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(, dt)

Calculate body-frame velocity to reach a goal.

DiffDash(, dt)

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 – SFM tuning parameters (see social_force_model).

  • force_factor_* – SFM tuning parameters (see social_force_model).

  • sigma_obstacle – SFM tuning parameters (see social_force_model).

  • lambda_importance – SFM tuning parameters (see social_force_model).

  • gamma – SFM tuning parameters (see social_force_model).

  • n_angular – SFM tuning parameters (see social_force_model).

  • 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, angular_acce: float = float('inf'), dt: float = 0.0) → 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.

The yaw rate is ramped down near the target so the robot can decelerate to a stop within the remaining angle. The ramp uses the exact discrete-time formula ω ≤ -a·dt + √(a²·dt² + 2·a·remaining) so the robot stops cleanly within one step of angle_tolerance without overshoot.

Parameters:

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

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

  • max_vel (np.array) – Absolute 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).

  • angular_acce (float) – Angular acceleration limit (rad/s^2). Used to cap yaw rate so the robot can always stop within the remaining angle. inf (default) gives bang-bang behaviour.

  • dt (float) – Simulation time step (s). Used for exact discrete-time decel ramp. 0 (default) falls back to the continuous-time approximation.

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, angular_acce: float = float('inf'), dt: float = 0.0) → 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) – Absolute 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).

  • angular_acce (float) – Angular acceleration limit (rad/s^2) for the decel ramp. inf (default) gives bang-bang behaviour.

  • dt (float) – Simulation time step (s) for exact discrete-time decel ramp.

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