Stress Testing Autonomous Racing Overtake Maneuvers with RRT

Bak, Stanley; Betz, Johannes; Chawla, Abhinav; Zheng, Hongrui; Mangharam, Rahul

doi:10.1109/iv51971.2022.9827237

Cited by 7 publications

(1 citation statement)

References 33 publications

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“…In the presence of external agents, ensuring safety without sacrificing performance becomes extremely challenging. Bak et al [37] presented an approach to stress test autonomous systems using the RRT algorithm. Their approach generates scenarios that test the robustness and safety of the system by introducing unexpected changes to the environment or system behavior.…”

Section: Introductionmentioning

confidence: 99%

Rapidly Exploring Random Trees for Autonomous Navigation in Observable and Uncertain Environments

Sarmiento¹,

Jacinto²,

Ariza³

2023

IJACSA

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This paper proposes the use of a small differential robot with two DC motors, controlled by an ESP32 microcontroller, that implements the Rapidly Exploring Random Trees algorithm to navigate from an origin point to a destination point in an unknown but observable environment. The motivation behind this research is to explore the use of a low-cost, versatile and efficient robotic platform for autonomous navigation in complex environments. This work presents a practical and costeffective solution that can be easily replicated and implemented in various scenarios such as search and rescue, surveillance, and industrial automation. The proposed robotic platform is equipped with a set of sensors and actuators that allow it to observe the environment, estimate its position, and move through it. The Rapidly Exploring Random Trees algorithm is implemented to generate a path from an origin to a destination point, avoiding obstacles and adjusting the robot's motion accordingly. The implementation of this algorithm enables the robot to navigate through complex environments with high efficiency and reliability, making it a suitable solution for a wide range of applications. The results obtained through simulations and experiments show that the proposed robotic platform and algorithm achieve high performance and accuracy in autonomous navigation, even in complex environments.

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Section: Introductionmentioning

confidence: 99%