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21
Coordinated Multi-Robot Exploration
- IEEE Transactions on Robotics
, 2005
"... In this paper, we consider the problem of exploring an unknown environment with a team of robots. As in singlerobot exploration the goal is to minimize the overall exploration time. The key problem to be solved in the context of multiple robots is to choose appropriate target points for the individu ..."
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Cited by 158 (10 self)
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In this paper, we consider the problem of exploring an unknown environment with a team of robots. As in singlerobot exploration the goal is to minimize the overall exploration time. The key problem to be solved in the context of multiple robots is to choose appropriate target points for the individual robots so that they simultaneously explore different regions of the environment. We present an approach for the coordination of multiple robots, which simultaneously takes into account the cost of reaching a target point and its utility. Whenever a target point is assigned to a specific robot, the utility of the unexplored area visible from this target position is reduced. In this way, different target locations are assigned to the individual robots. We furthermore describe how our algorithm can be extended to situations in which the communication range of the robots is limited. Our technique has been implemented and tested extensively in real-world experiments and simulation runs. The results demonstrate that our technique effectively distributes the robots over the environment and allows them to quickly accomplish their mission.
MAA*: A heuristic search algorithm for solving decentralized POMDPs
- In Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence
, 2005
"... We present multi-agent A * (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partiallyobservable Markov decision problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate i ..."
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Cited by 91 (21 self)
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We present multi-agent A * (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partiallyobservable Markov decision problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multirobot coordination, network traffic control, or distributed resource allocation. Solving such problems effectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA * has significant advantages. We introduce an anytime variant of MAA * and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems. 1
Evolving Controllers For A Homogeneous System Of Physical Robots: Structured Cooperation With Minimal Sensors
, 2003
"... this paper we report on our recent work evolving controllers for robots which are required to work as a team. The word team ' has been used in a variety of senses in both the multi-robot and the ethology literature, so it is appropriate to start the paper with a denition. We will adopt the deni ..."
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Cited by 68 (0 self)
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this paper we report on our recent work evolving controllers for robots which are required to work as a team. The word team ' has been used in a variety of senses in both the multi-robot and the ethology literature, so it is appropriate to start the paper with a denition. We will adopt the denition given by Anderson & Franks (2001) in their recent review of team behaviour in animal societies. They identify three dening features of team behaviour. First, individuals make dierent contributions to task success, i.e. they must perform dierent sub-tasks or roles (this does not preclude more than one individual adopting the same role; there may be more individuals than roles). Second, individual roles or sub-tasks are interdependent (or interlocking'), requiring structured cooperation; individuals operate concurrently, coordinating their dierent contributions in order to complete the task. Finally, a team's organizational structure persists over time, although its individuals may be substituted or swap roles (Anderson & Franks 2001)
Multi-Robot Dynamic Role Assignment and Coordination Through Shared Potential Fields
, 2003
"... Role assignment and coordination are difficult issues for multi-robot systems, especially in highly dynamic tasks. Robot soccer is one such task and it provides a unique challenge for multi-robot research. In this paper, we contribute the approach that we successfully developed for CMPACK'02 , ..."
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Cited by 40 (2 self)
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Role assignment and coordination are difficult issues for multi-robot systems, especially in highly dynamic tasks. Robot soccer is one such task and it provides a unique challenge for multi-robot research. In this paper, we contribute the approach that we successfully developed for CMPACK'02 , our team for the RoboCup-2002 Sony legged league. The RoboCup-2002 Sony robots were equipped with wireless communication for the first time this year. We developed an approach for sharing sensed information and effective coordination through the introduction of shared potential fields. The potential fields were based on the positions of the other robots on the team and the ball. The robots positioned themselves on the field by following the gradient to a minimum of the potential field. In principle, our potential functions can be applied to any multi-robot domain. We present the results of the RoboCup-2002 competition, which we won, and we show a post-competition, controlled empirical evaluation to analyze the impact of our algorithm. The results demonstrate that our team using our communication-dependent coordination outperforms a team of individually skilled robots without coordination.
Coordinated Multi-Robot Exploration using a Segmentation of the Environment
"... Abstract — This paper addresses the problem of exploring an unknown environment with a team of mobile robots. The key issue in coordinated multi-robot exploration is how to assign target locations to the individual robots such that the overall mission time is minimized. In this paper, we propose a n ..."
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Cited by 29 (2 self)
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Abstract — This paper addresses the problem of exploring an unknown environment with a team of mobile robots. The key issue in coordinated multi-robot exploration is how to assign target locations to the individual robots such that the overall mission time is minimized. In this paper, we propose a novel approach to distribute the robots over the environment that takes into account the structure of the environment. To achieve this, it partitions the space into segments, for example, corresponding to individual rooms. Instead of only considering frontiers between unknown and explored areas as target locations, we send the robots to the individual segments with the task to explore the corresponding area. Our approach has been implemented and tested in simulation as well as in real world experiments. The experiments demonstrate that the overall exploration time can be significantly reduced by considering our segmentation method. I.
Collaborative exploration of unknown environments with teams of mobile robots
- In dagstuhl
, 2002
"... Abstract. In this paper we consider the problem of exploring an unknown environment by a team of robots. As in single-robot exploration the goal is to minimize the overall exploration time. The key problem to be solved in the context of multiple robots is to choose appropriate target points for the ..."
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Cited by 27 (4 self)
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Abstract. In this paper we consider the problem of exploring an unknown environment by a team of robots. As in single-robot exploration the goal is to minimize the overall exploration time. The key problem to be solved in the context of multiple robots is to choose appropriate target points for the individual robots so that they simultaneously explore different regions of the environment. We present an approach for the coordination of multiple robots which, in contrast to previous approaches, simultaneously takes into account the cost of reaching a target point and its utility. The utility of a target point is given by the size of the unexplored area that a robot can cover with its sensors upon reaching that location. Whenever a target point is assigned to a specific robot, the utility of the unexplored area visible from this target position is reduced for the other robots. This way, a team of multiple robots assigns different target points to the individual robots. The technique has been implemented and tested extensively in real-world experiments and simulation runs. The results given in this paper demonstrate that our coordination technique significantly reduces the exploration time compared to previous approaches. 1
An optimal best-first search algorithm for solving infinite horizon DEC-POMDPs
- In Proc. of the European Conference on Machine Learning
, 2005
"... Abstract. In the domain of decentralized Markov decision processes, we develop the first complete and optimal algorithm that is able to ex-tract deterministic policy vectors based on finite state controllers for a cooperative team of agents. Our algorithm applies to the discounted in-finite horizon ..."
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Cited by 24 (1 self)
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Abstract. In the domain of decentralized Markov decision processes, we develop the first complete and optimal algorithm that is able to ex-tract deterministic policy vectors based on finite state controllers for a cooperative team of agents. Our algorithm applies to the discounted in-finite horizon case and extends best-first search methods to the domain of decentralized control theory. We prove the optimality of our approach and give some first experimental results for two small test problems. We believe this to be an important step forward in learning and planning in stochastic multi-agent systems. 1
Dynamic Multi-Robot Coordination
- In Multi-Robot Systems: From Swarms to Intelligent Automata, Volume II
, 2003
"... Communication among a group of robots should in principle improve the overall performance of the team of robots, as robots may share their world views and may negotiate task assignments. However, in practice, effectively handling in real-time multi-robot merge of information and coordination is a ch ..."
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Cited by 19 (6 self)
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Communication among a group of robots should in principle improve the overall performance of the team of robots, as robots may share their world views and may negotiate task assignments. However, in practice, effectively handling in real-time multi-robot merge of information and coordination is a challenging task. In this paper, we present the approach that we have successfully developed for a team of communicating soccer robots acting in a highly dynamic environment. Our approach involves creating shared potential functions based on shared positions of relevant obstacles in the world. The biases introduced in the potential functions are general and they could in principle be provided by external sources, such as a human or robot coach. We provide controlled experiments to analyze the impact of our approach in the overall performance of a robot team.
Speeding-up multi-robot exploration by considering semantic place information
- IN IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA
, 2006
"... In this paper, we consider the problem of exploring an unknown environment with a team of mobile robots. One of the key issues in multi-robot exploration is how to assign target locations to the individual robots. To better distribute the robots over the environment and to avoid redundant work, we ..."
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Cited by 12 (6 self)
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In this paper, we consider the problem of exploring an unknown environment with a team of mobile robots. One of the key issues in multi-robot exploration is how to assign target locations to the individual robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). To determine the type of a place, we apply a classifier learned with AdaBoost which additionally considers spatial dependencies between nearby locations. Our approach to incorporate the type of places in the coordination of the robots has been implemented and tested in different environments. The experiments demonstrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the semantic place labels.
Efficient Exploration of Unknown Indoor Environments using a Team of Mobile Robots
"... Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robot ..."
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Cited by 6 (2 self)
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Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned
