irsim.lib.behavior.behavior_methods#

Functions#

beh_diff_rvo(→ numpy.ndarray)

Behavior function for differential drive robot using RVO (Reciprocal Velocity Obstacles).

beh_diff_dash(→ numpy.ndarray)

Behavior function for differential drive robot using dash-to-goal behavior.

beh_omni_dash(→ numpy.ndarray)

Behavior function for omnidirectional robot using dash-to-goal behavior.

beh_omni_rvo(→ numpy.ndarray)

Behavior function for omnidirectional robot using RVO (Reciprocal Velocity Obstacles).

beh_diff_sfm(→ numpy.ndarray)

Behavior function for differential drive robot using the Social Force Model.

beh_omni_sfm(→ numpy.ndarray)

Behavior function for omnidirectional robot using the Social Force Model.

beh_omni_angular_dash(→ numpy.ndarray)

Behavior function for omnidirectional-angular robot using dash-to-goal behavior.

beh_acker_dash(→ numpy.ndarray)

Behavior function for Ackermann steering robot using dash-to-goal behavior.

SFMVelocity(→ tuple[float, float])

Compute the next world-frame velocity using the Social Force Model.

OmniRVO(→ numpy.ndarray)

Calculate the omnidirectional velocity using RVO.

DiffRVO(→ numpy.ndarray)

Calculate the differential drive velocity using RVO.

OmniDash(→ numpy.ndarray)

Calculate the body-frame velocity to reach a goal.

OmniAngularDash(→ numpy.ndarray)

Calculate body-frame velocity to reach a goal.

DiffDash(→ numpy.ndarray)

Calculate the differential drive velocity to reach a goal.

AckerDash(→ numpy.ndarray)

Calculate the Ackermann steering velocity to reach a goal.

Module Contents#

irsim.lib.behavior.behavior_methods.beh_diff_rvo(ego_object: Any, external_objects: list[Any], **kwargs: Any) → numpy.ndarray[source]#

Behavior function for differential drive robot using RVO (Reciprocal Velocity Obstacles).

Parameters:

  • ego_object – The ego robot object.

  • external_objects (list) – List of external objects in the environment.

  • **kwargs – Additional keyword arguments: - vxmax (float): Maximum x velocity, default 1.5. - vymax (float): Maximum y velocity, default 1.5. - acce (float): Acceleration factor, default 1.0. - factor (float): Additional scaling factor, default 1.0. - mode (str): RVO calculation mode, default “rvo”. - neighbor_threshold (float): Neighbor threshold distance, default 10.0.

Returns:

Velocity [linear, angular] (2x1) for differential drive.

Return type:

np.array

irsim.lib.behavior.behavior_methods.beh_diff_dash(ego_object: Any, external_objects: list[Any], **kwargs: Any) → numpy.ndarray[source]#

Behavior function for differential drive robot using dash-to-goal behavior.

Parameters:

  • ego_object – The ego robot object.

  • external_objects (list) – List of external objects in the environment.

  • **kwargs – Additional keyword arguments: - angle_tolerance (float): Allowable angular deviation, default 0.1.

Returns:

Velocity [linear, angular] (2x1) for differential drive.

Return type:

np.array

irsim.lib.behavior.behavior_methods.beh_omni_dash(ego_object: Any, external_objects: list[Any], **kwargs: Any) → numpy.ndarray[source]#

Behavior function for omnidirectional robot using dash-to-goal behavior.

Parameters:

  • ego_object – The ego robot object.

  • external_objects (list) – List of external objects in the environment.

  • **kwargs – Additional keyword arguments (currently unused).

Returns:

Velocity [vx, vy] (2x1) for omnidirectional drive.

Return type:

np.array

irsim.lib.behavior.behavior_methods.beh_omni_rvo(ego_object: Any, external_objects: list[Any], **kwargs: Any) → numpy.ndarray[source]#

Behavior function for omnidirectional robot using RVO (Reciprocal Velocity Obstacles).

Parameters:

  • ego_object – The ego robot object.

  • external_objects (list) – List of external objects in the environment.

  • **kwargs – Additional keyword arguments: - vxmax (float): Maximum x velocity, default 1.5. - vymax (float): Maximum y velocity, default 1.5. - acce (float): Acceleration factor, default 1.0. - factor (float): Additional scaling factor, default 1.0. - mode (str): RVO calculation mode, default “rvo”. - neighbor_threshold (float): Neighbor threshold distance, default 3.0.

Returns:

Velocity [vx, vy] (2x1) for omnidirectional drive.

Return type:

np.array

irsim.lib.behavior.behavior_methods.beh_diff_sfm(ego_object: Any, external_objects: list[Any], **kwargs: Any) → numpy.ndarray[source]#

Behavior function for differential drive robot using the Social Force Model.

Parameters:

  • ego_object – The ego robot object.

  • external_objects (list) – List of external objects in the environment.

  • **kwargs – SFM tuning parameters; see SFMVelocity().

Returns:

Velocity [linear, angular] (2x1) for differential drive.

Return type:

np.array

irsim.lib.behavior.behavior_methods.beh_omni_sfm(ego_object: Any, external_objects: list[Any], **kwargs: Any) → numpy.ndarray[source]#

Behavior function for omnidirectional robot using the Social Force Model.

Parameters:

  • ego_object – The ego robot object.

  • external_objects (list) – List of external objects in the environment.

  • **kwargs – SFM tuning parameters; see SFMVelocity().

Returns:

Velocity [vx, vy] (2x1) for omnidirectional drive.

Return type:

np.array

irsim.lib.behavior.behavior_methods.beh_omni_angular_dash(ego_object: Any, external_objects: list[Any], **kwargs: Any) → numpy.ndarray[source]#

Behavior function for omnidirectional-angular robot using dash-to-goal behavior.

Parameters:

  • ego_object – The ego robot object.

  • external_objects (list) – List of external objects in the environment.

  • **kwargs – Additional keyword arguments: - angle_tolerance (float): Allowable angular deviation, default 0.1.

Returns:

Velocity [forward, lateral, yaw_rate] (3x1) in body frame.

Return type:

np.array

irsim.lib.behavior.behavior_methods.beh_acker_dash(ego_object: Any, external_objects: list[Any], **kwargs: Any) → numpy.ndarray[source]#

Behavior function for Ackermann steering robot using dash-to-goal behavior.

Parameters:

  • ego_object – The ego robot object.

  • external_objects (list) – List of external objects in the environment.

  • **kwargs – Additional keyword arguments: - angle_tolerance (float): Allowable angular deviation, default 0.1.

Returns:

Velocity [linear, steering angle] (2x1) for Ackermann drive.

Return type:

np.array

irsim.lib.behavior.behavior_methods.SFMVelocity(state_tuple: Any, neighbor_list: list[Any] | None = None, line_segments: list[list[float]] | None = None, vmax: float = 1.5, step_time: float = 0.1, neighbor_threshold: float = 10.0, relaxation_time: float = 0.5, force_factor_desired: float = 1.0, force_factor_social: float = 2.1, force_factor_obstacle: float = 10.0, sigma_obstacle: float = 0.8, lambda_importance: float = 2.0, gamma: float = 0.35, n_angular: float = 2.0, n_velocity: float = 3.0, safety_radius: float = 0.0, **_: Any) → tuple[float, float][source]#

Compute the next world-frame velocity using the Social Force Model.

Anisotropic Moussaid-Helbing (2009) variant.

Parameters:

  • state_tuple – Full RVO-style state [x, y, vx, vy, radius, vx_des, vy_des, theta].

  • neighbor_list – Neighbour states [[x, y, vx, vy, radius], ...].

  • line_segments – Line obstacles [[x1, y1, x2, y2], ...].

  • vmax – Speed cap.

  • step_time – Integration step.

  • neighbor_threshold – Spatial cutoff for social interactions.

  • relaxation_time – gamma, n_angular, n_velocity: SFM tuning parameters (see social_force_model).

  • force_factor_* – gamma, n_angular, n_velocity: SFM tuning parameters (see social_force_model).

  • sigma_obstacle – gamma, n_angular, n_velocity: SFM tuning parameters (see social_force_model).

  • lambda_importance – gamma, n_angular, n_velocity: SFM tuning parameters (see social_force_model).

:paramgamma, n_angular, n_velocity: SFM tuning parameters

(see social_force_model).

Returns:

Updated world-frame velocity (vx, vy).

Return type:

tuple[float, float]

irsim.lib.behavior.behavior_methods.OmniRVO(state_tuple: Any, neighbor_list: list[Any] | None = None, vxmax: float = 1.5, vymax: float = 1.5, acce: float = 1, factor: float = 1.0, mode: str = 'rvo', neighbor_threshold: float = 3.0, line_segments: list[list[float]] | None = None) → numpy.ndarray[source]#

Calculate the omnidirectional velocity using RVO.

Parameters:

  • state_tuple (tuple) – Current state and orientation.

  • neighbor_list (list) – List of neighboring agents (default None).

  • vxmax (float) – Maximum x velocity (default 1.5).

  • vymax (float) – Maximum y velocity (default 1.5).

  • acce (float) – Acceleration factor (default 1).

  • factor (float) – Additional scaling factor (default 1.0).

  • mode (str) – RVO calculation mode (default “rvo”).

  • neighbor_threshold (float) – Neighbor threshold (default 3.0).

  • line_segments (list) – Line segments [[x1, y1, x2, y2], …] (default None).

Returns:

Velocity [vx, vy] (2x1).

Return type:

np.array

irsim.lib.behavior.behavior_methods.DiffRVO(state_tuple: Any, neighbor_list: list[Any] | None = None, vxmax: float = 1.5, vymax: float = 1.5, acce: float = 1, factor: float = 1.0, mode: str = 'rvo', neighbor_threshold: float = 3.0, line_segments: list[list[float]] | None = None) → numpy.ndarray[source]#

Calculate the differential drive velocity using RVO.

Parameters:

  • state_tuple (tuple) – Current state and orientation.

  • neighbor_list (list) – List of neighboring agents (default None).

  • vxmax (float) – Maximum x velocity (default 1.5).

  • vymax (float) – Maximum y velocity (default 1.5).

  • acce (float) – Acceleration factor (default 1).

  • factor (float) – Additional scaling factor (default 1.0).

  • mode (str) – RVO calculation mode (default “rvo”).

  • neighbor_threshold (float) – Neighbor threshold (default 3.0).

  • line_segments (list) – Line segments [[x1, y1, x2, y2], …] (default None).

Returns:

Velocity [linear, angular] (2x1).

Return type:

np.array

irsim.lib.behavior.behavior_methods.OmniDash(state: numpy.ndarray, goal: numpy.ndarray, max_vel: numpy.ndarray, goal_threshold: float = 0.1) → numpy.ndarray[source]#

Calculate the body-frame velocity to reach a goal.

Parameters:

  • state (np.array) – Current state [x, y, theta] (3x1 or 2x1).

  • goal (np.array) – Goal position [x, y] (2x1).

  • max_vel (np.array) – Maximum velocity [forward, lateral] (2x1).

  • goal_threshold (float) – Distance threshold to consider goal reached (default 0.1).

Returns:

Body-frame velocity [forward, lateral] (2x1).

Return type:

np.array

irsim.lib.behavior.behavior_methods.OmniAngularDash(state: numpy.ndarray, goal: numpy.ndarray, max_vel: numpy.ndarray, goal_threshold: float = 0.3, angle_tolerance: float = 0.1) → numpy.ndarray[source]#

Calculate body-frame velocity to reach a goal.

Drives forward and strafes laterally toward the goal while turning to face it. After arriving at the goal position, rotates in place to match the goal orientation.

Parameters:

  • state (np.array) – Current state [x, y, theta] (3x1).

  • goal (np.array) – Goal state [x, y, theta] (3x1).

  • max_vel (np.array) – Maximum velocity [forward, lateral, yaw_rate] (3x1).

  • goal_threshold (float) – Distance threshold to consider goal reached (default 0.3).

  • angle_tolerance (float) – Allowable angular deviation (default 0.1).

Returns:

Body-frame velocity [forward, lateral, yaw_rate] (3x1).

Return type:

np.array

irsim.lib.behavior.behavior_methods.DiffDash(state: numpy.ndarray, goal: numpy.ndarray, max_vel: numpy.ndarray, goal_threshold: float = 0.1, angle_tolerance: float = 0.2) → numpy.ndarray[source]#

Calculate the differential drive velocity to reach a goal.

Parameters:

  • state (np.array) – Current state [x, y, theta] (3x1).

  • goal (np.array) – Goal position [x, y] (2x1).

  • max_vel (np.array) – Maximum velocity [linear, angular] (2x1).

  • goal_threshold (float) – Distance threshold to consider goal reached (default 0.1).

  • angle_tolerance (float) – Allowable angular deviation (default 0.2).

Returns:

Velocity [linear, angular] (2x1).

Return type:

np.array

irsim.lib.behavior.behavior_methods.AckerDash(state: numpy.ndarray, goal: numpy.ndarray, max_vel: numpy.ndarray, goal_threshold: float, angle_tolerance: float) → numpy.ndarray[source]#

Calculate the Ackermann steering velocity to reach a goal.

Parameters:

  • state (np.array) – Current state [x, y, theta] (3x1).

  • goal (np.array) – Goal position [x, y] (2x1).

  • max_vel (np.array) – Maximum velocity [linear, steering angle] (2x1).

  • goal_threshold (float) – Distance threshold to consider goal reached.

  • angle_tolerance (float) – Allowable angular deviation.

Returns:

Velocity [linear, steering angle] (2x1).

Return type:

np.array