Conflict-Based Search Research Articles

RH-ECBS: enhanced conflict-based search for MRPP with region heuristics

Abstract This paper proposes a novel two-layer framework based on conflict-based search and regional divisions to improve the efficiency of multi-robot path planning. The high-level layer targets the reduction of conflicts and deadlocks, while the low-level layer is responsible for actual path planning. Distinct from previous dual-level search frameworks, the novelties of this work are (1) subdivision of planning regions for each robot to decrease the number of conflicts encountered during planning; (2) consideration of the number of robots in the region during planning in the node expansion stage of A*, and (3) formal proof demonstrating the nonzero probability of the proposed method in obtaining a solution, along with providing the upper bound of the solution in a special case. Experimental comparisons with Enhanced Conflict-Based Search demonstrate that the proposed method not only reduces the number of conflicts but also achieves a computation time reduction of over 30%.

A decoupled solution to heterogeneous multi-formation planning and coordination for object transportation

Multi-robot formations have numerous applications, such as cooperative object transportation in intelligent warehouses. In this context, robots are tasked with delivering objects in formation while avoiding intra- and inter-formation collisions. This necessitates the development of solutions for multi-robot task allocation, formation generation, rigid formation route planning, and formation coordination. In this paper, we present a cooperative formation object transportation system for heterogeneous multi-robot systems which captures robot dynamics and avoids inter-formation collisions. Accounting for heterogeneous formations expands the applicability of the proposed robotic transport system. For formation generation, we propose an approach based on conflict-based search, which integrates high-level path planning with low-level trajectory optimisation. For heterogeneous formation planning, we present a two-stage iterative trajectory optimisation framework which adheres to the kinematic constraints of our heterogeneous multi-robot system while retaining formation rigidity. For multi-formation coordination, we use a loosely-coupled algorithm which can guarantee collision-free and deadlock-free formation navigation under minimal assumptions. We demonstrate the efficacy of our approach in simulation.

Clique Analysis and Bypassing in Continuous-Time Conflict-Based Search

Clique Analysis and Bypassing in Continuous-Time Conflict-Based Search

Designing Automation for Pickup and Delivery Tasks in Modern Warehouses Using Multi Agent Path Finding (MAPF) and Multi Agent Reinforcement Learning (MARL) Based Approaches

A warehouse pickup and delivery problem finds its solution using multi agent path finding (MAPF) approach. Also, the problem has been used to showcase the capabilities of the multi agent reinforcement learning (MARL). The warehouse pickup and delivery work needs the agent to pick up a requested item and successfully deliver it to the intended location within the warehouse. The problem has been solved based on two approaches that include single shot and lifelong problem solution. The single shot solution has the delivery as the final goal and thus once it reaches the delivery address, it stops whereas in case of lifelong, the agent needs to deliver the item which it had picked, deliver it to the required place and then again pick up new item until requests are satisfied. The strategy used by multi agent path finding (MAPF) approach aims at constructing collision free paths to reach the delivery location but in case of multi agent reinforcement learning (MARL), the agents’ decision making tactics (or policies) are learned which are then used to help agents decide path to be followed based on environment state and agent’s position. The results show that the lifelong conflict based search (CBS) is a better option when the agents are less in number as in that case, the re-planning will take overall less time but when the agents are large in number then this re-planning can take very long to produce conflict free paths from source to goal nodes. In this case, shared experience action critic (SEAC) which is based on multi agent reinforcement learning (MARL) approach can be more efficient choice as it takes the current environment state to give the most suitable action for that time t. For this study the agents taken for learning are homogeneous in nature that can pickup and deliver any type of requested item. We can address the same pickup and delivery problem when the agents are not all same and differ in their capabilities and the type of item they can handle.

Decentralized multi-agent path finding framework and strategies based on automated negotiation

This paper introduces a negotiation framework to solve the Multi-Agent Path Finding (MAPF) Problem for self-interested agents in a decentralized fashion. The framework aims to achieve a good trade-off between the privacy of the agents and the effectiveness of solutions. Accordingly, a token-based bilateral negotiation protocol and two negotiation strategies are presented. The experimental results over four different settings of the MAPF problem show that the proposed approach could find conflict-free path solutions albeit suboptimally, especially when the search space is large and high-density. In contrast, Explicit Estimation Conflict-Based Search (EECBS) struggles to find optimal solutions. Besides, deploying a sophisticated negotiation strategy that utilizes information about local density for generating alternative paths can yield remarkably better solution performance in this negotiation framework.

Safer Conflict-Based Search: Risk-Constrained Optimal Pathfinding for Multiple Connected and Automated Vehicles

Safer Conflict-Based Search: Risk-Constrained Optimal Pathfinding for Multiple Connected and Automated Vehicles

An Improved Bounded Conflict-Based Search for Multi-AGV Pathfinding in Automated Container Terminals

An Improved Bounded Conflict-Based Search for Multi-AGV Pathfinding in Automated Container Terminals

A coordinated scheduling approach for task assignment and multi-agent path planning

Path finding is an essential problem in multi-agent systems, widely employed in warehousing and logistics. However, most of the current studies focus on the problem of assigning one agent with one task in a period, which may hinder the efficiency of path planning of the systems when facing multi-tasks. To address this problem, we propose a multi-layer planner, under which a co-scheduling method for multi-agent with batch tasks and path planning in a continuous workspace is proposed. In the task allocation layer of the framework, a standard Genetic Algorithm (GA) is adopted, which optimizes the allocation of task sets, and minimizes the path lengths and the probability of conflicts. Secondly, an Improved Car-Like Conflict-Based Search (ICL-CBS) algorithm is presented as the conflict resolution layer to reduce the runtime. Finally, ICL-CBS and the Spatiotemporal Hybrid-State A* (SHA*) algorithm are jointly used to determine multi-agent paths in the path planning layer. We conduct experiments and compare our method with the baseline algorithm on random obstacles and practical scenarios. The results show that our method effectively improves the efficiency of multi-task completion, and alleviates the computational burden of underlying path planning. Specifically, our method has less runtime.

Near-Optimal Multi-Robot Motion Planning with Finite Sampling

An underlying structure in several sampling-based methods for continuous multirobot motion planning (MRMP) is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tensor roadmap</i> , which emerges from combining multiple probabilistic roadmap (PRM) graphs constructed for the individual robots via a tensor product. We study the conditions under which the tensor roadmap encodes a near-optimal solution for MRMP—satisfying these conditions implies near optimality for a variety of popular planners, including dRRT*, and the discrete methods M* and conflict-based search, when applied to the continuous domain. We develop the first finite-sample analysis of this kind, which specifies the number of samples, their deterministic distribution, and magnitude of the connection radii that should be used by each individual PRM graph, to guarantee near-optimality using the tensor roadmap. This significantly improves upon a previous asymptotic analysis, wherein the number of samples tends to infinity. Our new finite sample-size analysis supports guaranteed high-quality solutions in practice within finite time. To achieve our new result, we first develop a sampling scheme, which we call the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">staggered grid</i> , for finite-sample motion planning for individual robots, which requires significantly fewer samples than previous work. We then extend it to the much more involved MRMP setting, which requires to account for interactions among multiple robots. Finally, we report on a few experiments that serve as a verification of our theoretical findings and raise interesting questions for further investigation.

A proposal on multi-agent static path planning strategy for minimizing radiation dose

To minimize the cumulative radiation dose, various path-finding approaches for single agent have been proposed. However, for emergence situations such as nuclear power plant accident, these methods cannot be effectively utilized for evacuating a large number of workers because no multi-agent method is valid to conduct the mission. In this study, a novel algorithm for solving the multi-agent path-finding problem is proposed using the conflict-based search approach and the objective function redefined in terms of the cumulative radiation dose. The proposed method can find multi paths that all agents arrive at the destinations with reducing the overall radiation dose. To verify the proposed method, three problems were defined. In the single-agent problem, the objective function proposed in this study reduces the cumulative dose by 82% compared with that of the shortest distance algorithm in experiment environment of this study. It was also verified in the two multi-agent problems that multi paths with minimized the overall radiation dose, in which all agents can reach the destination without collision, can be found. The method proposed in this study will contribute to establishing evacuation plans for improving the safety of workers in radiation-related facilities.

Efficient Multi Agent Path Finding with Turn Actions

Current approaches for real-world Multi-Agent Path Finding (MAPF) usually start with a simplified MAPF model and modify the resulting plans so they are kinematically feasible. We investigate one such problem, called MAPF with turn actions MAPF_T, and show that ignoring the kinematic constraints significantly increases solution cost. A first modification of the popular Conflict-Based Search algorithm to MAPF_T yields significantly better plans but comes at the cost of substantial decreases in scalability. We then introduce several techniques that can improve the performance of CBS for MAPF_T, including stronger and generalised versions of existing symmetry-breaking constraints and a novel pruning technique that eliminates redundant branches in the CBS constraint tree. Experimental results on six popular MAPF domains show convincing improvements for CBS success rate and substantial reductions in node expansions and runtime.

Beyond Pairwise Reasoning in Multi-Agent Path Finding

In Multi-Agent Path Finding (MAPF), we are asked to plan collision-free paths for teams of moving agents. Among the leading methods for optimal MAPF is Conflict-Based Search (CBS), an algorithmic family which has received intense attention in recent years and for which large advancements in efficiency and effectiveness have been reported. Yet all of the recent CBS gains come from reasoning over pairs of agents only. In this paper, we show how to further improve CBS by reasoning about more than two agents at the same time. Our new cluster reasoning techniques allow us to generate stronger bounds for CBS and to identify more bypasses (alternative cost-equivalent paths), which reduce the number of nodes in the CBS conflict tree.

Binary Branching Multi-Objective Conflict-Based Search for Multi-Agent Path Finding

This paper considers a multi-agent multi-objective path-finding problem that requires not only finding collision-free paths for multiple agents from their respective start locations to their respective goal locations but also optimizing multiple objectives simultaneously. In general, there is no single solution that optimizes all the objectives simultaneously, and the problem is thus to find the so-called Pareto-optimal frontier. To solve this problem, an algorithm called Multi-Objective Conflict-Based Search (MO-CBS) was recently developed and is guaranteed to find the exact Pareto-optimal frontier. However, MO-CBS does not scale well with the number of agents due to the large branching factor of the search, which leads to a lot of duplicated effort in agent-agent collision resolution. This paper therefore develops a new algorithm called Binary Branching MO-CBS (BB-MO-CBS) that reduces the branching factor as well as the duplicated collision resolution during the search, which expedites the search as a result. Our experimental results show that BB-MO-CBS reduces the number of conflicts by up to two orders of magnitude and often doubles or triples the success rates of MO-CBS on various maps given a runtime limit.

Cost Splitting for Multi-Objective Conflict-Based Search

The Multi-Objective Multi-Agent Path Finding (MO-MAPF) problem is the problem of finding the Pareto-optimal frontier of collision-free paths for a team of agents while minimizing multiple cost metrics. Examples of such cost metrics include arrival times, travel distances, and energy consumption. In this paper, we focus on the Multi-Objective Conflict-Based Search (MO-CBS) algorithm, a state-of-the-art MO-MAPF algorithm. We show that the standard splitting strategy used by MO-CBS can lead to duplicate search nodes and hence can duplicate the search effort of MO-CBS. To address this issue, we propose two new splitting strategies for MO-CBS, namely cost splitting and disjoint cost splitting. Our theoretical results show that, when using either splitting strategy, MO-CBS maintains its completeness and optimality guarantees. Our experimental results show that disjoint cost splitting, our best splitting strategy, speeds up MO-CBS by up to two orders of magnitude and substantially improves its success rates in various settings.

A multi-AGV fast path planning method based on improved CBS algorithm in workshops

Automatic guided vehicles (AGVs) are an important component of workshop logistics distribution systems. With the continuous increase in human cost, unmanned workshops are becoming increasingly popular, and an increasing number of AGVs are being applied to workshop logistics distribution systems. However, with the increasing number of AGVs in the workshop, planning a path quickly for each AGV is challenging, and the response time of multi-AGV fast path planning cannot meet actual requirements. To solve this problem, a multi-AGV fast path planning method based on an improved conflict-based search (ICBS) algorithm is proposed. First, the path planning problem is analyzed, and the environment expression, task, objective and constraint models are established. Subsequently, a two-layer ICBS algorithm is proposed to achieve fast path planning. At the bottom layer, an improved A* algorithm is adopted, and the selection strategy of the nodes is changed when multiple minimum values are equal. In the upper layer, the conflict number, conflict objective and conflict constraint sets are established. Three priority rules including Most Conflicts First (MCF) rule, Earliest Conflict First (ECF) rule, Single Search for Conflict Point (SSCP) rule are used. And a conflict elimination strategy is proposed. Finally, the superiority of the algorithm-solving is verified based on benchmark and actual workshop maps.

The Multi-Agent Transportation Problem

We introduce the multi-agent transportation (MAT) problem, where agents have to transport containers from their starting positions to their designated goal positions. Movement takes place in a common environment where collisions between agents and between containers must be avoided. In contrast to other frameworks such as multi-agent pathfinding (MAPF) or multi-agent pickup and delivery (MAPD), the agents are allowed to separate from the containers at any time, which can reduce the makespan and also allows for plans in scenarios that are unsolvable otherwise. We present a complexity analysis establishing the problem's NP-completeness and show how the problem can be reduced to a sequence of SAT problems when optimizing for makespan. A MAT solver is empirically evaluated with regard to varying input characteristics and movement constraints and compared to a MAPD solver that utilizes conflict-based search (CBS).

UGV Parking Planning Based on Swarm Optimization and Improved CBS in High-Density Scenarios for Innovative Urban Mobility

The existence of information silos between vehicles and parking lots means that Unmanned Ground Vehicles (UGVs) repeatedly drive to seek available parking slots, resulting in wasted resources, time consumption and traffic congestion, especially in high-density parking scenarios. To address this problem, a novel UGV parking planning method is proposed in this paper, which consists of cooperative path planning, conflict resolution strategy, and optimal parking slot allocation, intending to avoid ineffective parking seeking by vehicles and releasing urban traffic pressure. Firstly, the parking lot induction model was established and the IACA–IA was developed for optimal parking allocation. The IACA–IA was generated using the improved ant colony algorithm (IACA) and immunity algorithm. Compared with the first-come-first-served algorithm (FCFS), the normal ant colony algorithm (NACA), and the immunity algorithm (IA), the IACA–IA was able to allocate optimal slots at a lower cost and in less time in complex scenarios with multi-entrance parking lots. Secondly, an improved conflict-based search algorithm (ICBS) was designed to efficiently resolve the conflict of simultaneous path planning for UGVs. The dual-layer objective expansion strategy is the core of the ICBS, which takes the total path cost of UGVs in the extended constraint tree as the first layer objective, and the optimal driving characteristics of a single UGV as the second layer objective. Finally, three kinds of load-balancing and unbalanced parking scenarios were constructed to test the proposed method, and the performance of the algorithm was demonstrated from three aspects, including computation, quality and timeliness. The results show that the proposed method requires less computation, has higher path quality, and is less time-consuming in high-density scenarios, which provide a reasonable and efficient solution for innovative urban mobility.

A Conflict-Based Search Framework for Multiobjective Multiagent Path Finding

Conventional multi-agent path planners typically compute an ensemble of paths while optimizing a single objective, such as path length. However, many applications may require multiple objectives, say fuel consumption and completion time, to be simultaneously optimized during planning and these criteria may not be readily compared and sometimes lie in competition with each other. The goal of the problem is thus to find a Pareto-optimal set of solutions instead of a single optimal solution. Naively applying existing multi-objective search algorithms, such as multi-objective A* (MOA*), to multi-agent path finding may prove to be inefficient as the dimensionality of the search space grows exponentially with the number of agents. This article presents an approach named Multi-Objective Conflict-Based Search (MO-CBS) that attempts to address this so-called curse of dimensionality by leveraging prior Conflict-Based Search (CBS), a well-known algorithm for single-objective multi-agent path finding, and principles of dominance from multi-objective optimization literature. We also develop several variants of MO-CBS to improve its performance. We prove that MO-CBS and its variants can compute the entire Pareto-optimal set. Numerical results show that MO-CBS outperforms MOM*, a recently developed state-of-the-art multi-objective multi-agent planner.

군집 로봇의 충돌 회피를 고려한 순차적 경로계획

Multi-robot path planning involves establishing collision-free paths for multiple robots such that each robot reaches its goal without colliding with other robots. The possibility of collision between robots is high in dynamic environments where robots with different speeds move simultaneously. For a group of robots moving at different speeds, a path-planning method was proposed for minimizing the time cost while avoiding collisions between obstacles and robots. In the proposed method, the path is sequentially planned according to the predefined priorities between robots so that each robot can plan the path in a distributed manner. The performance of the proposed algorithm was compared with existing algorithms, namely hierarchical cooperative A* and conflict-based search, and its performance in various real-world scenarios was verified using a three-dimensional robot simulator.

Conflict-Based Search for Optimal Multi-Agent Pathfinding

We are assumed a customary of mediators in the multi-agent pathfinding problem (MAPF), each of which has its own start and goal positions. The objective is to discovery paths for each agent without causing any collisions. The majority of previous work on the optimal solution to this issue has useful single-agent exploration variations of the A* algorithm and treated each agent as a single "joint agent." The Conflict Based Search (CBS) optimum multi-agent pathfinding algorithm is the subject of this paper. CBS has two layer algorithm that does not use a single "joint agent" model to solve the problem. A Conflict Tree (CT), which is a tree based on conflicts between individual agents, is the high-level search engine. In the CT, each node represents a set of constraints on the agents' motion. To meet the high-level CT node's constraints, quick single-agent searches are carried out at the low level. CBS can frequently examine fewer states than A* while still maintaining optimality thanks to this two-level formulation. We discuss CBS's advantages and disadvantages. The Meta-Agent CBS (MA-CBS) algorithm is also discussed.MA-CBS is a broadening of CBS.MA-CBS, in contrast to basic CBS, permits low-level single-agent searches. Instead, agents can be combined into small groups of joint agents with MA-CBS. Performance is further enhanced and some of the drawbacks of basic CBS are reduced as a result. In point of fact, MA-CBS is a framework that can be layered on top of any MAPF solver that is both complete and optimal to boost its performance. A speedup of up to an order of magnitude over previous methods is demonstrated by experimental results on various problems. We are given a set of agents in the multi-agent pathfinding problem (MAPF), each of which has its own start and goal positions. The objective is to find paths for each agent without causing any collisions. The majority of previous work on the optimal solution to this issue has applied single-agent search variants of the A* algorithm and treated each agent as a single "joint agent." The Conflict Based Search (CBS) optimal multi-agent pathfinding algorithm is the subject of this paper. CBS is a two-level algorithm that does not use a single "joint agent" model to solve the problem. A Conflict Tree (CT), which is a tree based on conflicts between individual agents, is the high-level search engine. In the CT, each node represents a set of constraints on the agents' motion. To meet the high-level CT node's constraints, quick single-agent searches are carried out at the low level. CBS can frequently examine fewer states than A* while still maintaining optimality thanks to this two-level formulation. We discuss CBS's advantages and disadvantages. The Meta-Agent CBS (MA-CBS) algorithm is also discussed.MA-CBS is a broadening of CBS.MA-CBS, in contrast to basic CBS, permits low-level single-agent searches. Instead, agents can be combined into small groups of joint agents with MA-CBS. Performance is further enhanced and some of the drawbacks of basic CBS are reduced as a result. In point of fact, MA-CBS is a framework that can be layered on top of any MAPF solver that is both complete and optimal to boost its performance. A speedup of up to an order of magnitude over previous methods is demonstrated by experimental results on various problems.