Collaborative Payload Robot Navigation in Crowded Environments

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Solution Overview

Problem

Robotic systems are limited in executing complex tasks in unstructured environments due to limitations in physical and intellectual capabilities and communication of task parameters, restricting their adaptability and functionality in everyday human life.

Innovation Solution

A collaborative robotic system equipped with a payload platform, load sensors, proximity sensors, and a computing system that infers navigational cues from human collaborators, allowing it to navigate in crowded environments while sharing a payload and overriding human cues to avoid collisions, thereby enhancing its ability to execute complex tasks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If robotic systems use traditional control interfaces to execute tasks, then task execution is simple and reliable, but task complexity and adaptability are limited

Engineering Contradiction:

Improvetask complexityVSAvoidcommunication interface

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical control interfaces (buttons, switches, manual programming) with natural user interface technologies including speech recognition and gesture recognition. This allows users to communicate complex task parameters and constraints to the robotic system using natural language and body movements, significantly increasing task complexity handling without proportionally increasing interface complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces natural language processing and computer vision algorithms as intermediary layers between the user and the robotic control system. These intermediaries translate speech and gestures into structured task commands, enabling complex task execution while maintaining a simple user interface

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If robotic systems operate autonomously in unstructured environments, then productivity increases, but reliability decreases due to limited intellectual capability

Engineering Contradiction:

Improvetask execution speedVSAvoidtask execution accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent merges human cognitive capabilities with robotic physical execution capabilities in a collaborative framework. The human partner provides high-level task planning, decision-making, and adaptability, while the robotic system handles repetitive physical tasks with precision and speed. This combination achieves both high productivity and high reliability in unstructured environments

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent implements a dynamic task execution model where the robotic system can switch between autonomous execution for routine tasks and human-guided execution for complex or uncertain situations. This dynamic adjustment of autonomy level optimizes both productivity and reliability based on task requirements

Inventive Principle:

Principle #15Dynamics

3Speed

If robotic systems navigate crowded environments independently, then speed increases, but harmful factors increase due to collision risks

Engineering Contradiction:

Improvenavigation speedVSAvoidcollision risk

Core Design Contradiction:

SpeedVSObject-affected harmful factors

Solution Approach 1:

The patent implements real-time feedback loops using proximity sensors, depth cameras, and other sensing technologies to continuously monitor the robotic system's environment. This feedback enables dynamic adjustment of navigation speed and path planning to avoid collisions with humans and objects, maintaining high speed while minimizing collision risks in crowded environments

Inventive Principle:

Principle #23Feedback

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables the robotic system to effectively navigate and execute complex tasks in unstructured environments by leveraging human adaptability and sensor data, overcoming previous limitations in task complexity and environmental adaptability.

Implementation Method 1

load sensors of a collaborative robotic system measure forces in a plane parallel to the loading surface of the payload platform

Methodology Applied

Scientific EffectForce measurement: Force

Implementation Method 2

one or more proximity sensors configured to estimate the proximity of objects to the robotic system

Methodology Applied

Scientific EffectProximity sensing:

Data Source

PatentUS11548166B2Collaborative task execution with humans and robotic vehicles

Publication Date: 2023.01.10 SKYLLA TECHNOLOGIES INC

AI summary

Methods and systems for joint execution of complex tasks by a human and a robotic system are described herein. In one aspect, a collaborative robotic system includes a payload platform having a loading surface configured to carry a payload shared with a human collaborator. The collaborative robotic system navigates a crowded environment, while sharing a payload with the human collaborator. In another aspect, the collaborative robotic system measures forces in a plane parallel to the loading surface of the payload platform to infer navigational cues from the human collaborator. In some instances, the collaborative robotic system overrides the navigational cues of the human collaborator to avoid collisions between an object in the environment and any of the robotic system, the human collaborator, and the shared payload.