18 research outputs found
Towards computing low-makespan solutions for multi-arm multi-task planning problems
We propose an approach to find low-makespan solutions to multi-robot
multi-task planning problems in environments where robots block each other from
completing tasks simultaneously. We introduce a formulation of the problem that
allows for an approach based on greedy descent with random restarts for
generation of the task assignment and task sequence. We then use a multi-agent
path planner to evaluate the makespan of a given assignment and sequence. The
planner decomposes the problem into multiple simple subproblems that only
contain a single robots and a single task, and can thus be solved quickly to
produce a solution for a fixed task sequence. The solutions to the subproblems
are then combined to form a valid solution to the original problem. We showcase
the approach on robotic stippling and robotic bin picking with up to 4 robot
arms. The makespan of the solutions found by our algorithm are up to 30% lower
compared to a greedy approach.Comment: Workshop for Planning and Robotics (PlanRob), International
Conference on Automated Planning and Scheduling (ICAPS), 202
An Extension of BIM Using AI: a Multi Working-Machines Pathfinding Solution
Multi working-machines pathfinding solution enables more mobile machines simultaneously to work inside of a working site so that the productivity can be expected to increase evolutionary. To date, the potential cooperation conflicts among construction machinery limit the amount of construction machinery investment in a concrete working site. To solve the cooperation problem, civil engineers optimize the working site from a logistic perspective while computer scientists improve pathfinding algorithmsâ performance on the given benchmark maps. In the practical implementation of a construction site, it is sensible to solve the problem with a hybrid solution; therefore, in our study, we proposed an algorithm based on a cutting-edge multi-pathfinding algorithm to enable the massive number of machines cooperation and offer the advice to modify the unreasonable part of the working site in the meantime. Using the logistic information from BIM, such as unloading and loading point, we added a pathfinding solution for multi machines to improve the whole construction fleetâs productivity. In the previous study, the experiments were limited to no more than ten participants, and the computational time to gather the solution was not given; thus, we publish our pseudo-code, our tested map, and benchmark our results. Our algorithmâs most extensive feature is that it can quickly replan the path to overcome the emergency on a construction site
Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree
Combined Target-Assignment and Path-Finding problem (TAPF) requires
simultaneously assigning targets to agents and planning collision-free paths
for agents from their start locations to their assigned targets. 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. While
being able to find an optimal solution, CBS-TA suffers from scalability due to
the duplicated collision resolution in multiple trees and the expensive
computation of K-best assignments. We therefore develop Incremental Target
Assignment CBS (ITA-CBS) to bypass these two computational bottlenecks. ITA-CBS
generates only a single search tree and avoids computing K-best assignments by
incrementally computing new 1-best assignments during the search. We show that,
in theory, ITA-CBS is guaranteed to find an optimal solution and, in practice,
is computationally efficient
Multi-Robot Task Assignment and Path Finding for Time-Sensitive Missions with Online Task Generation
Executing time-sensitive multi-robot missions involves two distinct problems:
Multi-Robot Task Assignment (MRTA) and Multi-Agent Path Finding (MAPF).
Computing safe paths that complete every task and minimize the time to mission
completion, or makespan, is a significant computational challenge even for
small teams. In many missions, tasks can be generated during execution which is
typically handled by either recomputing task assignments and paths from
scratch, or by modifying existing plans using approximate approaches. While
performing task reassignment and path finding from scratch produces
theoretically optimal results, the computational load makes it too expensive
for online implementation. In this work, we present Time-Sensitive Online Task
Assignment and Navigation (TSOTAN), a framework which can quickly incorporate
online generated tasks while guaranteeing bounded suboptimal task assignment
makespans. It does this by assessing the quality of partial task reassignments
and only performing a complete reoptimization when the makespan exceeds a user
specified suboptimality bound. Through experiments in 2D environments we
demonstrate TSOTAN's ability to produce quality solutions with computation
times suitable for online implementation.Comment: 7 pages, 5 figure
A Conflict-Based Search Framework for Multi-Objective Multi-Agent 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. 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 size of the space of possible solutions, i.e., the
Pareto-optimal set, can grow exponentially with the number of agents (the
dimension of the search space). This article presents an approach named
Multi-Objective Conflict-Based Search (MO-CBS) that bypasses 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 further improve its performance. We prove
that MO-CBS and its variants are able to compute the entire Pareto-optimal set.
Numerical results show that MO-CBS outperforms both MOA* as well as MOM*, a
recently developed state-of-the-art multi-objective multi-agent planner.Comment: 11 pages, preliminary version published in ICRA 2021, journal version
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Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding
We formalize and study the multi-goal task assignment and path finding
(MG-TAPF) problem from theoretical and algorithmic perspectives. The MG-TAPF
problem is to compute an assignment of tasks to agents, where each task
consists of a sequence of goal locations, and collision-free paths for the
agents that visit all goal locations of their assigned tasks in sequence.
Theoretically, we prove that the MG-TAPF problem is NP-hard to solve optimally.
We present algorithms that build upon algorithmic techniques for the
multi-agent path finding problem and solve the MG-TAPF problem optimally and
bounded-suboptimally. We experimentally compare these algorithms on a variety
of different benchmark domains.Comment: ICRA 202
Optimizations of a Multi-Agent System for a Real-World Warehouse Problem
In recent years, many warehouses applied mobile robots to move products from one location to another. We focus on a traditional warehouse where agents are humans, and they are engaged with tasks to navigate to the next destination one after the other. The possible destinations are determined at the beginning of the daily shift. Our real-world warehouse client asked us to minimize the total wage cost, and to minimize the irritation of the workers because of conflicts in their tasks. We define a heuristic for the optimizations for splitting the orders into warehouse carts, defining the sequence of the products within the carts, and the assignment of the carts to workers. We extend Multi-Agent Path Finding (MAPF) solution techniques. Furthermore, we have implemented our proposal in a simulation software, and we have run several experiments. According to the experiments, the make-span and the wage cost cannot be reduced with the heuristic optimization, however the heuristic optimization considerably reduces the irritation of the workers. We conclude our work with a guideline for the warehouse