Essays about: "safe trees"

Showing result 1 - 5 of 28 essays containing the words safe trees.

  1. 1. Improving Behavior Trees that Use Reinforcement Learning with Control Barrier Functions : Modular, Learned, and Converging Control through Constraining a Learning Agent to Uphold Previously Achieved Sub Goals

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Jannik Wagner; [2023]
    Keywords : Behavior Trees; Reinforcement Learning; Control Barrier Functions; Robotics; Artificial Intelligence; Verhaltensbäume; Verstärkendes Lernen; Kontrollbarrierefunktionen; Robotik; Künstliche Intelligenz; Beteendeträd; Förstärkningsinlärning; Kontrollbarriärfunktioner; Robotik; Artificiell Intelligens;

    Abstract : This thesis investigates combining learning action nodes in behavior trees with control barrier functions based on the extended active constraint conditions of the nodes and whether the approach improves the performance, in terms of training time and policy quality, compared to a purely learning-based approach. Behavior trees combine several behaviors, called action nodes, into one behavior by switching between them based on the current state. READ MORE

  3. 2. What is a “good” forest? : a case study from Nepal to understand local women’s values for people-centred restoration

    University essay from SLU/Dept. of Urban and Rural Development

    Author : Tina Jahn; [2023]
    Keywords : people-centred restoration; forest restoration; community-based forest management; community forestry; social inclusion; gender inclusion; equity; livelihood benefits; participation;

    Abstract : Restoration is seen as a key strategy to counteract global issues of the climate crisis, deforestation, and land degradation. However, restoration initiatives are being criticised for failing to consider social and political dimensions, leading to negative effects on ecosystems and people. READ MORE

  4. 3. Estimating the load weight of freight trains using machine learning

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Erik Kongpachith; [2023]
    Keywords : Railway freight Transport; Rail Vehicle Weighing; Y25 Bogie; Sdggmrss T3000eD; GENSYS; Machine Learning; Regression; Polynomial Regression; Regression Trees; Random Forest Regression; Support Vector Regression; Järnvägsgods transport; Vägning av järnvägsfordon; Y25 Bogie; Sdggmrss T3000eD; GENSYS; Maskininlärning; Regression; Polynom Regression; Regressionsträd; Random Forest Regression; Support Vector Regression;

    Abstract : Accurate estimation of the load weight of freight trains is crucial for ensuring safe, efficient and sustainable rail freight transports. Traditional methods for estimating load weight often suffer from limitations in accuracy and efficiency. READ MORE

  5. 4. Smart Scooter : Solving e-scooter safety problems with multi-modal, privacy-preserving sensor technology and machine learning

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Beatrice Lovely; [2022]
    Keywords : Smart Devices; Machine Learning; Sensors; Radar; Inertial Measurement Unit; Computer Vision; Smarta Saker; Maskininlärning; Sensorer; Radar; Tröghetsmåttenhet; Dator- seende;

    Abstract : Micromobility ride-share scooters (e-scooters) have become a popular mode of transport in several major cities around the world, yet several safety and accessibility issues stem from how these scooters are operated, including sidewalk riding, unsafe parking and wrong-way riding. This thesis tackles these issues through a novel, privacy-preserving, end-to-end sensor system that employs lightweight machine learning models to provide real-time feedback to users to present unsafe scooter operation. READ MORE

  6. 5. Distributionally Robust Risk-Bounded Path Planning Through Exact Spatio-temporal Risk Allocation

    University essay from Lunds universitet/Institutionen för reglerteknik

    Author : Kajsa Ekenberg; [2022]
    Keywords : Technology and Engineering;

    Abstract : Planning safe paths in the presence of uncertainty is considered a central challenge in enabling robots to successfully navigate in real-world environments. Assumptions about Gaussian uncertainty are rarely justifiable based on real data and can lead to serious miscalculations of risk. READ MORE