EP-4524678-B1 - ROBOT LOCAL PLANNER SELECTION

EP4524678B1EP 4524678 B1EP4524678 B1EP 4524678B1EP-4524678-B1

Inventors

ANDREWS, DANIEL
Lee, Jeongran
SHARMA, VISHNU

Dates

Publication Date: 20260513
Application Date: 20240911

Claims (13)

A method for navigating a robot (100), the method comprising: generating a set of next consecutive waypoints; determining a local planner based on the set of next consecutive waypoints; outputting a velocity pair for navigating the robot, based on the determined local planner; characterised by determining a plurality of path clearance statuses based on the set of next consecutive waypoints: filtering the plurality of path clearance statuses, and wherein the determining the local planner is also based on the filtered path clearance statuses.
The method of claim 1, further comprising: generating a local cost map, and wherein the determining the local planner is further based on the local cost map.
The method of claim 1, wherein the determining the local planner includes: determining to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determining to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear.
The method of claim 3, further comprising: generating an approximate path based on a global path and the set of next consecutive waypoints; and determining whether all consecutive waypoints in the set of next consecutive waypoints are clear in response to no waypoint in the set of next consecutive waypoints being in a same position as an obstacle, based on a local cost map.
The method of claim 1, wherein the generating the set of next consecutive waypoints includes: discretizing a global path to generate discrete waypoints; and choosing, as the set of next consecutive waypoints, a number of next discrete waypoints from a current position of the robot (100) on the global path.
The method of claim 5, wherein the choosing includes: determining, among the discrete waypoints, a first waypoint, after a closest waypoint to the current position of the robot (100); and wherein the choosing chooses the number of next discrete waypoints starting from the first waypoint.
A mobile robot (100) comprising: at least one processor (101); and at least one memory (102) storing instructions that, when executed by the at least one processor, cause the mobile robot to generate a set of next consecutive waypoints, determine a local planner based on the set of next consecutive waypoints, output a velocity pair for navigating the mobile robot, based on the determined local planner; characterised in that the robot (100) is caused to determine a plurality of path clearance statuses based on the set of next consecutive waypoints; filter the plurality of path clearance statuses; and determine the local planner based also on the filtered path clearance statuses.
The mobile robot (100) of claim 7, wherein the at least one memory (102) stores instructions that, when executed by the at least one processor (101), cause the mobile robot to: generate a local cost map; and determine the local planner further based on the local cost map.
The mobile robot (100) of claim 7, wherein the at least one memory (102) stores instructions that, when executed by the at least one processor (101), cause the mobile robot to: determine to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determine to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear.
The mobile robot (100) of claim 9, wherein the at least one memory (102) stores instructions that, when executed by the at least one processor (101), cause the mobile robot to: generate an approximate path based on a global path and the next consecutive waypoints; and determine whether all consecutive waypoints in the set of next consecutive waypoints are clear in response to no waypoint in the set of next consecutive waypoints being in a same position as an obstacle, based on a local cost map.
The mobile robot (100) of claim 7, wherein the at least one memory (102) stores instructions that, when executed by the at least one processor (101), cause the mobile robot to: discretize a global path to generate discrete waypoints; and choose, as the set of next consecutive waypoints, a number of next discrete waypoints from a current position of the robot on the global path.
The mobile robot (100) of claim 11, wherein the at least one memory (102) stores instructions that, when executed by the at least one processor (101), cause the apparatus to: determine, among the discrete waypoints, a first waypoint, after a closest waypoint to the current position of the robot; and choose the number of next discrete waypoints starting from the first waypoint.
A non-transitory computer readable storage medium storing computer executable instructions that, when executed at a mobile robot (100), cause the mobile robot to perform a method for navigating the mobile robot, the method comprising: generating a set of next consecutive waypoints; determining a local planner based on the set of next consecutive waypoints; outputting a velocity pair for navigating the robot, based on the determined local planner; characterised by determining to use as the local planner a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determining to use as the local planner a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear.

Description

TECHNICAL FIELD One or more example embodiments relate to autonomous robot control. BACKGROUND The Robot Operating System (ROS) is the most popular framework for robot control. In ROS, navigation has two main components, the global planner and the local planner. Classical (or traditional) and learning-based approaches lie at the extremes of local planners for mobile robots. While classical planners are more robust and stable, learning-based planners can be more efficient in challenging situations. SUMMARY The scope of protection sought for various example embodiments is set out by the independent claims. The example embodiments and/or features, if any, described in this specification that do not fall under the scope of the independent claims are to be interpreted as examples useful for understanding various embodiments. At least one example embodiment provides a mobile robot configured to dynamically determine to use a traditional local planner or a reinforcement learning local planner according to current nearby obstacles. At least one example embodiment provides a method for navigating a robot, the method including generating a set of next consecutive waypoints, determining a local planner based on the set of next consecutive waypoints, and outputting a velocity pair for navigating the robot, based on the determined local planner. According to at least one example embodiment, the method further includes generating a local cost map. The determining the local planner is further based on the local cost map. According to at least one example embodiment, the method further includes determining a plurality of path clearance statuses based on the set of next consecutive waypoints, and filtering the plurality of path clearance statuses. The determining the local planner is based on the filtered path clearance statuses. According to at least one example embodiment, the determining the local planner includes determining whether to use a traditional local planner or a reinforcement learning (RL) local planner. According to at least one example embodiment, the determining the local planner includes determining to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear, and determining to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear. According to at least one example embodiment, the method further includes generating an approximate path based on a global path and the set of next consecutive waypoints, and determining whether all consecutive waypoints in the set of next consecutive waypoints are clear in response to no waypoint in the set of next consecutive waypoints being in a same position as an obstacle, based on a local cost map. According to at least one example embodiment, the generating the set of next consecutive waypoints includes discretizing a global path to generate discrete waypoints, and choosing, as the set of next consecutive waypoints, a number of next discrete waypoints from a current position of the robot on the global path. According to at least one example embodiment, the choosing includes determining, among the discrete waypoints, a first waypoint, after a closest waypoint to the current position of the robot. The choosing chooses the number of next discrete waypoints starting from the first waypoint. At least one example embodiment provides a mobile robot including at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the mobile robot to generate a set of next consecutive waypoints, determine a local planner based on the set of next consecutive waypoints, and output a velocity pair for navigating the mobile robot, based on the determined local planner. According to at least one example embodiment, the at least one memory stores instructions that, when executed by the at least one processor, cause the mobile robot to generate a local cost map, and determine the local planner further based on the local cost map. According to at least one example embodiment, the at least one memory stores instructions that, when executed by the at least one processor, cause the mobile robot to determine a plurality of path clearance statuses based on the set of next consecutive waypoints, filter the plurality of path clearance statuses, and determine the local planner based on the filtered path clearance statuses. According to at least one example embodiment, the at least one memory stores instructions that, when executed by the at least one processor, cause the mobile robot to determine whether to use a traditional local planner or a reinforcement learning (RL) local planner. According to at least one example embodiment, the at least one memory stores instructions that, when executed by the at least one processor, cause the mobile robot to determine to use a tradition