Results 1 - 10
of
54
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 ..."
Abstract
-
Cited by 88 (22 self)
- Add to MetaCart
(Show Context)
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.
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 ..."
Abstract
-
Cited by 64 (15 self)
- Add to MetaCart
(Show Context)
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.
Decentralised Coordination of Mobile Sensors Using the Max-Sum Algorithm
, 2009
"... In this paper, we introduce an on-line, decentralised coordination algorithm for monitoring and predicting the state of spatial phenomena by a team of mobile sensors. These sensors have their application domain in disaster response, where strict time constraints prohibit path planning in advance. Th ..."
Abstract
-
Cited by 39 (10 self)
- Add to MetaCart
In this paper, we introduce an on-line, decentralised coordination algorithm for monitoring and predicting the state of spatial phenomena by a team of mobile sensors. These sensors have their application domain in disaster response, where strict time constraints prohibit path planning in advance. The algorithm enables sensors to coordinate their movements with their direct neighbours to maximise the collective information gain, while predicting measurements at unobserved locations using a Gaussian process. It builds upon the max-sum message passing algorithm for decentralised coordination, for which we present two new generic pruning techniques that result in speed-up of up to 92 % for 5 sensors. We empirically evaluate our algorithm against several on-line adaptive coordination mechanisms, and report a reduction in root mean squared error up to 50 % compared to a greedy strategy.
The Impact of Adversarial Knowledge on Adversarial Planning in Perimeter Patrol
, 2008
"... This paper considers the problem of multi-robot patrolling around a closed area, in the presence of an adversary trying to penetrate the area. Previous work on planning in similar adversarial environments addressed worst-case settings, in which the adversary has full knowledge of the defending robot ..."
Abstract
-
Cited by 34 (14 self)
- Add to MetaCart
This paper considers the problem of multi-robot patrolling around a closed area, in the presence of an adversary trying to penetrate the area. Previous work on planning in similar adversarial environments addressed worst-case settings, in which the adversary has full knowledge of the defending robots. It was shown that non deterministic algorithms may be effectively used to maximize the chances of blocking such a full-knowledge opponent, and such algorithms guarantee a “lower bound” to the performance of the team. However, an open question remains as to the impact of the knowledge of the opponent on the performance of the robots. This paper explores this question in depth and provides theoretical results, supported by extensive experiments with 68 human subjects concerning the compatibility of algorithms to the extent of information possessed
Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments
, 2011
"... We present controllers that enable mobile robots to persistently monitor or sweep a changing environment. The changing environment is modeled as a field which grows in locations that are not within range of a robot, and decreases in locations that are within range of a robot. We assume that the rob ..."
Abstract
-
Cited by 34 (10 self)
- Add to MetaCart
We present controllers that enable mobile robots to persistently monitor or sweep a changing environment. The changing environment is modeled as a field which grows in locations that are not within range of a robot, and decreases in locations that are within range of a robot. We assume that the robots travel on given closed paths. The speed of each robot along its path is controlled to prevent the field from growing unbounded at any location. We consider the space of speed controllers that can be parametrized by a finite set of basis functions. For a single robot, we develop a linear program that is guaranteed to compute a speed controller in this space to keep the field bounded, if such a controller exists. Another linear program is then derived whose solution is the speed controller that minimizes the maximum field value over the environment. We extend our linear program formulation to develop a multi-robot controller that keeps the field bounded. The multi-robot controller has the unique feature that it does not require communication among the robots. Simulation studies demonstrate the robustness of the controllers to modeling errors, and to stochasticity in the environment.
Color learning on a mobile robot: Towards full autonomy under changing illumination
- In The International Joint Conference on Artificial Intelligence (IJCAI
, 2007
"... A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. It is commonly asserted that in order to do so, the agent must be able to learn to deal with unexpected environmental conditions. However an ability to learn is n ..."
Abstract
-
Cited by 23 (13 self)
- Add to MetaCart
(Show Context)
A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. It is commonly asserted that in order to do so, the agent must be able to learn to deal with unexpected environmental conditions. However an ability to learn is not sufficient. For true extended autonomy, an agent must also be able to recognize when to abandon its current model in favor of learning a new one; and how to learn in its current situation. This paper presents a fully implemented example of such autonomy in the context of color map learning on a vision-based mobile robot for the purpose of image segmentation. Past research established the ability of a robot to learn a color map in a single fixed lighting condition when manually given a “curriculum, ” an action sequence designed to facilitate learning. This paper introduces algorithms that enable a robot to i) devise its own curriculum; and ii) recognize when the lighting conditions have changed sufficiently to warrant learning a new color map. 1
Adversarial Uncertainty in Multi-Robot Patrol
"... We study the problem of multi-robot perimeter patrol in adversarial environments, under uncertainty of adversarial behavior. The robots patrol around a closed area using a nondeterministic patrol algorithm. The adversary’s choice of penetration point depends on the knowledge it obtained on the patro ..."
Abstract
-
Cited by 19 (1 self)
- Add to MetaCart
We study the problem of multi-robot perimeter patrol in adversarial environments, under uncertainty of adversarial behavior. The robots patrol around a closed area using a nondeterministic patrol algorithm. The adversary’s choice of penetration point depends on the knowledge it obtained on the patrolling algorithm and its weakness points. Previous work investigated full knowledge and zero knowledge adversaries, and the impact of their knowledge on the optimal algorithm for the robots. However, realistically the knowledge obtained by the adversary is neither zero nor full, and therefore it will have uncertainty in its choice of penetration points. This paper considers these cases, and offers several approaches to bounding the level of uncertainty of the adversary, and its influence on the optimal patrol algorithm. We provide theoretical results that justify these approaches, and empirical results that show the performance of the derived algorithms used by simulated robots working against humans playing the role of the adversary is several different settings. 1
Multi-robot adversarial patrolling: Facing a fullknowledge opponent
- Journal of Artificial Intelligence Research
"... Abstract The problem of adversarial multi-robot patrol has gained interest in recent years, mainly due to its immediate relevance to various security applications. In this problem, robots are required to repeatedly visit a target area in a way that maximizes their chances of detecting an adversary ..."
Abstract
-
Cited by 11 (2 self)
- Add to MetaCart
(Show Context)
Abstract The problem of adversarial multi-robot patrol has gained interest in recent years, mainly due to its immediate relevance to various security applications. In this problem, robots are required to repeatedly visit a target area in a way that maximizes their chances of detecting an adversary trying to penetrate through the patrol path. When facing a strong adversary that knows the patrol strategy of the robots, if the robots use a deterministic patrol algorithm, then in many cases it is easy for the adversary to penetrate undetected (in fact, in some of those cases the adversary can guarantee penetration). Therefore this paper presents a non-deterministic patrol framework for the robots. Assuming that the strong adversary will take advantage of its knowledge and try to penetrate through the patrol's weakest spot, hence an optimal algorithm is one that maximizes the chances of detection in that point. We therefore present a polynomial-time algorithm for determining an optimal patrol under the Markovian strategy assumption for the robots, such that the probability of detecting the adversary in the patrol's weakest spot is maximized. We build upon this framework and describe an optimal patrol strategy for several robotic models based on their movement abilities (directed or undirected) and sensing abilities (perfect or imperfect), and in different environment models -either patrol around a perimeter (closed polygon) or an open fence (open polyline).
Complete and Robust Cooperative Robot Area Coverage with Limited Range
"... Abstract — We address the problem of multi-robot area coverage and present a new approach in the case where the map of the area and its static obstacles are known and the robots have a limited visibility range. The proposed method starts by locating a set of static guards on the map of the target ar ..."
Abstract
-
Cited by 10 (4 self)
- Add to MetaCart
(Show Context)
Abstract — We address the problem of multi-robot area coverage and present a new approach in the case where the map of the area and its static obstacles are known and the robots have a limited visibility range. The proposed method starts by locating a set of static guards on the map of the target area and then builds a graph called Reduced-CDT, a new environment representation method based on the Constrained Delaunay Triangulation (CDT). Multi-Prim’s is used to decompose the graph into a forest of partial spanning trees (PSTs). Each PST is then modified through a mechanism called Constrained Spanning Tour (CST) to build a cycle which is then assigned to an individual robot. Subsequently, robots start navigating the cycles and consequently cover the whole area. We show that the proposed approach is complete and robust with respect to robot failure. I.
Coevolution of Heterogeneous MultiRobot Teams
- Proceedings of the Genetic and Evolutionary Computation Conference
, 2010
"... Evolving multiple robots so that each robot acting independently can contribute to the maximization of a system level objective presents significant scientific challenges. For example, evolving multiple robots to maximize aggregate information in exploration domains (e.g., planetary exploration, sea ..."
Abstract
-
Cited by 10 (9 self)
- Add to MetaCart
Evolving multiple robots so that each robot acting independently can contribute to the maximization of a system level objective presents significant scientific challenges. For example, evolving multiple robots to maximize aggregate information in exploration domains (e.g., planetary exploration, search and rescue) requires coordination, which in turn requires the careful design of the evaluation functions. Additionally, where communication among robots is expensive (e.g., limited power or computation), the coordination must be achieved passively, without robots explicitly informing others of their states/intended actions. Coevolving robots in these situations is a potential solution to producing coordinated behavior, where the robots are coupled through their evaluation functions. In this work, we investigate coevolution in three types of domains: (i) where precisely n homogeneous robots need to perform a task; (ii) where n is the optimal number of homogeneous robots for the task; and (iii) where n is the optimal number of heterogeneous robots for the task. Our results show that coevolving robots with evaluation functions that are locally aligned with the system evaluation significantly improve performance over robots evolving using the system evaluation function directly, particularly in dynamic environments.
