Partial-Expansion A* with Selective Node Generation

Felner, Ariel; Goldenberg, Meir; Sharon, Guni; Stern, Roni; Beja, Tal; Sturtevant, Nathan R.; Schaeffer, Jonathan; Holte, Robert C.

doi:10.1609/aaai.v26i1.8137

Cited by 33 publications

(28 citation statements)

References 14 publications

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“…OD reduces the branching factor at the cost of increasing the depth of the solution in the search tree. Another relevant A* variant is Enhanced Partial Expansion A* (EPEA*) (Felner et al 2012). EPEA* uses a priori domain knowledge to sort all successors of a given state according to their f values.…”

Section: Optimal Solversmentioning

confidence: 99%

“…These algorithms usually extend optimal search algorithms (e.g., A* or IDA*) by considering an inflated version of an admissible heuristic. Thus, one can apply these algorithms to any A*-based MAPF solver, such as EPEA* (Felner et al 2012), A* with OD (Standley 2010), and M* (Wagner and Choset 2011). We implemented and experimented with such bounded suboptimal MAPF solvers below.…”

Section: Bounded Suboptimal Solversmentioning

confidence: 99%

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Suboptimal Variants of the Conflict-Based Search Algorithm for the Multi-Agent Pathfinding Problem

Barer

Sharon

Stern

et al. 2021

SOCS

122

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The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents, each with a different start and goal position, such that agents do not collide. A successful optimal MAPF solver is the conflict-based search (CBS) algorithm. CBS is a two level algorithm where special conditions ensure it returns the optimal solution. Solving MAPF optimally is proven to be NP-hard, hence CBS and all other optimal solvers do not scale up. We propose several ways to relax the optimality conditions of CBS trading solution quality for runtime as well as bounded-suboptimal variants, where the returned solution is guaranteed to be within a constant factor from optimal solution cost. Experimental results show the benefits of our new approach; a massive reduction in running time is presented while sacrificing a minor loss in solution quality. Our new algorithms outperform other existing algorithms in most of the cases.

show abstract

Section: Optimal Solversmentioning

confidence: 99%

Section: Bounded Suboptimal Solversmentioning

confidence: 99%

Suboptimal Variants of the Conflict-Based Search Algorithm for the Multi-Agent Pathfinding Problem

Barer

Sharon

Stern

et al. 2021

SOCS

122

View full text Add to dashboard Cite

show abstract

“…We omit comparisons with the many MAPF solvers that have already been shown to perform worse than CBS [10,11] or worse than ECBS [23] or worse than EECBS [36,37]. We also omit comparisons with MAPF solvers that have no completeness and bounded-suboptimality guarantees [38,39,40].…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Previous complete and optimal MAPF solvers seek to explore only the neighbors of a joint state that could potentially yield an optimal solution [10,11,12,13,14]. Conflict Based Search (CBS) [15] is a state-of-the-art optimal MAPF solver that utilizes a lazy algorithm to delay generation of irrelevant neighbors.…”

Section: Introductionmentioning

confidence: 99%

CBS-Budget (CBSB): A Complete and Bounded Suboptimal Search for Multi-Agent Path Finding

Lim¹,

Tsiotras²

2022

Preprint

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Multi-Agent Path Finding (MAPF) is the problem of finding a collection of collision-free paths for a team of multiple agents while minimizing some global cost, such as the sum of the time travelled by all agents, or the time travelled by the last agent. Conflict Based Search (CBS) is a leading complete and optimal MAPF solver which lazily explores the joint agent state space, using an admissible heuristic joint plan. Such an admissible heuristic joint plan is computed by combining individual shortest paths found without considering inter-agent conflicts, and which becomes gradually more informed as constraints are added to individual agents' path planning problems to avoid discovered conflicts. In this paper, we seek to speedup CBS by finding a more informed heuristic joint plan which is bounded from above. We first propose the budgeted Class-Ordered A* (bCOA*), a novel algorithm that finds the shortest path with minimal number of conflicts that is upper bounded in terms of length. Then, we propose a novel bounded-cost variant of CBS, called CBS-Budget (CBSB) by using a bCOA* search at the low-level search of the CBS and by using a modified focal search at the high-level search of the CBS. We prove that CBSB is complete and boundedsuboptimal. In our numerical experiments, CBSB finds a near optimal solution for hundreds of agents within a fraction of a second. CBSB shows state-of-the-art performance, comparable to Explicit Estimation CBS (EECBS), an enhanced recent version of CBS. On the other hand, CBSB is easier to implement than EECBS, since only two priority queues at the high-level search are needed as in Enhanced CBS (ECBS).

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“…Another technique is related to surplus nodes, which are nodes generated but never be expanded to find an optimal solution. Avoiding generating the surplus nodes makes a substantial speedup (Standley, 2010;Felner et al, 2012;Goldenberg et al, 2014). In summary, though these techniques provide exponential speedup for A*-based methods, solution quality still degrades rapidly and computational time increases fast as the agent density increase.…”

Section: Optimal Solvers For Centralized Mpfmentioning

confidence: 99%

A Route Network Planning Method for Urban Air Delivery

He¹,

He²,

Li³

et al. 2022

Preprint

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High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.

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Partial-Expansion A* with Selective Node Generation

Cited by 33 publications

References 14 publications

Suboptimal Variants of the Conflict-Based Search Algorithm for the Multi-Agent Pathfinding Problem

Suboptimal Variants of the Conflict-Based Search Algorithm for the Multi-Agent Pathfinding Problem

CBS-Budget (CBSB): A Complete and Bounded Suboptimal Search for Multi-Agent Path Finding

A Route Network Planning Method for Urban Air Delivery

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