This paper surveys recent developments in motion planning and related geometric algorithms, a theoretical research area that has grown rapidly in response to increasing industrial demand for automatic manufacturing systems which use robotic manipulators and sensory feedback devices, and, more significantly, in anticipation of a future generation of substantially more autonomous and intelligent robots. These future robots are expected to possess advanced capabilities of sensing, planning, and control, enabling them to gather knowledge about their environment, construct a symbolic world model of the environment, and use this model in planning and carrying out tasks set to them in high-level style by an application programmer.
Current research in theoretical robotics therefore aims to identify these basic capabilities that an autonomous intelligent robot system will need to advance understanding of the mathematical and algorithmic principles fundamental to these capabilities.
Among these capabilities, planning involves the use of an environment model to carry out significant parts of a robot's activities automatically. The aim is to allow the robot's user to specify a desired activity in very high-level general terms, and then have the system fill in the missing low-level details. For example, the user might specify the end product of some assembly process, and ask the system to construct a sequence of assembly substeps, or, at a less demanding level, to plan collision-free motions which pick up individual subparts of an object to be assembled, transport them to their assembly position, and insert them into their proper places.
Techniques for the automatic planning of robot motions have advanced