Search Results for author: Sivakumar Rathinam

Found 15 papers, 7 papers with code

A Mixed-Integer Conic Program for the Moving-Target Traveling Salesman Problem based on a Graph of Convex Sets

no code implementations7 Mar 2024 Allen George Philip, Zhongqiang Ren, Sivakumar Rathinam, Howie Choset

This paper introduces a new formulation that finds the optimum for the Moving-Target Traveling Salesman Problem (MT-TSP), which seeks to find a shortest path for an agent, that starts at a depot, visits a set of moving targets exactly once within their assigned time-windows, and returns to the depot.

Traveling Salesman Problem

DMS*: Minimizing Makespan for Multi-Agent Combinatorial Path Finding

no code implementations11 Dec 2023 Zhongqiang Ren, Anushtup Nandy, Sivakumar Rathinam, Howie Choset

MCPF is challenging as it involves both planning collision-free paths for multiple agents and target sequencing, i. e., solving traveling salesman problems to assign targets to and find the visiting order for the agents.

Heuristic Search for Path Finding with Refuelling

no code implementations19 Sep 2023 Anushtup Nandy, Zhongqiang Ren, Sivakumar Rathinam, Howie Choset

This paper considers a generalization of the Path Finding (PF) with refueling constraints referred to as the Refuelling Path Finding (RF-PF) problem.

Enhanced Multi-Objective A* with Partial Expansion

no code implementations6 Dec 2022 Valmiki Kothare, Zhongqiang Ren, Sivakumar Rathinam, Howie Choset

The Multi-Objective Shortest Path Problem (MO-SPP), typically posed on a graph, determines a set of paths from a start vertex to a destination vertex while optimizing multiple objectives.

Informed Steiner Trees: Sampling and Pruning for Multi-Goal Path Finding in High Dimensions

no code implementations9 May 2022 Nikhil Chandak, Kenny Chour, Sivakumar Rathinam, R. Ravi

We interleave sampling based motion planning methods with pruning ideas from minimum spanning tree algorithms to develop a new approach for solving a Multi-Goal Path Finding (MGPF) problem in high dimensional spaces.

Motion Planning

Enhanced Multi-Objective A* Using Balanced Binary Search Trees

1 code implementation18 Feb 2022 Zhongqiang Ren, Richard Zhan, Sivakumar Rathinam, Maxim Likhachev, Howie Choset

This work addresses a Multi-Objective Shortest Path Problem (MO-SPP) on a graph where the goal is to find a set of Pareto-optimal solutions from a start node to a destination in the graph.

Subdimensional Expansion Using Attention-Based Learning For Multi-Agent Path Finding

1 code implementation29 Sep 2021 Lakshay Virmani, Zhongqiang Ren, Sivakumar Rathinam, Howie Choset

By leveraging a Visual Transformer, we develop a learning-based single-agent planner, which plans for a single agent while paying attention to both the structure of the map and other agents with whom conflicts may happen.

Multi-Agent Path Finding

Multi-Objective Path-Based D* Lite

no code implementations2 Aug 2021 Zhongqiang Ren, Sivakumar Rathinam, Maxim Likhachev, Howie Choset

Incremental graph search algorithms such as D* Lite reuse previous, and perhaps partial, searches to expedite subsequent path planning tasks.

Multi-objective Conflict-based Search Using Safe-interval Path Planning

1 code implementation2 Aug 2021 Zhongqiang Ren, Sivakumar Rathinam, Maxim Likhachev, Howie Choset

This paper addresses a generalization of the well known multi-agent path finding (MAPF) problem that optimizes multiple conflicting objectives simultaneously such as travel time and path risk.

Multi-Agent Path Finding

MS*: A New Exact Algorithm for Multi-agent Simultaneous Multi-goal Sequencing and Path Finding

no code implementations18 Mar 2021 Zhongqiang Ren, Sivakumar Rathinam, Howie Choset

In multi-agent applications such as surveillance and logistics, fleets of mobile agents are often expected to coordinate and safely visit a large number of goal locations as efficiently as possible.

Multi-Agent Path Finding

S$^*$: A Heuristic Information-Based Approximation Framework for Multi-Goal Path Finding

1 code implementation15 Mar 2021 Kenny Chour, Sivakumar Rathinam, Ramamoorthi Ravi

We combine ideas from uni-directional and bi-directional heuristic search, and approximation algorithms for the Traveling Salesman Problem, to develop a novel framework for a Multi-Goal Path Finding (MGPF) problem that provides a 2-approximation guarantee.

Traveling Salesman Problem

Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions

no code implementations8 Mar 2021 Zhongqiang Ren, Sivakumar Rathinam, Howie Choset

Multi-agent path finding (MAPF) determines an ensemble of collision-free paths for multiple agents between their respective start and goal locations.

Computational Efficiency Multi-Agent Path Finding

Subdimensional Expansion for Multi-objective Multi-agent Path Finding

1 code implementation2 Feb 2021 Zhongqiang Ren, Sivakumar Rathinam, Howie Choset

One example of subdimensional expansion, when applied to A*, is called M* and M* was limited to a single objective function.

Computational Efficiency Multi-Agent Path Finding

A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding

1 code implementation11 Jan 2021 Zhongqiang Ren, Sivakumar Rathinam, Howie Choset

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.

Multi-Agent Path Finding

String Stability of Connected Vehicle Platoons under Lossy V2V Communication

1 code implementation28 Aug 2020 Vamsi Vegamoor, Sivakumar Rathinam, Swaroop Darbha

Recent advances in vehicle connectivity have allowed formation of autonomous vehicle platoons for improved mobility and traffic throughput.

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