An off-policy Bayesian nonparameteric approximate reinforcement learning framework,termed as GPQ,that employs a Gaussian processes(GP)model of the value(Q)function is presented in both the batch and online settings.Su...An off-policy Bayesian nonparameteric approximate reinforcement learning framework,termed as GPQ,that employs a Gaussian processes(GP)model of the value(Q)function is presented in both the batch and online settings.Sufficient conditions on GP hyperparameter selection are established to guarantee convergence of off-policy GPQ in the batch setting,and theoretical and practical extensions are provided for the online case.Empirical results demonstrate GPQ has competitive learning speed in addition to its convergence guarantees and its ability to automatically choose its own bases locations.展开更多
Digital farming is the practice of modern technologies such as sensors,robotics,and data analysis for shifting from tedious operations to continuously automated processes.This paper reviews some of the latest achievem...Digital farming is the practice of modern technologies such as sensors,robotics,and data analysis for shifting from tedious operations to continuously automated processes.This paper reviews some of the latest achievements in agricultural robotics,specifically those that are used for autonomous weed control,field scouting,and harvesting.Object identification,task planning algorithms,digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming.The concepts of multi-robots,human-robot collaboration,and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming.It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information.For the case of robotic harvesting,an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators.While robots are becoming the inseparable parts of the modern farms,our conclusion is that it is not realistic to expect an entirely automated farming system in the future.展开更多
Research efforts for development of agricultural robots that can effectively perform tedious field tasks have grown significantly in the past decade.Agricultural robots are complex systems that require interdisciplina...Research efforts for development of agricultural robots that can effectively perform tedious field tasks have grown significantly in the past decade.Agricultural robots are complex systems that require interdisciplinary collaborations between different research groups for effective task delivery in unstructured crops and plants environments.With the exception of milking robots,the extensive research works that have been carried out in the past two decades for adaptation of robotics in agriculture have not yielded a commercial product to date.To accelerate this pace,simulation approach and evaluation methods in virtual environments can provide an affordable and reliable framework for experimenting with different sensing and acting mechanisms in order to verify the performance functionality of the robot in dynamic scenarios.This paper reviews several professional simulators and custom-built virtual environments that have been used for agricultural robotic applications.The key features and performance efficiency of three selected simulators were also compared.A simulation case study was demonstrated to highlight some of the powerful functionalities of the Virtual Robot Experimentation Platform.Details of the objects and scenes were presented as the proof-of-concept for using a completely simulated robotic platform and sensing systems in a virtual citrus orchard.It was shown that the simulated workspace can provide a configurable and modular prototype robotic system that is capable of adapting to several field conditions and tasks through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment.This review suggests that an open-source software platform for agricultural robotics will significantly accelerate effective collaborations between different research groups for sharing existing workspaces,algorithms,and reusing the materials.展开更多
文摘An off-policy Bayesian nonparameteric approximate reinforcement learning framework,termed as GPQ,that employs a Gaussian processes(GP)model of the value(Q)function is presented in both the batch and online settings.Sufficient conditions on GP hyperparameter selection are established to guarantee convergence of off-policy GPQ in the batch setting,and theoretical and practical extensions are provided for the online case.Empirical results demonstrate GPQ has competitive learning speed in addition to its convergence guarantees and its ability to automatically choose its own bases locations.
文摘Digital farming is the practice of modern technologies such as sensors,robotics,and data analysis for shifting from tedious operations to continuously automated processes.This paper reviews some of the latest achievements in agricultural robotics,specifically those that are used for autonomous weed control,field scouting,and harvesting.Object identification,task planning algorithms,digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming.The concepts of multi-robots,human-robot collaboration,and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming.It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information.For the case of robotic harvesting,an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators.While robots are becoming the inseparable parts of the modern farms,our conclusion is that it is not realistic to expect an entirely automated farming system in the future.
文摘Research efforts for development of agricultural robots that can effectively perform tedious field tasks have grown significantly in the past decade.Agricultural robots are complex systems that require interdisciplinary collaborations between different research groups for effective task delivery in unstructured crops and plants environments.With the exception of milking robots,the extensive research works that have been carried out in the past two decades for adaptation of robotics in agriculture have not yielded a commercial product to date.To accelerate this pace,simulation approach and evaluation methods in virtual environments can provide an affordable and reliable framework for experimenting with different sensing and acting mechanisms in order to verify the performance functionality of the robot in dynamic scenarios.This paper reviews several professional simulators and custom-built virtual environments that have been used for agricultural robotic applications.The key features and performance efficiency of three selected simulators were also compared.A simulation case study was demonstrated to highlight some of the powerful functionalities of the Virtual Robot Experimentation Platform.Details of the objects and scenes were presented as the proof-of-concept for using a completely simulated robotic platform and sensing systems in a virtual citrus orchard.It was shown that the simulated workspace can provide a configurable and modular prototype robotic system that is capable of adapting to several field conditions and tasks through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment.This review suggests that an open-source software platform for agricultural robotics will significantly accelerate effective collaborations between different research groups for sharing existing workspaces,algorithms,and reusing the materials.
