Page 100 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

MOTION PLANNING FOR A MOBILE ROBOT 75

avoiding many obstacles, literally and figuratively, on their way. They had no
maps. Sometimes along the way they created maps, and sometimes maps were
created by those who followed them. This suggests that one does not have to
know everything about the scene in order to solve the go-from-A-to-B motion
planning problem. By always knowing one’s position in space (recall the careful
triangulation of stars the seaman have done), by keeping in mind where the
target position is relative to one’s position, and by remembering two or three key
locations along the way, one should be able to infer some important properties
of the space in which one travels, which will be sufficient for getting there. Our
goal is to develop strategies that make this possible.
Note that the task we pose to the robot does not include producing a map of
the scene in which it travels. All we ask the robot to do is go from point A to
point B, from its current position to some target position. This is an important
distinction. If all I need to do is find a specific room in an unfamiliar building, I
have no reason to go into an expensive effort of creating a map of the building.
If I start visiting the same room in that same building often enough, eventually I
will likely work out a more or less optimal route to the room—though even then
I will likely not know of many nooks and crannies of the building (which would
have to appear in the map). In other words, map making is a different task that
arises from a different objective. A map may perhaps appear as a by-product of
some path planning algorithm; this would be a rather expensive way to do path
planning, but this may happen. We thus emphasize that one should distinguish
between path planning and map making.
Assuming for now that sensor-based planning algorithms are viable and com-
putationally simple enough for real-time operation and also assuming that they
can be extended to more complex cases—nonpoint (physical) robots, arm manip-
ulators, and complex nontactile sensing—the SIM paradigm is clearly very
attractive. It is attractive, first of all, from the practical standpoint:

1. Sensors are a standard fare in engineering and robot technology.
2. The SIM paradigm captures much of what we observe in nature. Humans
and animals solve complex motion planning tasks all the time, day in
and day out, while operating with local sensing information. It would be
wonderful to teach robots to do the same.
3. The paradigm does away with complex gathering of information about the
robot’s surroundings, replacing it with a continuous processing of incoming
sensor information. This, in turn, allows one not to worry about the shapes
and locations of obstacles in the scene, and perhaps even handle scenes
with moving or shape-changing obstacles.
4. From the control standpoint, sensor-based motion planning introduces the
powerful notion of sensor feedback control, thus transforming path plan-
ning into a continuous on-line control process. The fact that local sensing
information is sufficient to solve the global task (which we still need to
prove) is good news: Local information is likely to be simple and easy to
process.