irsim.lib

Submodules

• irsim.lib.algorithm
• irsim.lib.behavior
• irsim.lib.handler
• irsim.lib.path_planners

Attributes

register_behavior
register_behavior_class
register_group_behavior
register_group_behavior_class
kinematics_factory

Classes

reciprocal_vel_obs	A class to implement the Reciprocal Velocity Obstacle (RVO) algorithm for multi-robot collision avoidance.
social_force_model	Social Force Model controller for a single agent.
Behavior	Represents the behavior of an agent in the simulation.
GeometryFactory	Factory class to create geometry handlers.
KinematicsFactory	Factory class to create kinematics handlers.
KinematicsHandler	Abstract base class for handling robot kinematics.

Functions

generate_polygon(→ numpy.ndarray)	Generate a random polygon around a center point.
random_generate_polygon(...)	reference: https://stackoverflow.com/questions/8997099/algorithm-to-generate-random-2d-polygon
ackermann_kinematics(→ numpy.ndarray)	Calculate the next state for an Ackermann steering vehicle.
differential_kinematics(→ numpy.ndarray)	Calculate the next state for a differential wheel robot.
omni_kinematics(→ numpy.ndarray)	Calculate the next position for an omnidirectional robot.
register_kinematics(name)	Decorator to register a KinematicsHandler subclass.

Package Contents

irsim.lib.generate_polygon(center: list[float], avg_radius: float, irregularity: float, spikeyness: float, num_vertices: int) → numpy.ndarray

Generate a random polygon around a center point.

参数:

• center (Tuple[float, float]) -- Center of the polygon.

• avg_radius (float) -- Average radius from the center to vertices.

• irregularity (float) -- Variance of angle spacing between vertices. Range [0, 1]

• spikeyness (float) -- Variance of radius from the center. Range [0, 1]

• num_vertices (int) -- Number of vertices for the polygon.

返回:

Vertices of the polygon in CCW order.

返回类型:

numpy.ndarray

irsim.lib.random_generate_polygon(number: int = 1, center_range: list[float] | None = None, avg_radius_range: list[float] | None = None, irregularity_range: list[float] | None = None, spikeyness_range: list[float] | None = None, num_vertices_range: list[int] | None = None, **kwargs: Any) → numpy.ndarray | list[numpy.ndarray]

reference: https://stackoverflow.com/questions/8997099/algorithm-to-generate-random-2d-polygon

Generate random polygons with specified properties.

参数:

• number (int) -- Number of polygons to generate (default 1).

• center_range (List[float]) -- Range for the polygon center [min_x, min_y, max_x, max_y].

• avg_radius_range (List[float]) -- Range for the average radius of the polygons.

• irregularity_range (List[float]) -- Range for the irregularity of the polygons.

• spikeyness_range (List[float]) -- Range for the spikeyness of the polygons.

• num_vertices_range (List[int]) -- Range for the number of vertices of the polygons.

返回:

List of vertices for each polygon or a single polygon's vertices if number=1.

irsim.lib.ackermann_kinematics(state: numpy.ndarray, velocity: numpy.ndarray, step_time: float, noise: bool = False, alpha: list[float] | None = None, mode: str = 'steer', wheelbase: float = 1) → numpy.ndarray

Calculate the next state for an Ackermann steering vehicle.

参数:

• state -- A 4x1 vector [x, y, theta, steer_angle] representing the current state.

• velocity -- A 2x1 vector representing the current velocities, format depends on mode. For "steer" mode, [linear, steer_angle] is expected. For "angular" mode, [linear, angular] is expected.

• step_time -- The time step for the simulation.

• noise -- Boolean indicating whether to add noise to the velocity (default False).

• alpha -- List of noise parameters for the velocity model (default [0.03, 0, 0, 0.03]). alpha[0] and alpha[1] are for linear velocity, alpha[2] and alpha[3] are for angular velocity.

• mode -- The kinematic mode, either "steer" or "angular" (default "steer").

• wheelbase -- The distance between the front and rear axles (default 1).

返回:

A 4x1 vector representing the next state.

返回类型:

new_state

irsim.lib.differential_kinematics(state: numpy.ndarray, velocity: numpy.ndarray, step_time: float, noise: bool = False, alpha: list[float] | None = None) → numpy.ndarray

Calculate the next state for a differential wheel robot.

参数:

• state -- A 3x1 vector [x, y, theta] representing the current position and orientation.

• velocity -- A 2x1 vector [linear, angular] representing the current velocities.

• step_time -- The time step for the simulation.

• noise -- Boolean indicating whether to add noise to the velocity (default False).

• alpha -- List of noise parameters for the velocity model (default [0.03, 0, 0, 0.03]). alpha[0] and alpha[1] are for linear velocity, alpha[2] and alpha[3] are for angular velocity.

返回:

A 3x1 vector [x, y, theta] representing the next state.

返回类型:

next_state

irsim.lib.omni_kinematics(state: numpy.ndarray, velocity: numpy.ndarray, step_time: float, noise: bool = False, alpha: list[float] | None = None) → numpy.ndarray

Calculate the next position for an omnidirectional robot.

Uses body-frame velocity: the two components are forward and lateral speeds relative to the robot heading (theta). Since omni robots have no yaw control, theta remains unchanged.

参数:

• state -- A 3x1 vector [x, y, theta] representing the current state.

• velocity -- A 2x1 vector [forward, lateral] in body frame.

• step_time -- The time step for the simulation.

• noise -- Boolean indicating whether to add noise to the velocity (default False).

• alpha -- List of noise parameters for the velocity model (default [0.03, 0.03]).

返回:

A 3x1 vector [x, y, theta] representing the next state.

Theta is preserved unchanged.

返回类型:

next_state

class irsim.lib.reciprocal_vel_obs(state: list, obs_state_list=None, vxmax=1.5, vymax=1.5, acce=0.5, factor=1.0, line_obs_list=None)

A class to implement the Reciprocal Velocity Obstacle (RVO) algorithm for multi-robot collision avoidance.

参数:

• state (list) -- The rvo state of the agent [x, y, vx, vy, radius, vx_des, vy_des].

• obs_state_list (list) -- List of states of static obstacles [[x, y, vx, vy, radius]].

• vxmax (float) -- Maximum velocity in the x direction.

• vymax (float) -- Maximum velocity in the y direction.

• acce (float) -- Acceleration limit.

• factor (float) -- Penalty weighting factor for velocity selection.

• line_obs_list (list) -- List of line segments [[x1, y1, x2, y2], ...].

state

obs_state_list = None

line_obs_list = None

vxmax = 1.5

vymax = 1.5

acce = 0.5

factor = 1.0

update(state, obs_state_list, line_obs_list=None)

Update the agent, circular-obstacle, and line-obstacle states.

cal_vel(mode='rvo')

Calculate the velocity of the agent based on the Reciprocal Velocity Obstacle (RVO) algorithm.

参数:

mode (str) -- The vo configure to calculate the velocity. It can be "rvo", "hrvo", or "vo". - rvo: Reciprocal Velocity Obstacle (RVO) algorithm, for multi-robot collision avoidance. - hrvo: Hybrid Reciprocal Velocity Obstacle (HRVO) algorithm, for multi-robot collision avoidance. - vo: Velocity Obstacle (VO) algorithm, for obstacle-robot collision avoidance.

返回:

Selected velocity [vx, vy].

返回类型:

list[float]

config_rvo()

Build reciprocal velocity-obstacle cones for all obstacles.

config_rvo_mode(obstacle)

Build one RVO cone for a circular obstacle.

参数:

obstacle -- Moving obstacle state [x, y, vx, vy, radius] or static circular obstacle state [x, y, radius].

返回:

[apex, left_vector, right_vector] cone description.

返回类型:

list

config_hrvo()

Build hybrid reciprocal velocity-obstacle cones for all obstacles.

config_hrvo_mode(obstacle)

Build one HRVO cone for a circular obstacle.

参数:

obstacle -- Moving obstacle state [x, y, vx, vy, radius] or static circular obstacle state [x, y, radius].

返回:

[apex, left_vector, right_vector] cone description.

返回类型:

list | None

config_vo()

Build standard velocity-obstacle cones for all obstacles.

config_vo_mode(obstacle)

Build one VO cone for a circular obstacle.

参数:

obstacle -- Moving obstacle state [x, y, vx, vy, radius] or static circular obstacle state [x, y, radius].

返回:

[apex, left_vector, right_vector] cone description.

返回类型:

list

config_vo_lines()

Compute VO cones for line segment obstacles.

For each segment, compute the angular span as seen from the agent, expanded by asin(r / dist) on each side to account for the agent radius. The apex is [0, 0] since line obstacles are static.

vel_candidate(rvo_list)

Sample reachable velocities and split them by VO feasibility.

参数:

rvo_list -- Velocity-obstacle cone descriptions [apex, left, right].

返回:

Feasible velocities outside all cones and infeasible velocities inside at least one cone.

返回类型:

tuple[list, list]

vo_out(vx, vy, rvo_list)

Return whether a candidate velocity lies outside every VO cone.

vel_select(vo_outside, vo_inside)

Select the best velocity from feasible candidates or penalized fallback.

Both arguments may be lists of [vx, vy] or (N, 2) arrays; the first minimum is returned, as min would.

penalties(vels, vel_des, factor)

Vectorized penalty() for an (N, 2) array of velocities.

Same formulas and branches as the scalar version, evaluated for all candidates at once; the crowded-scene fallback otherwise calls penalty hundreds of times per robot per step.

penalty(vel, vel_des, factor)

Scalar penalties() for one velocity, kept for compatibility.

static between_vector(line_left_vector, line_right_vector, line_vector)

Return whether line_vector lies inside a cone boundary pair.

static cross_product(vector1, vector2)

Compute the 2D cross product vector1 x vector2.

class irsim.lib.social_force_model(state: list, neighbor_list: list | None = None, line_obs_list: list | None = None, vmax: float = 1.5, step_time: float = 0.1, 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, neighbor_range: float = 10.0, safety_radius: float = 0.0)

Social Force Model controller for a single agent.

The interface mirrors reciprocal_vel_obs so the two algorithms are interchangeable from a behavior method.

参数:

• state (list) -- Agent state [x, y, vx, vy, radius, vx_des, vy_des, theta].

• neighbor_list (list) -- Other moving/static circular agents [[x, y, vx, vy, radius], ...].

• line_obs_list (list) -- Line obstacles [[x1, y1, x2, y2], ...].

• vmax (float) -- Speed cap applied after the velocity update.

• step_time (float) -- Integration step dt.

• relaxation_time (float) -- tau in the desired-force term.

• force_factor_desired (float) -- Weight alpha_D on the desired force.

• force_factor_social (float) -- Weight alpha_S on the social force.

• force_factor_obstacle (float) -- Weight alpha_O on the obstacle force.

• sigma_obstacle (float) -- Decay length of the obstacle repulsion.

• lambda_importance (float) -- Weight of relative velocity in the interaction direction (lambda in Moussaïd 2009).

• gamma (float) -- Sets the interaction range B = gamma * ||t||.

• n_angular (float) -- Angular sharpness n for the sideways force.

• n_velocity (float) -- Angular sharpness n' for the slowdown force.

• neighbor_range (float) -- Max distance for an agent to count as a social-force neighbor.

• safety_radius (float) -- Personal-space buffer subtracted from the agent-to-agent distance inside the social-force exponential. 0 reproduces the upstream behavior (point agents). > 0 shifts the decay closer-in so the repulsion saturates at 2 * safety_radius of centre-to-centre clearance, effectively giving each agent a body radius for SFM.

state

neighbor_list

line_obs_list

vmax = 1.5

step_time = 0.1

relaxation_time = 0.5

force_factor_desired = 1.0

force_factor_social = 2.1

force_factor_obstacle = 10.0

sigma_obstacle = 0.8

lambda_importance = 2.0

gamma = 0.35

n_angular = 2.0

n_velocity = 3.0

neighbor_range = 10.0

safety_radius = 0.0

update(state: list, neighbor_list: list, line_obs_list: list | None = None) → None

Refresh the per-step inputs without re-instantiating.

cal_vel() → list

Integrate one SFM step and return the new global velocity.

返回:

Updated velocity [vx, vy], clipped to vmax.

返回类型:

list[float]

desired_force() → list

Relaxation toward the desired velocity v0 * e_goal.

The desired velocity is supplied directly via state[5:7].

social_force() → list

Anisotropic neighbor repulsion (Moussaid-Helbing 2009).

Iterates over all neighbors within neighbor_range and sums their contribution.

obstacle_force() → list

Exponential repulsion summed over all nearby line obstacles.

The upstream reference uses only the single nearest obstacle, which oscillates in symmetric environments (two parallel walls flip which one is "nearest" each step). We use the Helbing-Molnar (1995) summation form instead: every segment within 5 * sigma_obstacle contributes an exponentially decayed push, so symmetric walls cancel and the agent walks the centreline. The integration is also clamped against overlap (distance < 0 would otherwise make exp(-distance/sigma) explode).

class irsim.lib.Behavior(object_info=None, behavior_dict=None)

Represents the behavior of an agent in the simulation.

参数:

• object_info (object) -- Object information from the object_base class ObjectInfo.

• behavior_dict (dict) --

  Dictionary containing behavior parameters for different behaviors. Name Options include: 'dash', 'rvo'. target_roles:

  • 'all': all objects in the environment will be considered within this behavior.

  • 'obstacle': only obstacles will be considered within this behavior.

  • 'robot': only robots will be considered within this behavior.

Initialize the behavior with object info and parameters.

参数:

• object_info -- Information about the agent (from ObjectBase.ObjectInfo).

• behavior_dict (dict | None) -- Behavior parameters; if None, defaults to an empty dict.

object_info = None

behavior_dict

gen_vel(ego_object, external_objects=None)

Generate a velocity for the agent based on configured behavior.

参数:

• ego_object -- The agent itself (object with needed attributes).

• external_objects (list | None) -- Other objects in the environment.

返回:

A 2x1 velocity vector appropriate for the agent kinematics.

返回类型:

numpy.ndarray

load_behavior(behaviors: str = '.behavior_methods')

Load behavior parameters from the script.

参数:

behaviors (str) -- name of the behavior script.

invoke_behavior(kinematics: str, action: str, **kwargs: Any) → Any

Invoke a specific behavior method based on kinematics model and action type.

This method looks up and executes the appropriate behavior function from the behavior registry based on the combination of kinematics model and action name.

参数:

• kinematics (str) --

  Kinematics model identifier. Supported values:

  • 'diff': Differential drive kinematics

  • 'omni': Omnidirectional kinematics

  • 'acker': Ackermann steering kinematics

• action (str) --

  Behavior action name. Examples:

  • 'dash': Direct movement toward goal

  • 'rvo': Reciprocal Velocity Obstacles for collision avoidance

• **kwargs -- Additional keyword arguments passed to the behavior function. Common parameters include ego_object, external_objects, goal, etc.

返回:

Generated velocity vector (2x1) in the format appropriate for the specified kinematics model.

返回类型:

np.ndarray

抛出:

ValueError -- If no behavior method is found for the given kinematics and action combination.

示例

>>> # Invoke differential drive dash behavior
>>> vel = behavior.invoke_behavior('diff', 'dash',
...                               ego_object=robot,
...                               external_objects=obstacles)

irsim.lib.register_behavior

irsim.lib.register_behavior_class

irsim.lib.register_group_behavior

irsim.lib.register_group_behavior_class

class irsim.lib.GeometryFactory

Factory class to create geometry handlers.

static create_geometry(name: str = 'circle', **kwargs) → geometry_handler

Create a geometry handler from a YAML shape name.

参数:

• name -- Shape name. Supported values are circle, polygon, rectangle, compound, linestring, and map.

• **kwargs -- Shape-specific parameters forwarded to the handler.

返回:

Concrete geometry handler.

返回类型:

geometry_handler

抛出:

ValueError -- If name is not supported.

class irsim.lib.KinematicsFactory

Factory class to create kinematics handlers.

static create_kinematics(name: str | None = None, noise: bool = False, alpha: list | None = None, mode: str | None = None, wheelbase: float | None = None, role: str = 'robot', *, shape_wheelbase: float | None = None, **kwargs: Any) → KinematicsHandler

Create a kinematics handler from a YAML kinematics block.

参数:

• name -- Registered kinematics name: diff, omni, omni_angular, acker, or a custom name registered with register_kinematics(). None defaults to diff; static is retained as the static-object sentinel.

• noise -- Whether to apply motion noise.

• alpha -- Noise parameters passed to the handler.

• mode -- Steering mode of acker handlers; forwarded when given.

• wheelbase -- Wheelbase set under kinematics; forwarded when given and takes precedence over shape_wheelbase.

• role -- Object role, retained for API compatibility.

• shape_wheelbase -- Wheelbase taken from a car-like shape; the fallback for acker handlers (default 1.0) when wheelbase is not given.

• **kwargs -- Any other kinematics key, forwarded to the handler's __init__ (a custom handler's own parameters).

返回:

Handler instance for name.

返回类型:

KinematicsHandler

抛出:

• NotImplementedError -- If name is not registered.

• TypeError -- If the handler does not accept a forwarded parameter.

static get_handler_class(name: str) → type[KinematicsHandler] | None

Look up a registered handler class by name without instantiation.

参数:

name (str) -- Kinematics name (e.g. "diff", "omni").

返回:

The class, or None if not found.

返回类型:

type[KinematicsHandler] | None

class irsim.lib.KinematicsHandler(name, noise: bool = False, alpha: list | None = None)

Bases: abc.ABC

Abstract base class for handling robot kinematics.

Subclasses should set the class-attribute metadata described below and implement step(), velocity_to_xy(), compute_max_speed(), and compute_heading(). A subclass may add keyword parameters to __init__; any extra key under kinematics: in YAML is passed to it.

Initialize the KinematicsHandler class.

参数:

• name (str) -- Kinematics model name.

• noise (bool) -- Boolean indicating whether to add noise to the velocity (default False).

• alpha (list) -- List of noise parameters for the velocity model (default [0.03, 0, 0, 0.03]).

action_dim: int = 2

min_state_dim: int = 3

state_dim: int = 3

vel_max: ClassVar[list[float]] = [1, 1]

vel_min: ClassVar[list[float]]

acce: ClassVar[list[float]]

color: str = 'g'

obstacle_color: str = 'k'

description: str | None = None

show_arrow: bool = True

name

noise = False

alpha = [0.03, 0, 0, 0.03]

abstractmethod step(state: numpy.ndarray, velocity: numpy.ndarray, step_time: float) → numpy.ndarray

Calculate the next state using the kinematics model.

参数:

• state (np.ndarray) -- Current state.

• velocity (np.ndarray) -- Velocity vector.

• step_time (float) -- Time step for simulation.

返回:

Next state.

返回类型:

np.ndarray

velocity_to_xy(state: numpy.ndarray, velocity: numpy.ndarray) → numpy.ndarray

Convert velocity to [vx, vy] in world frame.

The default implementation follows differential-drive conventions: velocity[0] is the linear speed projected through the heading angle state[2], which is what vel_diff2world() does. Subclasses with different velocity semantics (e.g. omnidirectional) should override this.

参数:

• state (np.ndarray) -- Current state vector.

• velocity (np.ndarray) -- Velocity vector in kinematics frame.

返回:

(2, 1) array of [vx, vy].

返回类型:

np.ndarray

compute_max_speed(vel_max: numpy.ndarray) → float

Compute the scalar maximum speed from the vel_max vector.

The default implementation follows differential-drive conventions: the first component vel_max[0, 0] is the translational speed limit. Subclasses where max speed is derived differently (e.g. omnidirectional using the L2 norm) should override this.

参数:

vel_max (np.ndarray) -- Maximum velocity vector.

返回:

Scalar maximum speed.

返回类型:

float

compute_heading(state: numpy.ndarray, velocity: numpy.ndarray) → float

Compute the heading angle.

The default implementation follows differential-drive conventions: heading is state[2] (the orientation component). Returns 0.0 if the state has fewer than 3 rows.

参数:

• state (np.ndarray) -- Current state vector.

• velocity (np.ndarray) -- Current velocity vector.

返回:

Heading in radians.

返回类型:

float

irsim.lib.register_kinematics(name: str)

Decorator to register a KinematicsHandler subclass.

Any extra key under kinematics: in YAML is passed to the subclass's __init__ (after name, noise, alpha), so a registered model can take its own parameters, e.g. kinematics: {name: lag, tau: 0.5}.

参数:

name (str) -- Name used in YAML configs (e.g. "diff", "omni").

返回:

Class decorator that registers and returns the class unchanged.

返回类型:

Callable

irsim.lib.kinematics_factory: dict[str, collections.abc.Callable[..., Any]]