Configure Sensors for the robot
=================================

## LiDAR Configuration Parameters

The YAML configuration file and Python Script below shows an example of a robot with a 2D LiDAR sensor:

::::{tab-set}

:::{tab-item} Python Script

```python
import irsim

env = irsim.make()   

for i in range(1000):

    env.step()
    env.render(0.05)

    if env.done():
        break

env.end()
```

:::

:::{tab-item} YAML Configuration

YAML file (same name as the python script):

```yaml
world:
  height: 10  
  width: 10   

robot:
  - kinematics: {name: 'diff'}  # omni, diff, acker
    shape: {name: 'circle', radius: 0.2}  # radius
    goal: [9, 9, 0]

    sensors:
      - name: 'lidar2d'
        range_min: 0
        range_max: 5
        angle_range: 3.14 
        number: 200
        noise: False
        alpha: 0.3
      
obstacle:
  - shape: {name: 'circle', radius: 1.0}  # radius
    state: [5, 5, 0]  
  
  - shape: {name: 'rectangle', length: 1.5, width: 1.2}  # length, width
    state: [6, 5, 1] 
  
  - shape: {name: 'linestring', vertices: [[10, 5], [4, 0], [6, 7]]}  # vertices
    state: [0, 0, 0] 
```

:::

:::{tab-item} Demonstration
:selected:

```{image} gif/lidar2d.gif
:alt: Select Parameters
:width: 400px
:align: center
```
:::
::::

```{tip}
Update order

The environment updates sensors after all objects have moved in a step. This avoids temporal skew in readings. If you control objects manually, either pass `sensor_step=True` to `ObjectBase.step(...)` or call `obj.sensor_step()` after updating object states.
```

### Important Parameters Explained

To configure the 2D LiDAR sensor, the sensor name of `lidar2d` should be defined in the `sensors` section of the robot. Key parameters of the LiDAR sensor are explained below:

- **range_min**: The minimum range of the laser beam.
- **range_max**: The maximum range of the laser beam.
- **angle_range**: The angle range of the laser beam.
- **number**: The number of beams.
- **alpha**: The transparency of the laser beam.

A full list of parameters can be found in the [YAML Configuration](#../yaml_config/configuration/).


### Advanced Lidar Configuration with noise

To add noise to the LiDAR sensor, you can set the `noise` parameter to `True`.

::::{tab-set}

:::{tab-item} Python Script

```python
import irsim

env = irsim.make()   

for i in range(1000):

    env.step()
    env.render(0.05)

    if env.done():
        break

env.end()
```

:::

:::{tab-item} YAML Configuration

```yaml
world:
  height: 10  
  width: 10   

robot:
  - kinematics: {name: 'diff'}  # omni, diff, acker
    shape: {name: 'circle', radius: 0.2}  # radius
    goal: [9, 9, 0]

    sensors:
      - name: 'lidar2d'
        range_min: 0
        range_max: 5
        angle_range: 3.14 #  4.7123
        number: 200
        noise: True
        std: 0.1
        angle_std: 0.2
        offset: [0, 0, 0]
        alpha: 0.3
      
obstacle:
  - shape: {name: 'circle', radius: 1.0}  # radius
    state: [5, 5, 0]  
  
  - shape: {name: 'rectangle', length: 1.5, width: 1.2}  # length, width
    state: [6, 5, 1] 
  
  - shape: {name: 'linestring', vertices: [[10, 5], [4, 0], [6, 7]]}  # vertices
    state: [0, 0, 0] 

```

:::
::::

Gaussian noise is added to the LiDAR sensor with the `std` and `angle_std` parameters. The `std` parameter is the standard deviation of the range noise, and the `angle_std` parameter is the standard deviation of the angle noise. 


## FOV Configuration Parameters

The YAML configuration file and Python Script below shows an example of objects within the field of view (FOV). The FOV is defined by the `fov` (float) and `fov_radius` (float) parameters in the object configuration. Each object has a FOV that can detect the robot within the FOV by the function `fov_detect_object()`. 


::::{tab-set} 


:::{tab-item} Python Script

```python
import irsim

env = irsim.make()

for i in range(200):

    env.step()

    for obs in env.obstacle_list:
        if obs.fov_detect_object(env.robot):
            print(f'The robot is in the FOV of the {obs.name}. The parameters of this obstacle are: state [x, y, theta]: {obs.state.flatten()}, velocity [linear, angular]: {obs.velocity.flatten()}, fov in radian: {obs.fov}.')

    env.render(figure_kwargs={'dpi': 100})
    
env.end()
```

:::

:::{tab-item} YAML Configuration

```yaml
world:
  height: 50
  width: 50   
  step_time: 0.1 
  sample_time: 0.1  
  offset: [0, 0]  
  collision_mode: 'stop' 
  control_mode: 'auto' 

robot:
  - kinematics: {name: 'diff'}  # omni, diff, acker
    shape: {name: 'circle', radius: 0.4}
    state: [10, 10, 0, 0]
    goal: [45, 45, 0]
    goal_threshold: 0.4
    vel_max: [3, 1]
    vel_min: [-3, -1]
    behavior: {name: 'dash', wander: True, range_low: [15, 15, -3.14], range_high: [35, 35, 3.14]} 
    plot:
        show_goal: True
        show_trajectory: True

obstacle:
  - number: 10
    distribution: {name: 'random', range_low: [10, 10, -3.14], range_high: [40, 40, 3.14]}
    kinematics: {name: 'diff'}
    behavior: {name: 'rvo', vxmax: 1.5, vymax: 1.5, acce: 1.0, factor: 2.0, mode: 'vo', wander: True, range_low: [15, 15, -3.14], range_high: [35, 35, 3.14], target_roles: 'all'}
    vel_max: [3, 3.14]
    vel_min: [-3, -3.14]
    shape:
      - {name: 'circle', radius: 0.5}  # radius
    fov: 1.57 
    fov_radius: 5.0
    plot:
      show_fov: True
      show_arrow: True
      arrow_length: 0.8
```

:::

:::{tab-item} Demonstration
:selected:

```{image} gif/fov.gif
:alt: Select Parameters
:width: 400px
:align: center
```
:::
::::