We have formulated the motion economy problem as an AI planning problem. We have developed and formalized a model for representing the actions, environment and products of the domain. We have interpreted and represented the expertise of motion economy specialists as search control rules by using temporal formulas and used them to generate quality plans.

The problem of planning humanlike motions is difficult to define, to model and to solve. It is however a problem that will grow in importance as humanoid assistants and autonomous virtual characters become essential in many applications. In this abstract we propose simple definitions to model basic aspects of the problem and summarize algorithms based on multi-modality and search spaces defined...

This work considers a family of motion planning problems with movable blocks. Such problem is defined by a maze grid occupied by immovable blocks (walls) and free squares. There are k movable blocks (stones) and k fixed goal squares. The man is a movable block that can traverse free squares and move stones between them. The problem goal is to move the stones from their initial positions to the ...

Humanoid robots become increasingly sophisticated, both in terms of their movement as well as their sensorial capabilities. This allows to target for more complicated problems, eventually leading to robotic systems that can perform useful tasks in every days environment. In this paper, we will give an overview on some elements we consider to be important for a movement control and planning arch...

In this paper we propose a new approach to motion planning, based on the introduction of a lattice structure in the workspace of the robot, leading to efficient computations of plans for rather complex vehicles, and allowing for the implementation of optimization procedures in a rather straightforward way. The basic idea is the purposeful restriction of the set of possible input functions to th...

Motion planning is a fundamental problem in robotics. It comes in a variety of forms, but the simplest version is as follows. We are given a robot system B, which may consist of several rigid objects attached to each other through various joints, hinges, and links, or moving independently, and a 2D or 3D environment V cluttered with obstacles. We assume that the shape and location of the obstac...

This paper surveys some recent theoretical work in incorporating the dynamics of robot models in motion planning algorithms. Such work is often referred in the literature as kinodynamic motion planning. We rst give an overview of the problem in general and how it diiers from motion planning with pure kinematics. We then describe three model algorithms, each representing one of three broad categ...

Motion planning consists of finding a sequence of motions for a robot (movable object) to move from a start configuration to a goal configuration without colliding with any obstacle. PRMs create a roadmap that paths can be extracted from. To create the roadmap, nodes are generated in the environment at random. Each node represents a possible position the robot can move to. Only feasible nodes a...

The basic problem of computing a collision-free path for a robot among known obstacles is well-understood and reasonably well-solved; however, deficiencies in the problem formulation itself and the demand of engineering challenges in the design of autonomous systems raise important questions and topics for future research. The shortcomings of basic path planning become clearly visible when cons...