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Efficient Boustrophedon Multi-Robot Coverage: an algorithmic approach
- ANN MATH ARTIF INTELL
, 2009
"... This paper presents algorithmic solutions for the complete coverage path planning problem using a team of mobile robots. Multiple robots decrease the time to complete the coverage, but maximal efficiency is only achieved if the number of regions covered multiple times is minimized. A set of multi-ro ..."
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Cited by 14 (0 self)
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This paper presents algorithmic solutions for the complete coverage path planning problem using a team of mobile robots. Multiple robots decrease the time to complete the coverage, but maximal efficiency is only achieved if the number of regions covered multiple times is minimized. A set of multi-robot coverage algorithms is presented that minimize repeat coverage. The algorithms use the same planar cellbased decomposition as the Boustrophedon single robot coverage algorithm, but provide extensions to handle how robots cover a single cell, and how robots are allocated among cells. Specifically, for the coverage task our choice of multi-robot policy strongly depends on the type of communication that exists between the robots. When the robots operate under the line-of-sight communication restriction, keeping them as a team helps to minimize repeat coverage. When communication between the robots is available without any restrictions, the robots are initially distributed through space, and each one is allocated a virtually-bounded area to cover. A greedy auction mechanism is used for task/cell allocation among the robots. Experimental results from different simulated and real environments that illustrate our approach for different communication conditions are presented.
P-SLAM: Simultaneous Localization and Mapping With Environmental-Structure Prediction
"... Abstract—Traditionally, simultaneous localization and mapping (SLAM) algorithms solve the localization and mapping problem in explored regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental-structure predictor to predict the structure inside an une ..."
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Cited by 6 (0 self)
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Abstract—Traditionally, simultaneous localization and mapping (SLAM) algorithms solve the localization and mapping problem in explored regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental-structure predictor to predict the structure inside an unexplored region (i.e., look-ahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar environment/structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can use the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save the exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be used to speed up the SLAM process and build a more accurate map. We have also derived the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM on a Pioneer 3-DX mobile robot using a Rao–Blackwellized particle filter in real time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in indoor environments. Index Terms—Bayes procedures, environmental-structure prediction, simultaneous localization and mapping (SLAM). I.
Market-based multirobot coordination: A comprehensive survey and analysis
, 2006
"... As robotic technology improves, we charge robots with increasingly varied and difficult tasks. Many of these tasks can potentially be completed better by a team of robots working together than by individual robots working alone. Coordination can lead to faster task completion, increased robustness, ..."
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Cited by 5 (0 self)
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As robotic technology improves, we charge robots with increasingly varied and difficult tasks. Many of these tasks can potentially be completed better by a team of robots working together than by individual robots working alone. Coordination can lead to faster task completion, increased robustness, higher-quality solutions, and the completion of tasks impossible for single robots. Nevertheless, effective coordination can be difficult to achieve because of a range of adverse real-world conditions including dynamic events, changing task demands, resource failures, and limited deliberation time. The desire to overcome these challenges and harness the benefits of robot teams has made multirobot coordination a vital field in robotics research. Of the resulting wealth of research, market-based multirobot coordination approaches in particular have received significant attention and are growing in popularity within the community. These approaches harness the principles of market economies—which
Analysis of Cluster Formation Techniques for Multi-Robot Task Allocation using Sequential Single-cluster Auctions
, 2012
"... Recent research has shown the benefits of using K-means clustering in task allocation to robots. However, there is little evaluation of other clustering techniques. In this paper we compare K-means clustering to single-linkage clustering and consider the effects of straight line and true path distan ..."
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Cited by 2 (2 self)
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Recent research has shown the benefits of using K-means clustering in task allocation to robots. However, there is little evaluation of other clustering techniques. In this paper we compare K-means clustering to single-linkage clustering and consider the effects of straight line and true path distance metrics in cluster formation. Our empirical results show single-linkage clustering with a true path distance metric provides the best solutions to the multi-robot task allocation problem when used in sequential single-cluster auctions.
ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning
"... In this paper we present a novel approach for multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, we call ACS-PRM, consists of three steps, which are C-space sampling, roadmap building and motion planning. Firstly, an adeq ..."
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In this paper we present a novel approach for multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, we call ACS-PRM, consists of three steps, which are C-space sampling, roadmap building and motion planning. Firstly, an adequate number of points should be generated in C-space on an occupancy grid map by using an adaptive cross sampling method. Secondly, a roadmap should be built while the potential targets and the milestones are extracted by second learning the result of sampling. Finally, the motion of robots should be planned by querying the constructed roadmap. In contrast to previous approaches, our ACS-PRM approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the planning efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be significantly reduced by our ACS-PRM approach compared with previous approaches.
using multi agent system
"... Optimization of route planning and exploration using multi agent system ..."
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Comparative Study of Algorithms for Frontier based Area Exploration and Slam for Mobile Robots
"... Exploration strategies are used to guide mobile robots for map building. Usually, exploration strategies work greedily by evaluating a number of candidate observations on the basis of a utility function and selecting the best one. The core challenge in area exploration is to deploy a large number of ..."
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Exploration strategies are used to guide mobile robots for map building. Usually, exploration strategies work greedily by evaluating a number of candidate observations on the basis of a utility function and selecting the best one. The core challenge in area exploration is to deploy a large number of robots in an unknown environment, map the environment and establishing an efficient communication between the robots. Simultaneous Localization and Mapping (SLAM) comes in to add more accuracy and heuristics to the generic area exploration strategies. Addition to SLAM algorithms will improve the performance of the exploration process and map building to a great extend. In this paper a survey of existing approaches in frontier based area exploration and various SLAM algorithms which can be useful for the process of area exploration are discussed.
A Hybrid Evolutionary Approach for Multi Robot Path Exploration Problem
"... planning problem is one of the famous problems in robot’s offline decision making algorithms. In this paper, a hybrid approach is presented that combines clustering and Genetic Algorithm (GA) to solve the Multi Robot Path Exploration Problem. The aim is to find collision free path, which Robot can f ..."
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planning problem is one of the famous problems in robot’s offline decision making algorithms. In this paper, a hybrid approach is presented that combines clustering and Genetic Algorithm (GA) to solve the Multi Robot Path Exploration Problem. The aim is to find collision free path, which Robot can follow to reach the target from its starting position. Environment is considered as a complete weighted graph representing the locations or points in the world environment and Traveling Salesman Problem (TSP) solving approach, based on GA is tried to solve this problem. Clustering is used to group the points (land marks) in the environment and rendezvous point is selected where all the robots finally meet. Experimental results are presented to illustrate the performance of the proposed scheme.
Coverage Using Teams of Homogeneous Robots submitted by Sriram Raghavan to the
"... This is to certify that the thesis entitled Distributed Algorithms for Hierarchical Area ..."
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This is to certify that the thesis entitled Distributed Algorithms for Hierarchical Area
Homogeneous Hierarchical Composition of Areas in Multi-robot Area Coverage
"... Abstract. Multi-robot area coverage poses several research challenges. The challenge of coordinating multiple robots ’ actions coupled with the challenge of minimizing the overlap in coverage across robots becomes even more complex and critical when large teams and large areas are involved. In fact, ..."
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Abstract. Multi-robot area coverage poses several research challenges. The challenge of coordinating multiple robots ’ actions coupled with the challenge of minimizing the overlap in coverage across robots becomes even more complex and critical when large teams and large areas are involved. In fact, the efficiency critically hinges on the coordination algorithms used and the robot capabilities. Multi-robot coverage of such large areas can be tackled by the divide-andconquer policy; decomposing the coverage area into several small coverage grids. It is fairly simple to devise algorithms to minimize the overlap in small grids by making simple assumptions. If the overlap ratio of these small grids can be controlled, one may be able to integrate them appropriately to cover the large grid. In this paper, we introduce homogeneous hierarchical composition grids to decompose a coverage area into several small coverage primitives with appropriately sized robot teams. These coverage grids are viewed as cells at a Meta level and composed hierarchically with such teams functioning as a single unit. We state and prove an associated theorem that provides very good scaling properties to large grids. We have performed simulated studies to validate the claims and study performance. 1
