Adversarial Sampling-Based Motion Planning

Journal Article · Mon Feb 07 00:00:00 EST 2022 · IEEE Robotics and Automation Letters

DOI:https://doi.org/10.1109/LRA.2022.3148464· OSTI ID:1845392

Nichols, Hayden ^[1]; Jimenez, Mark ^[2]; Goddard, Zachary ^[1]; Sparapany, Michael ^[3]; Boots, Byron ^[4]; Mazumdar, Anirban ^[1]

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1. Georgia Institute of Technology, Atlanta, GA (United States)
2. Univ. of Hawaii, Hilo, HI (United States)
3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
4. Univ. of Washington, Seattle, WA (United States)

In this report there are many scenarios in which a mobile agent may not want its path to be predictable. Examples include preserving privacy or confusing an adversary. However, this desire for deception can conflict with the need for a low path cost. Optimal plans such as those produced by RRT* may have low path cost, but their optimality makes them predictable. Similarly, a deceptive path that features numerous zig-zags may take too long to reach the goal. We address this trade-off by drawing inspiration from adversarial machine learning. We propose a new planning algorithm, which we title Adversarial RRT*. Adversarial RRT* attempts to deceive machine learning classifiers by incorporating a predicted measure of deception into the planner cost function. Adversarial RRT* considers both path cost and a measure of predicted deceptiveness in order to produce a trajectory with low path cost that still has deceptive properties. We demonstrate the performance of Adversarial RRT*, with two measures of deception, using a simulated Dubins vehicle. We show how Adversarial RRT* can decrease cumulative RNN accuracy across paths to 10%, compared to 46% cumulative accuracy on near-optimal RRT* paths, while keeping path length within 16% of optimal. We also present an example demonstration where the Adversarial RRT* planner attempts to safely deliver a high value package while an adversary observes the path and tries to intercept the package.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0003525
• OSTI ID: 1845392
• Report Number(s): SAND2022-0910J; 703237
• Journal Information: IEEE Robotics and Automation Letters, Journal Name: IEEE Robotics and Automation Letters Journal Issue: 2 Vol. 7; ISSN 2377-3766
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
deep learning methods
integrated planning and learning
motion and path planning