US 2026/0037883 A1 · METHOD, APPARATUS AND COMPUTER PROGRAM PRODUCT FOR LOGISTICS ROBOT DEPLOYMENT

Abstract

Method, apparatuses and computer program products for training machine learning models for logistics robots and routing the robots are disclosed. A method of training an ML model involves providing a route to a logistics robot, obtaining route issue indications from the robot when it fails to traverse the route as expected, and associating map objects with the issue locations. The trained model can then be used to determine the likelihood of route issues for a specific logistics robot type based on the presence of certain map objects along the route. The disclosure further involves calculating route penalty value(s) based on the likelihood and updating the route accordingly, including selection of a logistics robot type for the route.

Claims20

Dependent claims

A computer-implemented method for training a machine learning (ML) model for logistics robots, the method comprising:

providing a route to a logistics robot, wherein the route comprises a start location, and a destination location, wherein the route traverses, at least in part, a mapped public environment;
obtaining, from the logistics robot, a route issue indication, wherein the route issue indication comprises a route issue location for a route issue, and wherein the route issue causes the logistics robot to fail to traverse the provided route as expected;
obtaining, from a map database, at least one map object corresponding to the route issue location;
and
training the ML model to associate the map object with the route issue indication.

2 more elements

A computer-implemented method for routing a logistics robot, the method comprising:

obtaining a route for a logistics robot, wherein the route comprises a start location, and a destination location, wherein the route traverses, at least in part, a mapped public environment;
retrieving, from a map database, at least one map object located on or adjacent to the route;
determining for a first logistics robot type, using a machine learning (ML) model trained by associating map objects to route issue indications, a first route issue indication likelihood associated with at least one map object, wherein the ML model has been trained at least for the first logistics robot type;
calculating, based on the first route issue indication likelihood, a first route penalty value for a portion of the route where the map object is located on or adjacent to;
and,
updating the route based on the calculated first route penalty value.

3 more elements

An apparatus comprising:

a processor;
and
a memory including computer program code for a program,
the memory and the computer program code configured to, with the processor, cause the apparatus to:
obtain a route for a logistics robot, wherein the route comprises a start location, and a destination location, wherein the route traverses, at least in part, a mapped public environment;
retrieve, from a map database, at least one map object located on or adjacent to the route;
determine for a first logistics robot type, using a machine learning (ML) model trained by associating map objects to route issue indications, a first route issue indication likelihood associated with at least one map object, wherein the ML model has been trained at least for the first logistics robot type;
calculate, based on the first route issue indication likelihood, a first route penalty value for a portion of the route where the map object is located on or adjacent to;
and,
update the route based on the calculated first route penalty value.

7 more elements

Dependencies are inferred from the claim text.

Method, apparatus and computer program product for logistics robot deployment

Life of this patent

Method, apparatus and computer program product for logistics robot deployment

Life of this patent

Abstract

Claims20