Multi-Robot Routing with Time Windows: A Column Generation Approach

• URL: http://arxiv.org/abs/2103.08835v1
• Date: Tue, 16 Mar 2021 03:39:42 GMT
• Title: Multi-Robot Routing with Time Windows: A Column Generation Approach
• Authors: Naveed Haghani, Jiaoyang Li, Sven Koenig, Gautam Kunapuli, Claudio Contardo, Amelia Regan, Julian Yarkony
• Abstract summary: We consider the problem of coordinating a fleet of robots performing picking operations in a warehouse. We propose an efficient optimization scheme that avoids consideration of every increment within the time windows. We also propose a pricing algorithm that can efficiently solve the pricing subproblem.
• Score: 19.425263342626007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robots performing tasks in warehouses provide the first example of wide-spread adoption of autonomous vehicles in transportation and logistics. The efficiency of these operations, which can vary widely in practice, are a key factor in the success of supply chains. In this work we consider the problem of coordinating a fleet of robots performing picking operations in a warehouse so as to maximize the net profit achieved within a time period while respecting problem- and robot-specific constraints. We formulate the problem as a weighted set packing problem where the elements in consideration are items on the warehouse floor that can be picked up and delivered within specified time windows. We enforce the constraint that robots must not collide, that each item is picked up and delivered by at most one robot, and that the number of robots active at any time does not exceed the total number available. Since the set of routes is exponential in the size of the input, we attack optimization of the resulting integer linear program using column generation, where pricing amounts to solving an elementary resource-constrained shortest-path problem. We propose an efficient optimization scheme that avoids consideration of every increment within the time windows. We also propose a heuristic pricing algorithm that can efficiently solve the pricing subproblem. While this itself is an important problem, the insights gained from solving these problems effectively can lead to new advances in other time-widow constrained vehicle routing problems.

Related papers

• Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
  This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-08-29T20:22:22Z)
• Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses [1.2810395420131764]
  Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories. We consider a realistic problem of online order delivery in a warehouse, where a fleet of robots bring the products belonging to each order from shelves to workstations. This creates a stream of inter-dependent pickup and delivery tasks and the associated MAPF problem consists of computing realistic collision-free robot trajectories.
  arXiv  Detail & Related papers  (2024-08-26T15:13:38Z)
• DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments [55.204450019073036]
  We present a novel reinforcement learning based task allocation and decentralized navigation algorithm for mobile robots in warehouse environments. We consider the problem of joint decentralized task allocation and navigation and present a two level approach to solve it. We observe improvement up to 14% in terms of task completion time and up-to 40% improvement in terms of computing collision-free trajectories for the robots.
  arXiv  Detail & Related papers  (2022-09-07T00:35:27Z)
• Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots [60.06216976204385]
  We present an efficient motion planning framework for simultaneously solving locomotion, grasping, and contact problems. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45deg with a much shorter planning time.
  arXiv  Detail & Related papers  (2022-07-04T13:52:10Z)
• Multi-Robot Active Mapping via Neural Bipartite Graph Matching [49.72892929603187]
  We study the problem of multi-robot active mapping, which aims for complete scene map construction in minimum time steps. The key to this problem lies in the goal position estimation to enable more efficient robot movements. We propose a novel algorithm, namely NeuralCoMapping, which takes advantage of both approaches.
  arXiv  Detail & Related papers  (2022-03-30T14:03:17Z)
• Machine-learning-based arc selection for constrained shortest path problems in column generation [5.08128537391027]
  In this work, we propose a new pricing algorithm based on machine learning. The objective is to reduce the size of the network and accelerate the PP, keeping only the arcs that have a high chance to be part of the linear relaxation solution. The method has been applied to two specific problems: the vehicle and crew scheduling problem in public transit and the vehicle routing problem with time windows.
  arXiv  Detail & Related papers  (2022-01-07T16:49:12Z)
• Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients [122.85280150421175]
  We present a learning method to solve the unlabelled motion problem with motion constraints and space constraints in 2D space for a large number of robots. We employ a graph neural network (GNN) to parameterize policies for the robots.
  arXiv  Detail & Related papers  (2021-02-11T21:57:43Z)
• Optimal Sequential Task Assignment and Path Finding for Multi-Agent Robotic Assembly Planning [42.38068056643171]
  We study the problem of sequential task assignment and collision-free routing for large teams of robots in applications with inter-task precedence constraints. We propose a hierarchical algorithm for computing makespan-optimal solutions to the problem.
  arXiv  Detail & Related papers  (2020-06-16T00:45:07Z)
• Integer Programming for Multi-Robot Planning: A Column Generation Approach [21.217989597414384]
  We consider the problem of coordinating a fleet of robots in a warehouse so as to maximize the reward achieved within a time limit. We formulate the problem as a weighted set packing problem where elements are defined as being the space-time positions a robot can occupy and the items that can be picked up and delivered. We enforce that robots do not collide, that each item is delivered at most once, and that the number of robots active at any time does not exceed the total number available.
  arXiv  Detail & Related papers  (2020-06-08T18:19:14Z)
• Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints [52.58352707495122]
  We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
  arXiv  Detail & Related papers  (2020-05-27T01:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.