behavior, in order to predict their future behavior, coordinate with them, assist them, or counter their actions. Typically, agent modeling techniques assume the availability of a plan- or behavior-library, which encodes the full repertoire of expected observed behavior. However, recent applications areas of agent modeling raise challenges to the assumption of such a library, as agent modeling systems are increasingly used in open and/or adversarial settings, where the behavioral repertoire of the observed agents is unknown at design time. This paper focuses on the challenge of the unsupervised autonomous learning of the sequential behaviors of agents, from observations of their behavior. The techniques we present translate observations of the dynamic, complex, continuous multi-variate world state into a time-series of recognized atomic behaviors. This time-series is then analyzed to find repeating subsequences characterizing each team. We compare two alternative approaches to extracting such characteristic sequences, based on frequency counts and statistical dependencies. Our results indicate that both techniques are able to extract meaningful sequences, and do significantly better than random predictions. However, the statistical dependency approach is able to correctly reject sequences that are frequent, but are due to random co-occurrence of behaviors, rather than to a true sequential dependency between them.

Adaptive agent integration architectures for heterogeneous team members

With the proliferation of software agents and smart hardware devices there is a growing realization that large-scale problems can be addressed by integration of such standalone systems. This has led to an increasing interest in integration architectures that enable a heterogeneous variety of agents and humans to work together. These agents and humans differ in their capabilities, preferences, the level of autonomy they are willing to grant the integration architecture and their information requirements and performance. The challenge in coordinating such a diverse agent set is that potentially a large number of domain-specific and agent specific coordination plans may be required. We present a novel two-tiered approach to address this coordination problem. We first provide the integration architecture with general purpose teamwork coordination capabilities, but then enable adaptation of such capabilities for the needs or requirements of specific individuals. A key novel aspect of this adaptation is that it takes place in the context of other heterogeneous team members. We are realizing this approach in an implemented distributed agent integration architecture called Teamcore. Experimental results from two different domains are presented.