Cloud Robot Trajectory Control Using Cached Web Services
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud robotics frameworks, particularly following the PaaS approach, lack versatility in robot service and control, and existing trajectory or path generation methods do not utilize Web services or Web-storing mechanisms like caching, limiting scalability and efficiency in robot motion control.
Innovation Solution
The proposed solution utilizes a Web service infrastructure for scalable robot control from the cloud, employing trajectory planning and calculation methods that leverage caching and content delivery networks (CDNs) to efficiently store and execute trajectories, allowing immediate robot movement and optimizing precision and response time, even in complex environments with multiple robots and moving objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trajectory calculation is performed in real-time for every robot movement request, then movement precision can be maintained, but calculation latency increases and system response time deteriorates
Solution Approach 1:
The system pre-calculates and stores trajectories in a database before they are needed. When a robot movement request is received, the system queries the database for pre-computed trajectories matching the current state and target position, rather than calculating from scratch. This preliminary action significantly reduces calculation latency while maintaining trajectory precision.
Solution Approach 2:
The system changes the parameter of trajectory calculation from real-time computation to pre-computation with cached storage. By transforming the calculation timing parameter and utilizing database storage, the system achieves both high precision and low latency through efficient query retrieval of pre-calculated trajectories.
2Adaptability or versatility
If cloud-based trajectory calculation is implemented to enable remote robot control, then control versatility and accessibility improve, but network dependency increases and control reliability may deteriorate
Solution Approach 1:
The system introduces a local edge server as an intermediary between the cloud and the robot. The edge server caches trajectories locally and can serve robot control requests even when cloud connectivity is interrupted. This intermediary maintains control versatility through cloud integration while ensuring reliability through local fallback capability.
3Ease of manufacture
If Web service infrastructure is used for robot control to leverage existing protocols and scalability, then ease of integration and scalability improve, but control precision and real-time performance may worsen
Solution Approach 1:
The system segments the control architecture into distinct layers: Web service interface layer for ease of integration, edge computing layer for real-time processing, and robot control layer for precision execution. Each layer handles specific functions, allowing the Web service infrastructure to provide scalability while the lower layers ensure control precision through optimized local processing.
Data Source
AI summary
The present disclosure relates to a first Web server (102, 204, 60, 70) and a second Web server (108, 214, 80, 90), and methods therein for controlling of a robot device over a cloud interface. A hyper-text transfer protocol, HTTP, request for a trajectory between a start position and a goal position is sent (S120, S230, 302, 402) towards the second Web server. One or more calculated trajectories are obtained (S122, 304) based on information as received encoded in the request. A HTTP response is sent (306) towards the first WEB server, comprising one or more calculated trajectories. Executing (S126, S266; 308, 406) of a trajectory at least based said one or more of the received trajectories is performed by the first Web server (102, 204, 60, 70). A scalable robot device control method is thus proposed, which is advantageously uses stored calculated trajectories between start and goal positions, for the robot device.