#Docs2 motion planning gamemaker full#
#Docs2 motion planning gamemaker plus#

A planner is resolution complete if it always finds a solution when one exists at the level of discretization employed in the representation of the problem. A weaker version of this notion is resolution completeness. We say that a planner is complete if it always finds a solution when one exists. If the C-space changes often, however, a single-query planner attempts to find the solution to a single motion-planning problem as quickly as possible.

A multiple-query planner is one that invests time in developing a good representation of C-space, so that future motion-planning problems in that space can be solved quickly. Finally, we might require motions that go exactly to a goal state or we could be satisfied with a final state anywhere in a goal region.ДЄpart from the characteristics of the motion planning problem, we can define the following properties of a motion planner: The planner may be designed for multiple queries or a single query. We could ask for minimum-time, minimum-length, or otherwise minimum-cost motions, or we could be satisfied with any collision-free motion that reaches the goal. The planner might have to make changes in real time as obstacles move in the environment, or it could be allowed to do its work in advance of the robot motion. The robot could have an actuator for every degree of freedom, like most robot arms, or it could have fewer actuators than degrees of freedom, like a car with only two control inputs, forward-backward speed and turning speed.

#Docs2 motion planning gamemaker full#

There are a number of possible variations on the motion-planning problem: we could plan a full trajectory, with timing information, or just a collision-free geometric path. We assume that we have perfect models of the robot and the environment. With these definitions, a fairly general statement of the motion planning problem is: 'Given an initial state x_start and a desired final state x_goal, find a time T and a set of controls such that the motion satisfies x-of-T equals x_goal and is collision free.' The integral form of the equations of motion is shown here. The equations of motion are x-dot equals f of (x,u), where the control u is an element of the feasible control set capital U.

#Docs2 motion planning gamemaker plus#

The state of the robot is x, where x could simply be the configuration q, if the robot's control inputs are considered to be velocities, or it could be the configuration plus the velocity, if the control inputs are considered to be accelerations, or forces. The C-space is the union of C_free, the set of configurations where the robot does not contact any obstacle, and C_obs, the set of configurations where the robot is in collision with an obstacle. More generally, the C-space of the robot could be an arbitrary manifold, like SE(3) for a rigid body, but we will focus on configurations explicitly parametrized by a minimum set of coordinates. In this chapter, the configuration of a robot is described by a vector of n coordinates q, and the robot's C-space is denoted C, a subset of R^n. Since some versions of the path planning problem have been called the piano-mover's problem, here's an animation of a piano being maneuvered through a tight space. For example, in this video, a motion has been planned for the robot arm to move its end-effector from one frame to another, without hitting any obstacles in the environment. Chapter 10 focuses on robot motion planning, particularly in the case of obstacles in the environment.