Search Results for author:
Found 14 papers, 6 papers with code
A Mixed-Integer Conic Program for the Moving-Target Traveling Salesman Problem based on a Graph of Convex Sets
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.
DMS*: Minimizing Makespan for Multi-Agent Combinatorial Path Finding
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
This paper considers a generalization of the Path Finding (PF) with refueling constraints referred to as the Refuelling Path Finding (RF-PF) problem.
Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree
As a leading approach to address TAPF, Conflict-Based Search with Target Assignment (CBS-TA) leverages both K-best target assignments to create multiple search trees and Conflict-Based Search (CBS) to resolve collisions in each search tree.
Enhanced Multi-Objective A* with Partial Expansion
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.
MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position
To intelligently balance the robustness of sub-map merging and exploration efficiency, we develop a new approach for lidar-based multi-agent exploration, which can direct one agent to repeat another agent's trajectory in an \emph{adaptive} manner based on the quality indicator of the sub-map merging process.
Enhanced Multi-Objective A* Using Balanced Binary Search Trees
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
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-Objective Path-Based D* Lite
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
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.
MS*: A New Exact Algorithm for Multi-agent Simultaneous Multi-goal Sequencing and Path Finding
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.
Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions
Multi-agent path finding (MAPF) determines an ensemble of collision-free paths for multiple agents between their respective start and goal locations.
Subdimensional Expansion for Multi-objective Multi-agent Path Finding
One example of subdimensional expansion, when applied to A*, is called M* and M* was limited to a single objective function.
A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding
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.
