EPSRC Reference: |
GR/S31402/01 |
Title: |
Modelling and Estimation of Vehicle-Terrain Interactions for Autonomous Unmanned Ground Vehicles |
Principal Investigator: |
Seneviratne, Professor LD |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
Mechanical Engineering |
Organisation: |
Kings College London |
Scheme: |
Standard Research (Pre-FEC) |
Starts: |
01 November 2003 |
Ends: |
30 April 2007 |
Value (£): |
322,447
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EPSRC Research Topic Classifications: |
Control Engineering |
Robotics & Autonomy |
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EPSRC Industrial Sector Classifications: |
Aerospace, Defence and Marine |
Construction |
No relevance to Underpinning Sectors |
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Related Grants: |
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Panel History: |
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Summary on Grant Application Form |
Existing unmanned ground vehicles (UGVs) suffer from an inability to cope with variable terrains, resulting in loss of autonomy during task execution. This proposal investigates the interaction dynamics between a UGV and unstructured outdoor terrain. UGV-terrain interaction dynamics are vitally important for UGV mobility. A real-time model for predicting UGV-terrain interaction dynamic forces has many potential applications, including traversability prediction, trajectory planning and traction control. The focus of the research is to develop a physics based model for the vehicle-terrain interaction dynamics. The model will be used to estimate the vehicle slip and terrain parameters in real-time, based on simple sensor feedback. The research will result in a generic approach for UGVs to identify slip and terrain parameters. The terrain and slip parameter estimation poses challenging research problems, particularly in unstructured terrains. Previous research on UGV-terrain interactions has generally focused on special cases, and real-time models and estimators for outdoor environments have yet to be developed. This research has great potential to benefit industries such as defence, agriculture and construction. The proposed research is multi-disciplinary with a core activity in mechatronics involving modelling, parameter estimation and embedded machine intelligence.
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Key Findings |
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Potential use in non-academic contexts |
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Impacts |
Description |
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Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
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Project URL: |
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Further Information: |
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Organisation Website: |
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