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Arseniy Pertzovsky

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

AAMAS Conference 2025 Conference Paper

Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields

Arseniy Pertzovsky
Roni Stern
Ariel Felner
Roie Zivan

We explore the use of Artificial Potential Fields (APFs) to solve Lifelong Multi-Agent Path Finding (LMAPF) problems. In LMAPF, a team of agents must move to their goal locations without collisions, and new goals are generated upon arrival. We propose methods for incorporating APFs in a range of LMAPF algorithms, including Prioritized Planning and MAPF-LNS2. Experimental results show that using APF yields up to a 7-fold increase in overall system throughput for LMAPF.

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IROS Conference 2024 Conference Paper

CGA: Corridor Generating Algorithm for Multi-Agent Environments

Arseniy Pertzovsky
Roni Stern
Roie Zivan

In this work, we consider path planning for a team of mobile agents where one agent must reach a given target as soon as possible and the others must accommodate to avoid collisions. We call this practical problem the Single-Agent Corridor Generating (SACG) problem and explore several algorithms for solving it. We propose two baseline algorithms based on existing Multi-Agent Path Finding (MAPF) algorithms and outline their limitations. Then, we present the Corridor Generating Algorithm (CGA), a fast and complete algorithm for solving SACG. CGA performs well compared to the baseline approaches. In addition, we show how CGA can be generalized to address the lifelong version of MAPF, where new goals appear over time.

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JAIR Journal 2024 Journal Article

Collision Avoiding Max-Sum for Mobile Sensor Teams

Arseniy Pertzovsky
Roie Zivan
Noa Agmon

Recent advances in technology have large teams of robots with limited computation skills work together in order to achieve a common goal. Their personal actions need to contribute to the joint effort, however, they also must assure that they do not harm the efforts of the other members of the team, e.g., as a result of collisions. We focus on the distributed target coverage problem, in which the team must cooperate in order to maximize utility from sensed targets, while avoiding collisions with other agents. State of the art solutions focus on the distributed optimization of the coverage task in the team level, while neglecting to consider collision avoidance, which could have far reaching consequences on the overall performance. Therefore, we propose CAMS: a collision-avoiding version of the Max-sum algorithm, for solving problems including mobile sensors. In CAMS, a factor-graph that includes two types of constraints (represented by function-nodes) is being iteratively generated and solved. The first type represents the task-related requirements, and the second represents collision avoidance constraints. We prove that consistent beliefs are sent by target representing function-nodes during the run of the algorithm, and identify factor-graph structures on which CAMS is guaranteed to converge to an optimal (collision-free) solution. We present an experimental evaluation in extensive simulations, showing that CAMS produces high quality collision-free coverage also in large and complex scenarios. We further present evidence from experiments in a real multi-robot system that CAMS outperforms the state of the art in terms of convergence time.

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