Strawberry Fields: Difference between revisions

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Client: Marc Jones, [[Antobot]] <marc.jones@antobot.ai>
Client: Marc Jones, [[Antobot]] <marc.jones@antobot.ai>


 
Strawberry pickers currently spend a significant amount of time (approx. 20%) manually transporting trays to stations, generally at the end of the field. Autonomous logistics robots provide the opportunity to increase productivity by transporting the trays, thus reducing the demand on labour. Your challenge is to create a simulation of a typical harvesting scenario in ROS Gazebo and develop an optimised algorithm for efficient multiple robots path planning. Use this to recommend the minimum number of robots required (e.g. scheduling / logic) – too many robots will be complex and potentially too expensive, too few will create delays for the pickers and reduce efficiency. Based on the simulation results, you should identify required sensor and actuator technologies in order for the robot to optimally interact with human staff.
Strawberry pickers currently spend a significant amount of time (approx. 20%) manually transporting trays to stations generally at the end of the field. Autonomous logistics robots provide the opportunity to increase productivity by transporting the trays, thus reducing the demand on labour and increasing profit margins for the grower. Your challenge is to create a simulation of a typical harvesting scenario in ROS Gazebo and develop an optimised algorithm for efficient multiple robots path planning. Use this to recommend the minimum number of robots required (e.g. scheduling / logic) – too many robots will be complex and potentially too expensive, too few will create delays for the pickers and reduce efficiency. Based on the simulation results, you should identify required sensor and actuator technologies in order for the robot to optimally interact with human staff.

Latest revision as of 19:17, 12 November 2021

Client: Marc Jones, Antobot <marc.jones@antobot.ai>

Strawberry pickers currently spend a significant amount of time (approx. 20%) manually transporting trays to stations, generally at the end of the field. Autonomous logistics robots provide the opportunity to increase productivity by transporting the trays, thus reducing the demand on labour. Your challenge is to create a simulation of a typical harvesting scenario in ROS Gazebo and develop an optimised algorithm for efficient multiple robots path planning. Use this to recommend the minimum number of robots required (e.g. scheduling / logic) – too many robots will be complex and potentially too expensive, too few will create delays for the pickers and reduce efficiency. Based on the simulation results, you should identify required sensor and actuator technologies in order for the robot to optimally interact with human staff.