Магистерский проект презентация

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Система управления роботом
Визуальные датчики

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Положения робота -- localization
где Я ?
Детализация окружающего пространства – mapping
На что похоже то что вокруг меня?

Оба источника данных зашумлены.

map.

Prior: p(x0)

filter

The robot’s trajectory estimate is a tracking problem

Sequentially update μt and Σt

Represent the distribution of robot location xt (and map mt) by a large amount of simulated samples.

Resample xt (and mt) at each time step

small error will quickly accumulated over time steps.

The errors come from inaccurate measurement of actual robot motion (noisy action) and the distance from obstacle/landmark (noisy observation).

When the robot closes a physical loop in the environment, serious misalignment errors could happen.

p(o|x) is a normal distribution

Тогда:
Posterior p(x1), …, p(xt) are all normally distributed.


Mean μt and covariance matrix Σt can be derived analytically.
Sequentially update μt and Σt for each time step t

(3+2N)-dimentional Gaussian

State vector xt can be grown as new landmarks are discovered.

are focused on:
Reducing the cumulative error
Fast SLAM (online)
Way to organize the data structure (saving robot path and map).
Maintain single map: cumulative error
Multiple maps: memory and computation time

In Parr’s paper:
Use ancestry tree to record particle history
Each particle has its own map (multiple maps)
Use observation tree for each grid square (cell) to record the map corresponding to each particle.
Update ancestry tree and observation tree at each iteration.
Cell occupancy is represented by a probabilistic approach.

doesn’t know where the target is.

Agent has 2 sensors:

Camera: tell agent “occupied” or “empty” cells in 4 orientations, noisy sensor.

Acoustic sensor: find the orientation of the target, effective only within certain distance.

Noisy observation, noisy action.

Assumption:

not necessary.
Objective is to find the target. Robot need to actively select actions to find the target as soon as possible.

Similar to SLAM
To find the target, agent need build map and estimate its location.

(state) is either occupied or empty.
The agent moves between the empty grid squares.
Actions: walk to any one of the 4 directions, or “stay”. Could fail in walking with certain probability.
Observations: observe 4 orientations of its neighbor grid squares: “occupied” or “empty”. Could make a wrong observation with certain probability.
State, action and observation are all discrete.

state: the agent believes which state it is currently in. It is a distribution over all the states in the current map.

The map: The agent thinks what the environment is .

For each state (grid square), a 2-dimentional Dirichlet distribution is used to represent the probability of “empty” and “occupied”.

The hyperparameters of Dirichlet distribution are updated based on current observation and belief state.

following rules:

Less explored grids have higher reward.

Try to walk to the “empty” grid square.

Consider neighbor of s with priority.

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