Model Predictive Control for Crowd Navigation via Learning-Based Trajectory Prediction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915437537656832 |
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| author | Aslam, Mohamed Parvez Derajic, Bojan Bouzidi, Mohamed-Khalil Bernhard, Sebastian Ringert, Jan Oliver |
| author_facet | Aslam, Mohamed Parvez Derajic, Bojan Bouzidi, Mohamed-Khalil Bernhard, Sebastian Ringert, Jan Oliver |
| contents | Safe navigation in pedestrian-rich environments remains a key challenge for autonomous robots. This work evaluates the integration of a deep learning-based Social-Implicit (SI) pedestrian trajectory predictor within a Model Predictive Control (MPC) framework on the physical Continental Corriere robot. Tested across varied pedestrian densities, the SI-MPC system is compared to a traditional Constant Velocity (CV) model in both open-loop prediction and closed-loop navigation. Results show that SI improves trajectory prediction - reducing errors by up to 76% in low-density settings - and enhances safety and motion smoothness in crowded scenes. Moreover, real-world deployment reveals discrepancies between open-loop metrics and closed-loop performance, as the SI model yields broader, more cautious predictions. These findings emphasize the importance of system-level evaluation and highlight the SI-MPC framework's promise for safer, more adaptive navigation in dynamic, human-populated environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07079 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model Predictive Control for Crowd Navigation via Learning-Based Trajectory Prediction Aslam, Mohamed Parvez Derajic, Bojan Bouzidi, Mohamed-Khalil Bernhard, Sebastian Ringert, Jan Oliver Robotics Artificial Intelligence Systems and Control Safe navigation in pedestrian-rich environments remains a key challenge for autonomous robots. This work evaluates the integration of a deep learning-based Social-Implicit (SI) pedestrian trajectory predictor within a Model Predictive Control (MPC) framework on the physical Continental Corriere robot. Tested across varied pedestrian densities, the SI-MPC system is compared to a traditional Constant Velocity (CV) model in both open-loop prediction and closed-loop navigation. Results show that SI improves trajectory prediction - reducing errors by up to 76% in low-density settings - and enhances safety and motion smoothness in crowded scenes. Moreover, real-world deployment reveals discrepancies between open-loop metrics and closed-loop performance, as the SI model yields broader, more cautious predictions. These findings emphasize the importance of system-level evaluation and highlight the SI-MPC framework's promise for safer, more adaptive navigation in dynamic, human-populated environments. |
| title | Model Predictive Control for Crowd Navigation via Learning-Based Trajectory Prediction |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2508.07079 |