Fusing Offline and Online Trajectory Optimization Techniques for Goal-to-Goal Navigation of a Scaled Autonomous Vehicle
- Delivery
- Provide download link
- Format
- Price
- Non-members (tax incl.)：￥6,600 Members (tax incl.)：￥5,280
- Paper/Info type
- SAE Paper
No.2021-01-0097
- Pages
- 1-10(Total 10 p)
- Date of publication
- Apr 2021
- Publisher
- SAE International
- Language
- English
- Event
- SAE WCX Digital Summit 2021
Detailed Information
| Author(E) | 1) Ajinkya Joglekar, 2) Bhooshan Deshpande, 3) Mugdha Basuthakur, 4) Venkat N Krovi |
|---|---|
| Affiliation(E) | 1) Clemson University, 2) Clemson University, 3) Clemson University, 4) Clemson University |
| Abstract(E) | 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. |
