A carregar...
Publicação
Robotic manipulation of objects through physical simulation
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 3.29 MB | Adobe PDF |
Resumo(s)
In recent years, robotics has profoundly impacted automated industries, with robotic manipulators revolutionizing efficiency and allowing humans to dedicate time to more complex tasks. However, traditional
robotic systems are programmed for predictable environments, creating a growing interest in replicating
human adaptability in these systems — especially in scenarios where object manipulation is necessary.
Humans form beliefs from neural processing of sensory information, which allow us to predict and anticipate the outcome of a situation by assigning higher probabilities to the case that corresponds the most
with our past experience and sensations. This aligns exactly with the operational principles of the Bayes
Filter, making it a promising method to implement in robotic manipulation to improve the reliability of
robots in performing tasks within uncontrolled environments.
Inspired by the Bayesian Brain theory, this dissertation aims to address this challenge by developing
a system that utilizes Bayesian particle filtering to estimate physical properties of an unknown object,
such as the mass and the center of mass, through manipulation. In this approach, we generate a range of
potential simulation scenarios, where each instance corresponds to a particle representing a possible state
of the object within a robot-object interaction. As the robot interacts with the object, it gathers sensory
measurements, which are used to iteratively update the particles through the filtering process, until the
particles converge toward a single, accurate estimation of the object’s properties.
The evaluation performed for the solution demonstrates the proposed method’s effectiveness in enhancing a manipulator’s adaptability to an unpredictable environment. By integrating the principles of
probabilistic robotics into manipulation, this research contributes to the development of more capable
robotic manipulation strategies in real-world applications, and confirms the viability of Bayesian filtering as a technique for robotic adaptability.
Descrição
Tese de mestrado, Engenharia Física, 2025, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
Manipulação Robótica Robótica Probabilística Simulação Física Filtro de Bayes Teses de mestrado - 2025