Conflict-Free Routing of Mobile Robots
Doktorsavhandling, 2022

The recent advances in perception have enabled the development of more autonomous mobile robots in the sense that they can operate in a more dynamic environment where obstacles surrounding the robot emerge, disappear, and move. The increased perception of Autonomous Mobile Robots (AMRs) allows them to plan detailed on-line trajectories in order to avoid previously unforeseen obstacles, making AMRs useful in dynamic environments where humans, traditional fork-lifts, and also other mobile robots operate. These abilities contributed to increase automation in logistic applications. This thesis discusses how to efficiently operate a fleet of AMRs and make sure that all tasks are successfully completed.

Assigning robots to specific delivery tasks and deciding the routes they have to travel can be modelled as a variant of the classical Vehicle Routing Problem (VRP), the combinatorial optimization problem of designing routes for vehicles. In related research it has been extended to scheduling routes for vehicles to serve customers according to predetermined specifications, such as arrival time at a customer, amount of goods to deliver, etc.

In this thesis we consider to schedule a fleet of robots such that areas avoid being congested, delivery time-windows are met, the need for robots to recharge is considered, while at the same time the robots have freedom to use alternative paths to handle changes in the environment. This particular version of the VRP, called CF-EVRP (Conflict-free Electrical Vehicle Routing Problem) is motivated by an industrial need. In this work we consider using optimizing general purpose solvers, in particular, MILP and SMT solvers are investigated. We run extensive computational analysis over well-known combinatorial optimization problems, such as job shop scheduling and bin-packing problems, to evaluate modeling techniques and the relative performance of state-of-the-art MILP and SMT solvers.

We propose a monolithic model for the CF-EVRP as well as a compositional approach that decomposes the problem into sub-problems and formulate them as either MILP or SMT problems depending on what fits each particular problem best. The performance of the two approaches is evaluated on a set of CF-EVRP benchmark problems, showing the feasibility of using a compositional approach for solving practical fleet scheduling problems.

SMT

Job Shop

Bin Sorting

Vehicle Routing

MILP

Campus Johanneberg, Hörsalsvägen 14, lecture hall HC2
Opponent: Maria Pia Fanti, System and Control Engineering ,Department of Electrical and Information Engineering, Polytechnic of Bari (Italy)

Författare

Sabino Francesco Roselli

Chalmers, Elektroteknik, System- och reglerteknik

A Compositional Algorithm for the Conflict-Free Electric Vehicle Routing Problem

IEEE Transactions on Automation Science and Engineering,;Vol. In Press(2022)

Artikel i vetenskaplig tidskrift

On the Use of Equivalence Classes for Optimal and Suboptimal Bin Packing and Bin Covering

IEEE Transactions on Automation Science and Engineering,;Vol. 18(2021)p. 369-381

Artikel i vetenskaplig tidskrift

Solving the conflict-free electric vehicle routing problem using SMT solvers

2021 29th Mediterranean Conference on Control and Automation, MED 2021,;(2021)p. 542-547

Paper i proceeding

SMT Solvers for Job-Shop Scheduling Problems: Models Comparison and Performance Evaluation

IEEE International Conference on Automation Science and Engineering,;Vol. 2018-August(2018)p. 547-552

Paper i proceeding

Leveraging Conflicting Constraints in Solving Vehicle Routing Problems

IFAC-PapersOnLine,;Vol. 55(2022)p. 22-29

Paper i proceeding

Material handling by means of autonomous mobile robots (AMRs) is a phenomenon that has gained momentum in the last few years, as the perception and decision-making capabilities of the robots increase, and as computers become more powerful and can control larger and larger fleets. In modern industrial applications, fleets of AMRs operate in a heterogeneous environment, shared with humans and other vehicles and obstacles. In this work we model the features of a modern production environment and thus formulate the Conflict-Free Electric Vehicle Routing Problem (CF-EVRP). The inputs to the CF-EVRP are:

·       information on the fleet (how many AMRs, of what type, and their operating range);

·       list of tasks to execute (location in the plant and time windows for execution);

·       plant layout (road segments, allowed travelling direction, and depots location).

Solving the CF-EVRP provides a schedule for the fleet of AMRs to execute the tasks within their time windows, and to account for the AMR's limited operating range, so that the charging time at the depots is part of the schedule. Moreover, the CF-EVRP includes capacity constraints on the road segments, limitations on the number of robots that can travel on road segments at the same time.

The overall problem is to find solutions that satisfy all constraints while avoiding travelling unnecessarily long routes, and at the same time meet the stipulated time-windows to deliver material just-in-time. The compositional algorithm (ComSat) presented in this work is based on the idea to break down the overall scheduling problem into sub-problems that are easier to solve, and then to build a schedule based on the solutions of the sub-problems. ComSat is designed to work well for industrial scenarios where there are good reasons to believe that feasible solutions do exist. This is a reasonable assumption as in an industrial setting a sufficient number of mobile robots can be assumed to be available.

Projekt ViMCoR

Volvo Group (ProjectViMCoR), 2019-09-01 -- 2021-08-31.

Engineering Tool Chain for Efficient and Iterative Development of Smart Factories (ENTOC)

VINNOVA (2016-02716), 2016-09-01 -- 2019-08-31.

EUREKA ITEA3 AIToC

VINNOVA (2020-01947), 2020-10-01 -- 2023-09-30.

Styrkeområden

Produktion

Ämneskategorier

Elektroteknik och elektronik

ISBN

978-91-7905-708-4

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5174

Utgivare

Chalmers

Campus Johanneberg, Hörsalsvägen 14, lecture hall HC2

Online

Opponent: Maria Pia Fanti, System and Control Engineering ,Department of Electrical and Information Engineering, Polytechnic of Bari (Italy)

Mer information

Senast uppdaterat

2022-10-03