Robot Accessory Scheduling for Multi-Service Mobile Operations

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Solution Overview

Problem

Existing autonomous mobile robots are limited to executing only transport services and lack the capability to perform a variety of services without additional accessory units.

Innovation Solution

A management system and method that acquires information about accessory units and services, sets an operation schedule for autonomous mobile robots and accessory units to execute multiple services, and outputs a schedule for efficient service execution.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If accessory units are integrated with autonomous mobile robots, then service versatility is improved, but system complexity increases

Engineering Contradiction:
Improveservice versatilityVSAvoidsystem complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system divides the robot fleet into multiple groups based on service types, with each group associated with specific accessory units. The management server independently schedules each group, preventing the need to manage all components as a single complex system while still enabling versatile service execution through coordinated groups.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The management server implements a universal scheduling mechanism that handles multiple service types (transport, delivery, food service) through a single platform. This allows the system to manage diverse robot-accessory combinations without requiring separate management systems for each service type, thereby reducing overall system complexity.

Inventive Principle:
Principle #6Universality (Multi-functionality)

2Productivity

If multiple services are scheduled for autonomous mobile robots, then productivity is improved, but scheduling complexity increases

Engineering Contradiction:
Improveservice efficiencyVSAvoidscheduling complexity
Core Design Contradiction:
ProductivityVSDevice complexity

Solution Approach 1:

The scheduling process is segmented into service-type-specific schedules, where the management server creates independent schedules for transport services, delivery services, and food services. This segmentation allows efficient multi-service scheduling without overwhelming complexity, as each schedule can be optimized independently according to its specific requirements.

Inventive Principle:
Principle #1Segmentation

3Adaptability or versatility

If accessory units are assigned to autonomous mobile robots, then service capability is improved, but coordination difficulty increases

Engineering Contradiction:
Improveservice capabilityVSAvoidcoordination difficulty
Core Design Contradiction:
Adaptability or versatilityVSEase of operation

Solution Approach 1:

The management server merges the scheduling of robots and accessory units into a unified process. By considering robot capabilities and accessory unit characteristics together when creating schedules, the system automatically coordinates their operations, eliminating the need for separate coordination processes and reducing operational difficulty.

Inventive Principle:
Principle #5Merging (Combining)

Data Source

PatentUS20250348803A1Management system and management method
Publication Date: 2025.11.13 TOYOTA JIDOSHA KK
  • US20250348803A1 patent drawing
  • US20250348803A1 patent drawing
  • US20250348803A1 patent drawing

AI summary

The management system according to the present embodiment acquires unit information on a plurality of accessory units that enable the autonomous mobile robot to execute a plurality of different services by being used in combination with the autonomous mobile robot, acquires service information on a plurality of types of services to be executed by the autonomous mobile robot, and sets an operation schedule of the autonomous mobile robot and the plurality of accessory units with reference to the unit information so as to execute a plurality of types of services based on the service information, and outputs output information indicating the operation schedule. A machine learning model such as deep learning may be used for controlling the robot and setting the schedule.