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HomeUniversitiesLoughborough UniversityAgricultural Robot Navigation in Complex Farm and Rugged Terrain Environments Using Deep Reinforcement Learning AI. PhD
This project aims to use deep learning with a number of sensors to make sense of the complex environment, by detecting and mapping non traversable areas, followed by reinforcement learning for robot navigation. This would provide enhanced robot navigation and control, to avoid areas and obstacles which would normally not be detected and cause the robot to get stuck. We also aim to use the same Reinforcement Learning techniques in end-to-end control to directly drive the robot’s wheels to enable greater terrain traversable by using a variety of sensor data. These sorts of functions are critical to enabling the sustainable cost-effective use of fully autonomous agricultural robots in agriculture and horticulture, which previously solely rely on GPS and a lot of human intervention to operate. Deep Reinforcement Learning is an exciting and extremely powerful AI tool that is seeing a huge increase in use in several areas. It is a machine learning technique that mimics how humans learn by trial and error, reinforcing actions that lead to a goal and ignoring actions that don't. This will enable us to effectively train a robot to operate in these complex environments much faster than using traditional techniques that previously required a robot to have a more semantic understanding of the environment.
Programme overview
Degree
PhD
Study Level
PHD
Study Mode
On Campus
This project aims to use deep learning with a number of sensors to make sense of the complex environment, by detecting and mapping non traversable areas, followed by reinforcement learning for robot navigation. This would provide enhanced robot navigation and control, to avoid areas and obstacles which would normally not be detected and cause the robot to get stuck. We also aim to use the same Reinforcement Learning techniques in end-to-end control to directly drive the robot’s wheels to enable greater terrain traversable by using a variety of sensor data. These sorts of functions are critical to enabling the sustainable cost-effective use of fully autonomous agricultural robots in agriculture and horticulture, which previously solely rely on GPS and a lot of human intervention to operate. Deep Reinforcement Learning is an exciting and extremely powerful AI tool that is seeing a huge increase in use in several areas. It is a machine learning technique that mimics how humans learn by trial and error, reinforcing actions that lead to a goal and ignoring actions that don't. This will enable us to effectively train a robot to operate in these complex environments much faster than using traditional techniques that previously required a robot to have a more semantic understanding of the environment.
Admission Requirements
3.2+
6.5+
92+
A 2:1 undergraduate degree in a relevant subject. An interest or experience with either robotics, machine learning, agriculture or SLAM would be an advantage.
18 Feb 2025
3 Years
Oct
International
28,600
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Agricultural Robot Navigation in Complex Farm and Rugged Terrain Environments Using Deep Reinforcement Learning AI. PhD
Loughborough University, Loughborough, United Kingdom
36 monthsProgramme duration
28,600 Tuition Fee/year
18 Feb, 2025Application Deadline
Programme overview
Degree
PhD
Study Level
PHD
Study Mode
On Campus
We also aim to use the same Reinforcement Learning techniques in end-to-end control to directly drive the robot’s wheels to enable greater terrain traversable by using a variety of sensor data. These sorts of functions are critical to enabling the sustainable cost-effective use of fully autonomous agricultural robots in agriculture and horticulture, which previously solely rely on GPS and a lot of human intervention to operate.
Deep Reinforcement Learning is an exciting and extremely powerful AI tool that is seeing a huge increase in use in several areas. It is a machine learning technique that mimics how humans learn by trial and error, reinforcing actions that lead to a goal and ignoring actions that don't. This will enable us to effectively train a robot to operate in these complex environments much faster than using traditional techniques that previously required a robot to have a more semantic understanding of the environment.
Programme overview
Degree
PhD
Study Level
PHD
Study Mode
On Campus
We also aim to use the same Reinforcement Learning techniques in end-to-end control to directly drive the robot’s wheels to enable greater terrain traversable by using a variety of sensor data. These sorts of functions are critical to enabling the sustainable cost-effective use of fully autonomous agricultural robots in agriculture and horticulture, which previously solely rely on GPS and a lot of human intervention to operate.
Deep Reinforcement Learning is an exciting and extremely powerful AI tool that is seeing a huge increase in use in several areas. It is a machine learning technique that mimics how humans learn by trial and error, reinforcing actions that lead to a goal and ignoring actions that don't. This will enable us to effectively train a robot to operate in these complex environments much faster than using traditional techniques that previously required a robot to have a more semantic understanding of the environment.
Admission Requirements
International
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Life Sciences and Medicine (6)
Adaptive and Predictive Computational Modelling of Biomaterials: A Digital Twin Framework for Next-Generation Personalised Healthcare PhD
Adaptive and Predictive Computational Modelling of Biomaterials: A Digital Twin Framework for Next-Generation Personalised Healthcare PhD
Biomarkers of Aging PhD
Biomarkers of Aging PhD
Developing Prescription Guidelines for Passive Heat Therapy PhD
Developing Prescription Guidelines for Passive Heat Therapy PhD
Does Socialised Real-Life Body Exposure Promote Body Image Positivity PhD
Does Socialised Real-Life Body Exposure Promote Body Image Positivity PhD
Energy Dissipation In Human Soft Tissue During Impacts PhD
Energy Dissipation In Human Soft Tissue During Impacts PhD
Natural Sciences (6)
Accelerated Screening and Synthesis of New Chemotherapeutic Agents for Pancreatic Cancer PhD
Accelerated Screening and Synthesis of New Chemotherapeutic Agents for Pancreatic Cancer PhD
Bespoke Organometallic Compounds for Anticancer Studies PhD
Bespoke Organometallic Compounds for Anticancer Studies PhD
Bio-based Sustainable Materials for Autonomous Thermal Management in Wearables PhD
Bio-based Sustainable Materials for Autonomous Thermal Management in Wearables PhD
Biomimmetic Catalytic Asymmetric Synthesis of the Bisorbicillinoids and Related Products PhD
Biomimmetic Catalytic Asymmetric Synthesis of the Bisorbicillinoids and Related Products PhD
Climate and Ecological Transitions Hub Studentship PhD
Climate and Ecological Transitions Hub Studentship PhD
Coherent State Methods in Spectral Asymptotics PhD
Coherent State Methods in Spectral Asymptotics PhD
Computational Modelling of Sustainable Alloys PhD
Designing Classroom Tasks To Improve Students’ Learning Of Mathematics PhD
Developing Novel Antibiofilm Coatings for Sustainable Biofilm-Free Surfaces PhD
Development of Hybrid Microcomb Sources with Topologically Protected Dark Resonances for Simultaneous Classical and Quantum PhD
Development of Novel Bicyclopentanes (BCP) Bioisosteres From Bench Stable [1.1.1]-Propellane Precursors PhD
Digital Twins for Biofilm Management PhD
Dispersionless Integrable Systems In 2+1 Dimensions PhD
Social Sciences and Management (6)
An Evaluation of Erasmus+ Sport Projects and Their Impact on EU Sport Policy Development PhD
An Evaluation of Erasmus+ Sport Projects and Their Impact on EU Sport Policy Development PhD
Climate Change, Work Design and Worker Wellbeing Phd
Climate Change, Work Design and Worker Wellbeing Phd
Communication and Social Change (CSC) Hub Studentship PhD
Communication and Social Change (CSC) Hub Studentship PhD
Enhancing Motivation in Sport and Exercise Contexts PhD
Enhancing Motivation in Sport and Exercise Contexts PhD
Out of School Youth Provision: An Analysis of Funding and Operational Resilience within the Sector PhD
Out of School Youth Provision: An Analysis of Funding and Operational Resilience within the Sector PhD