Collaborative Perception in Multi-Robot Systems: Case Studies in Household Cleaning and Warehouse Operations
This work addresses the challenge of limited perception in multi-robot systems for applications like cleaning and logistics, but it is incremental as it builds on existing CP concepts with specific case studies.
This paper tackles the problem of improving multi-robot perception by introducing a Collaborative Perception (CP) framework that aggregates sensor data from multiple robots, and it demonstrates enhanced task efficiency and coordination in household cleaning and warehouse operations through case studies.
This paper explores the paradigm of Collaborative Perception (CP), where multiple robots and sensors in the environment share and integrate sensor data to construct a comprehensive representation of the surroundings. By aggregating data from various sensors and utilizing advanced algorithms, the collaborative perception framework improves task efficiency, coverage, and safety. Two case studies are presented to showcase the benefits of collaborative perception in multi-robot systems. The first case study illustrates the benefits and advantages of using CP for the task of household cleaning with a team of cleaning robots. The second case study performs a comparative analysis of the performance of CP versus Standalone Perception (SP) for Autonomous Mobile Robots operating in a warehouse environment. The case studies validate the effectiveness of CP in enhancing multi-robot coordination, task completion, and overall system performance and its potential to impact operations in other applications as well. Future investigations will focus on optimizing the framework and validating its performance through empirical testing.