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An Efficient Environment Encoding for Trajectory Feasibility and Cost Predictions


Zou, Sarah Jin (2022) An Efficient Environment Encoding for Trajectory Feasibility and Cost Predictions. Senior thesis (Major), California Institute of Technology. doi:10.7907/605s-5h91.


An efficient method to encoding a robot’s surrounding is important for the safety and scalability of path planning problems. I present a method for encoding occupied spaces in an environment via parametric geometry (e.g. circles). This encoding is utilized to train a cost and feasibility predicting neural network for path planning between two states. The circle encoding method uses either bounding circles or set cover to provide a lower dimension input of environments for neural network. The lower input dimension of the models allows for a smaller and faster model of similar performance. Feasibility and cost prediction neural networks enable lazy planning for trajectory calculation. While there are no safety-guarantees, the model I present can act as an heuristic to identify the best paths, amortizing a robot’s onboard computation. To train a cost and feasibility predicting model, I first generate multiple environments and calculate encoding circles for obstacle-occupied spaces. For each environment, I use planners to calculate multiple trajectories and store their feasibility and cost. I then train the feasibility and cost prediction models from this data . The feasibility model has 90% accuracy on train and test environments. The circle encoding method was compared with occupancy grid encoding method. When encoding methods were compared to predict occupied space, models with circle encoding generalized to unseen environments better than occupancy grid input models. The method of circle encoding I introduce can be utilized in other learning problems that use environment as input. The feasibility and cost predictions done in this work can be applied to bias tree growth or to other global planning methods.

Item Type:Thesis (Senior thesis (Major))
Subject Keywords:machine learning, embedding methods, trajectory optimization
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Electrical Engineering
Minor Option:Information and Data Sciences
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Yue, Yisong
Thesis Committee:
  • None, None
Defense Date:3 June 2022
Record Number:CaltechTHESIS:12142023-183159114
Persistent URL:
Zou, Sarah Jin0009-0007-5275-9739
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:16266
Deposited By: Kathy Johnson
Deposited On:14 Dec 2023 19:36
Last Modified:14 Dec 2023 19:36

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