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Fusing Offline and Online Trajectory Optimization Techniques for Goal-to-Goal Navigation of a Scaled Autonomous Vehicle
Technical Paper
2021-01-0097
ISSN: 0148-7191, e-ISSN: 2688-3627
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Event:
SAE WCX Digital Summit
Language:
English
Abstract
Enabling self-driving vehicles to efficiently and autonomously navigate through an obstacle-filled environment remains a topic of significant contemporary research interest. Motion-planning frameworks, encapsulating both path- and trajectory-planning, have played a dominant role in realizing the deployment of a “sense-think-act” intelligence for autonomous vehicles. However, verification and validation of such intelligence on actual self-driving autonomous vehicles has been limited. Simulation-based verification and validation has the advantage of permitting diverse scenario-based testing and comprehensive “what-if” analyses - but is ultimately limited by the simulation fidelity and realism. In contrast, testing on full-scale real-world systems is constrained by the usual challenges of time, space, and cost engendered in reproducing diverse scenarios in practice. Further, motion-planning frameworks often engender a mixture of global-planning (typically performed offline) coupled with a sensor-based local-planning (typically done online), which requires both simulation and physical testing.
Thus, scaled vehicle experimentation provides researchers with an exciting via-media to evaluate the performance and robustness of motion-planning algorithms on actual physical hardware - especially in real-time sensor-based motion planning settings. In this paper, we analyze a 1/10th scale F1/10 vehicle's performance in simulation and the actual hardware. A global planning algorithm is used to provide the waypoints for a feasible collision-free path between the start and goal configurations in the environment. We explored the deployment of Rapidly exploring Random Tree (RRT) and Rapidly exploring Random Tree* (RRT*). The Time Elastic Band local trajectory planner in ROS is then used for the realization of smooth, feasible paths between the waypoints. A comparison of validation in simulation has been provided with a detailed discussion of the parametric tuning for improving each case's performance.
Authors
Citation
Joglekar, A., Deshpande, B., Basuthakur, M., and Krovi, V., "Fusing Offline and Online Trajectory Optimization Techniques for Goal-to-Goal Navigation of a Scaled Autonomous Vehicle," SAE Technical Paper 2021-01-0097, 2021, https://doi.org/10.4271/2021-01-0097.Data Sets - Support Documents
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References
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