Multi-AAV Cooperative Path Planning using Nonlinear Model Predictive Control with Localization Constraints
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866911990884073472 |
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| author | Manoharan, Amith Sharma, Rajnikanth Sujit, P. B. |
| author_facet | Manoharan, Amith Sharma, Rajnikanth Sujit, P. B. |
| contents | In this paper, we solve a joint cooperative localization and path planning problem for a group of Autonomous Aerial Vehicles (AAVs) in GPS-denied areas using nonlinear model predictive control (NMPC). A moving horizon estimator (MHE) is used to estimate the vehicle states with the help of relative bearing information to known landmarks and other vehicles. The goal of the NMPC is to devise optimal paths for each vehicle between a given source and destination while maintaining desired localization accuracy. Estimating localization covariance in the NMPC is computationally intensive, hence we develop an approximate analytical closed form expression based on the relationship between covariance and path lengths to landmarks. Using this expression while computing NMPC commands reduces the computational complexity significantly. We present numerical simulations to validate the proposed approach for different numbers of vehicles and landmark configurations. We also compare the results with EKF-based estimation to show the superiority of the proposed closed form approach. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2201_09285 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Multi-AAV Cooperative Path Planning using Nonlinear Model Predictive Control with Localization Constraints Manoharan, Amith Sharma, Rajnikanth Sujit, P. B. Robotics Systems and Control In this paper, we solve a joint cooperative localization and path planning problem for a group of Autonomous Aerial Vehicles (AAVs) in GPS-denied areas using nonlinear model predictive control (NMPC). A moving horizon estimator (MHE) is used to estimate the vehicle states with the help of relative bearing information to known landmarks and other vehicles. The goal of the NMPC is to devise optimal paths for each vehicle between a given source and destination while maintaining desired localization accuracy. Estimating localization covariance in the NMPC is computationally intensive, hence we develop an approximate analytical closed form expression based on the relationship between covariance and path lengths to landmarks. Using this expression while computing NMPC commands reduces the computational complexity significantly. We present numerical simulations to validate the proposed approach for different numbers of vehicles and landmark configurations. We also compare the results with EKF-based estimation to show the superiority of the proposed closed form approach. |
| title | Multi-AAV Cooperative Path Planning using Nonlinear Model Predictive Control with Localization Constraints |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2201.09285 |