Online Prediction for Vision-Based Active Pursuit Using a Domain Agnostic Offline Motion Model
Description
In a pursuit-evasion setup where one group of agents tracks down another adversarial group, vision-based algorithms have been known to make use of techniques such as Linear Dynamic Estimation to determine the probable future location of an evader in a given environment. This helps a pursuer attain an edge over the evader that has conventionally benefited from the uncertainty of the pursuit. The pursuer can utilize this knowledge to enable a faster capture of the evader, as opposed to a pursuer that only knows the evader's current location. Inspired by the function of dorsal anterior cingulate cortex (dACC) neurons in natural predators, the use of a predictive model that is built using an encoder-decoder Long Short-Term Memory (LSTM) Network and can produce a more accurate estimate of the evader's future location is proposed. This enables an even quicker capture of a target when compared to previously used filtering-based methods. The effectiveness of the approach is evaluated by setting up these agents in an environment based in the Modular Open Robots Simulation Engine (MORSE). Cross-domain adaptability of the method, without the explicit need to retrain the prediction model is demonstrated by evaluating it in another domain.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2021
Agent
- Author (aut): Godbole, Sumedh
- Thesis advisor (ths): Yang, Yezhou
- Committee member: Srivastava, Siddharth
- Committee member: Zhang, Wenlong
- Publisher (pbl): Arizona State University