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Efficient Informative Sensing using Multiple Robots
"... The need for efficient monitoring of spatiotemporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, these robots have bounded resources, such as l ..."
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Cited by 53 (5 self)
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The need for efficient monitoring of spatiotemporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, these robots have bounded resources, such as limited battery or limited amounts of time to obtain measurements. Thus, careful coordination of their paths is required in order to maximize the amount of information collected, while respecting the resource constraints. In this paper, we present an efficient approach for nearoptimally solving the NPhard optimization problem of planning such informative paths. In particular, we first develop eSIP (efficient Singlerobot Informative Path planning), an approximation algorithm for optimizing the path of a single robot. Hereby, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to quantify the amount of information collected. We prove that the mutual information collected using paths obtained by using eSIP is close to the information obtained by an optimal solution. We then provide a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multirobot problem. This procedure approximately generalizes any guarantees for the singlerobot problem to the multirobot case. We extensively evaluate the effectiveness of our approach on several experiments performed infield for two important environmental sensing applications, lake and river monitoring, and simulation experiments performed using several real world sensor network data sets. 1.
Sweep Coverage with Mobile Sensors
"... Many efforts have been made for addressing coverage problems in sensor networks. They fall into two categories, full coverage and barrier coverage, featured as static coverage. In this work, we study a new coverage scenario, sweep coverage, which differs with the previous static coverage. In sweep c ..."
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Cited by 37 (1 self)
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Many efforts have been made for addressing coverage problems in sensor networks. They fall into two categories, full coverage and barrier coverage, featured as static coverage. In this work, we study a new coverage scenario, sweep coverage, which differs with the previous static coverage. In sweep coverage, we only need to monitor certain points of interest (POIs) periodically so the coverage at each POI is timevariant, and thus we are able to utilize a small number of mobile sensors to achieve sweep coverage among a much larger number of POIs. We investigate the definitions and model for sweep coverage. Given a set of POIs and their sweep period requirements, we prove that determining the minimum number of required sensors (minsensor sweepcoverage problem) is NPhard, and it cannot be approximated within a factor of 2. We propose a centralized algorithm with constant approximation ratio 2 + ɛ for the simplified problem where all sweep periods are identical. We further characterize the nonlocality of the problem and design a distributed sweep algorithm, DSWEEP, cooperating sensors to provide required sweep requirements with the best effort. We conduct extensive simulations to study the performance of the proposed algorithms. Our simulations show that DSWEEP outperforms the randomized scheme in both effectiveness and efficiency. 1
The Increasing Cost Tree Search for Optimal MultiAgent Pathfinding
 PROCEEDINGS OF THE TWENTYSECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
"... We address the problem of optimal path finding for multiple agents where agents must not collide and their total travel cost should be minimized. Previous work used traditional singleagent search variants of the A* algorithm. We present a novel formalization for this problem which includes a search ..."
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Cited by 14 (6 self)
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We address the problem of optimal path finding for multiple agents where agents must not collide and their total travel cost should be minimized. Previous work used traditional singleagent search variants of the A* algorithm. We present a novel formalization for this problem which includes a search tree called the increasing cost tree (ICT) and a corresponding search algorithm that finds optimal solutions. We analyze this new formalization and compare it to the previous stateoftheart A*based approach. Experimental results on various domains show the benefits and drawbacks of this approach. A speedup of up to 3 orders of magnitude was obtained in a number of cases.
Complete Algorithms for Cooperative Pathfinding Problems
 PROCEEDINGS OF THE TWENTYSECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
"... Problems that require multiple agents to follow noninterfering paths from their current states to their respective goal states are called cooperative pathfinding problems. We present the first complete algorithm for finding these paths that is sufficiently fast for realtime applications. Furthermo ..."
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Cited by 14 (0 self)
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Problems that require multiple agents to follow noninterfering paths from their current states to their respective goal states are called cooperative pathfinding problems. We present the first complete algorithm for finding these paths that is sufficiently fast for realtime applications. Furthermore, our algorithm offers a tradeoff between running time and solution quality. We then refine our algorithm into an anytime algorithm that first quickly finds a solution, and then uses any remaining time to incrementally improve that solution until it is optimal or the algorithm is terminated. We compare our algorithms to those in the literature and show that in addition to completeness, our algorithms offer improved solution quality as well as competitive running time.
Decentralized prioritized planning in large multirobot teams
"... Abstract — In this paper, we address the problem of distributed motion planning for large teams of hundreds of robots in constrained environments. We introduce two distributed prioritized planning algorithms: an efficient, complete method which is shown to converge to the centralized prioritized pla ..."
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Abstract — In this paper, we address the problem of distributed motion planning for large teams of hundreds of robots in constrained environments. We introduce two distributed prioritized planning algorithms: an efficient, complete method which is shown to converge to the centralized prioritized planner solution, and a sparse method in which robots discover collisions probabilistically. Planning is divided into a number of iterations, during which every robot simultaneously and independently computes a planning solution based on other robots ’ path information from the previous iteration. Paths are exchanged in ways that exploit the cooperative nature of the team and a statistical phenomenon known as the “birthday paradox”. Performance is measured in simulated 2D environments with teams of up to 240 robots. We find that in moderately constrained environments, these methods generate solutions of similar quality to a centralized prioritized planner, but display interesting communication and planning time characteristics. I.
Planning Optimal Paths for Multiple Robots on Graphs
"... Abstract — In this paper, we study the problem of optimal multirobot path planning (MPP) on graphs. We propose two multiflow based integer linear programming (ILP) models that computes minimum last arrival time and minimum total distance solutions for our MPP formulation, respectively. The resultin ..."
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Cited by 5 (3 self)
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Abstract — In this paper, we study the problem of optimal multirobot path planning (MPP) on graphs. We propose two multiflow based integer linear programming (ILP) models that computes minimum last arrival time and minimum total distance solutions for our MPP formulation, respectively. The resulting algorithms from these ILP models are complete and guaranteed to yield true optimal solutions. In addition, our flexible framework can easily accommodate other variants of the MPP problem. Focusing on the time optimal algorithm, we evaluate its performance, both as a stand alone algorithm and as a generic heuristic for quickly solving large problem instances. Computational results confirm the effectiveness of our method. I.
A Polynomialtime Algorithm for Nonoptimal MultiAgent Pathfinding
"... Multiagent pathfinding, where multiple agents must travel to their goal locations without getting stuck, has been studied in both theoretical and practical contexts, with a variety of both optimal and suboptimal algorithms proposed for solving problems. Recent work has shown that there is a linear ..."
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Cited by 4 (1 self)
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Multiagent pathfinding, where multiple agents must travel to their goal locations without getting stuck, has been studied in both theoretical and practical contexts, with a variety of both optimal and suboptimal algorithms proposed for solving problems. Recent work has shown that there is a lineartime check for whether a multiagent pathfinding problem can be solved in a tree, however this was not used to actually produce solutions. In this paper we provide a constructive proof of how to solve multiagent pathfinding problems in a tree that culminates in a novel approach that we call the treebased agent swapping strategy (TASS). Experimental results showed that TASS can find solutions to the multiagent pathfinding problem on a highly crowded tree with 1000 nodes and 996 agents in less than 8 seconds. These results are far more efficient and general than existing work, suggesting that TASS is a productive line of study for multiagent pathfinding.
MAPP: a Scalable MultiAgent Path Planning Algorithm with Tractability and Completeness Guarantees
"... Multiagent path planning is a challenging problem with numerous reallife applications. Running a centralized search such as A * in the combined state space of all units is complete and costoptimal, but scales poorly, as the state space size is exponential in the number of mobile units. Traditiona ..."
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Cited by 4 (1 self)
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Multiagent path planning is a challenging problem with numerous reallife applications. Running a centralized search such as A * in the combined state space of all units is complete and costoptimal, but scales poorly, as the state space size is exponential in the number of mobile units. Traditional decentralized approaches, such as FAR and WHCA*, are faster and more scalable, being based on problem decomposition. However, such methods are incomplete and provide no guarantees with respect to the running time or the solution quality. They are not necessarily able to tell in a reasonable time whether they would succeed in finding a solution to a given instance. We introduce MAPP, a tractable algorithm for multiagent path planning on undirected graphs. We present a basic version and several extensions. They have lowpolynomial worstcase upper bounds for the running time, the memory requirements, and the length of solutions. Even though all algorithmic versions are incomplete in the general case, each provides formal guarantees on problems it can solve. For each version, we discuss the algorithm’s completeness with respect to clearly defined subclasses of instances. Experiments were run on realistic game grid maps. MAPP solved 99.86 % of all mobile units, which is 18–22 % better than the percentage of FAR and WHCA*. MAPP marked 98.82 % of all units as provably solvable during the first stage of plan computation. Parts of MAPP’s computation can be reused across instances on the same map. Speedwise, MAPP is competitive or significantly faster than WHCA*, depending on whether MAPP performs all computations from scratch. When data that MAPP can reuse are preprocessed offline and readily available, MAPP is slower than the very fast FAR algorithm by a factor of 2.18 on average. MAPP’s solutions are on average 20% longer than FAR’s solutions and 7–31 % longer than WHCA*’s solutions. 1.
Bridging the Gap Between Centralised and Decentralised MultiAgent Pathfinding
 FOURTEENTH AAAI / SIGART DOCTORAL CONSORTIUM (2009)
, 2009
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Abstract Path Planning for Multiple Robots: A Theoretical Study
, 2010
"... An abstraction of the problem of multirobot path planning is introduced in this paper. The basic task is to determine spatialtemporal plan for each robot of a group of robots where each robot is given its initial position in the environment and it needs to go to the given goal position. Robots mu ..."
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An abstraction of the problem of multirobot path planning is introduced in this paper. The basic task is to determine spatialtemporal plan for each robot of a group of robots where each robot is given its initial position in the environment and it needs to go to the given goal position. Robots must avoid obstacles and must not collide with each other. The abstraction adopted in this work models the environment within that robots are moving as an undirected graph. Robots are placed in vertices of the graph; at most one robot is placed in each vertex and at most one vertex remains unoccupied.The move is allowed into the unoccupied vertex or into the vertex being vacated by an allowed move supposed that no other robot is entering the same target vertex. The relation of multirobot path planning to the problem of pebble motion on a graph (which the most widely known representative is 15puzzle) is discussed. The optimization variant of the abstract multirobot path planning is particularly studied. The task is to find a solution of the makespan as small as possible in the optimization variant. The main contribution of the paper is the proof of the NPcompleteness of the decision version of the optimization variant of multirobot path planning. The reduction of Boolean satisfiability to multirobot path planning is used in the proof.