Sensors, Navigation Agents and Arbitration
12
C H A P T E R
Different environments offer different types of navigation features, and it is impor- tant that a robot be able to adapt to these differences on the fly. There is no reason why a robot cannot switch seamlessly from satellite navigation to lidar navigation, or to sonar navigation, or even use multiple methods simultaneously. Even a single- sensor system may be used in multiple ways to look for different types of features at the same time.
The advantages to physical paths are their technical simplicity and their relative ro- bustness, especially in feature-challenged environments. The sensor system itself is usually quite inexpensive, but installing and maintaining the path often is not. Other disadvantages to physical paths include the lack of flexibility and in some cases cos- metic issues associated with the path.
A less obvious disadvantage for physical paths is one that would appear to be a strength:
their precise repeatability. This characteristic is particularly obvious in upscale envi- ronments like offices, where the vehicle’s wheels quickly wear a pattern in the floor.
Truly autonomous vehicles will tend to leave a wider and less obvious wear footprint, and can even be programmed to intentionally vary their lateral tracking.
Sonar
Most larger indoor robots have some kind of sonar system as part of their collision avoidance capability. If this is the case, then any navigational information such a system can provide is a freebee. Most office environments can be navigated exclu- sively by sonar, but the short range of sonar sensors and their tendency to be specularly reflected means that they can only provide reliable navigation data over limited angles and distances.
There are two prevalent sonar technologies: Piezo-electric and electrostatic. Piezo- electric transducers are based on crystal compounds that flex when a current passes through them and which in turn produce a current when flexed. Electrostatic trans- ducers are capacitive in nature. Both types require a significant AC drive signal (from 50 to 350 volts). Electrostatic transducers also require a high-voltage bias during de- tection (several hundred volts DC). This bias is often generated by rectifying the drive signal.
Both of these technologies come in different beam patterns, but the widest beam widths are available only with Piezo-electric transducers. Starting in the 1980s the Polaroid Corporation made available the narrow beam Piezo-electric transducer and ranging module that they had developed for the company’s cameras. The cost of this combi- nation was very modest, and it quickly gained popularity among robot designers.
The original Polaroid transducer had a beam pattern that was roughly 15 degrees wide and conical in cross-section. As a result of the low cost and easy availability, a great number of researchers adopted this transducer for their navigation projects. To many of these researchers, the configuration in which such sensors should be deployed seemed obvious. Since each transducer covered 15 degrees, a total of 24 transducers
would provide the ability to map the entire environment 360 degrees around the robot.
The obvious way is usually a trap!
Sonar
Ring 15o
The Polaroid “ring” configuration ignored the principles of proper sensor deployment.
Figure 12.1. The ubiquitous “Polaroid ring” sonar configuration
The most obvious weakness of the narrow-beam ring configuration is that it offers little useful information for collision avoidance as shown in Figure 12.2. This means that the cost of the ring must be justified entirely by the navigation data it returns.
From a navigation standpoint, the narrow beam ring configuration represents a classic case of static thinking. The reasoning was that the robot could simply remain sta- tionary, activate the ring, and instantly receive a map of the environment around it.
Unfortunately, this is far from the case. Sonar, in fact, only returns valid ranges in two ways: normal reflection and retroreflection.
Figure 12.2. Vertical coverage of a narrow-beam ring
90o
Convex Object
90o
Wall (flat vertical surface)
Normal reflection Retro-reflection
Normal reflection
Sonar Ring
1 6
5 4 3 2
Phantom Object
Table
Steps
Figure 12.3 shows two cases of normal reflection, one from a flat surface and one from a convex object. Also shown is a case of retroreflection from a corner. Retroreflection can occur for a simple corner (two surfaces) or a corner-cube. In the case of a simple corner such as in Figure 12.3, the beam will only be returned if the incidence angle in the vertical plane is approximately 90 degrees.
Unless the wall surface contains irregularities larger than one-half the wavelength of the sonar frequency (typically 1 to 3 cm), sensors 2, 3, and 5 will “go specular” and not return ranges from it. Interestingly, beam 2 returns a phantom image of the post due to multiple reflections.
Sonar Ring
Wall
Wall Corner
Post
Phantom of post
Figure 12.4. Sonar “map” as returned by narrow-beam sonar ring Figure 12.4 approximates the sonar data returned from our hypothetical environment in Figure 12.3. If the data returned by lidar (Figure 10.2) seemed sparse, then the data from sonar would appear to be practically nonexistent.
Flashback…
I remember visiting the very impressive web site of a university researcher working with a sonar ring configuration. The site contained video clips of the robot navigating uni- versity hallways and showed the sonar returns along the walls ahead. The robot was successfully imaging and mapping oblique wall surfaces out to more than three meters.
The signal processing and navigation software were very well done, and it appeared that the configuration had somehow overcome the specular reflection problems of sonar. I was very puzzled as to how this could possibly be and found myself doubting my own experience.
Some months later, I was visiting the university and received an invitation to watch the robot in action. As soon as I exited the stairwell into the hall, the reason for the apparent suspension of the laws of nature became crystal clear. The walls of the hall were covered with a decorative concrete facade that had half-inch wide grooves run- ning vertically on three-inch centers. These grooves formed perfect corner reflectors for the sonar. This is yet another example of a robot being developed to work well in one environment, but not being able to easily transition to other environments.
It would appear from Figure 12.4 that sonar navigation would be nearly impossible given so little data. Nothing could be further from the truth. The fact is that the two wall reflections are very dependable and useful. Since paths will normally run parallel to walls, we might be tempted to orient two or more sensors parallel on each side of the robot to obtain a heading fix from a wall. This would be another case of static think- ing, and is far from the best solution. For one thing, with only two readings it would be almost impossible to filter out objects that did not represent the wall.
On the other hand, if the robot moves roughly parallel to one of these walls record- ing the ranges from normal reflections of a single beam, it can quickly obtain a very nice image of a section of the wall. This image can be used to correct the robot’s heading estimate and its lateral position estimate, and can contain the number of points required to assure that it is really the wall.
The first bad assumption of the ring configuration is, therefore, that data in all dir- ections is of equal value. In fact, data from the sides forward is usually of the most potential navigational importance, followed by data from the front. Since sonar echoes take roughly 2 ms per foot of range, and since transducers can interfere with each other, it is important that time not be wasted pinging away in directions where there is little likelihood of receiving meaningful data. Worse, as we will see, these useless
The second bad assumption of the ring configuration is that the narrow-beam of the transducer provides the angular resolution for the range data returned. This may be true, but 15 degrees is far too wide to achieve reasonable spatial resolution.
The robot in Figure 12.4 can, in fact, determine a very precise vector angle for the reflection from the wall. The trick is for the robot to have a priori knowledge of the position and azimuth of the wall. If paths are programmed parallel to walls, then the robot can imply this from the programmed azimuth of the path. The navigation pro- gram therefore knows that the echo azimuth is 90 degrees to the wall in real space.
This is not to imply that the Polaroid ranger itself is not a useful tool for robot col- lision avoidance and navigation. Configurations of these rangers that focus on the cardinal directions of the robot have been used very successfully by academic researchers and commercial manufacturers of indoor mobile robots. Polaroid has also introduced other transducers and kits that have been of great value to robotics developers.
For Cybermotion robots, on the other hand, we selected wide-beam Piezo-electric transducers. Our reasoning in this selection was that the resolution would be provided by a priori knowledge, and that the wider beam width allowed us to image a larger volume of space per ping. The signal of a sonar system is reduced with the square of the pattern radius, so the 60-degree beam pattern we selected returned 1/16th the energy of the 15-degree transducers. To overcome the lower signal-to-noise rations resulting from wide-beam widths, we used extensive digital signal processing.
A final warning about the use of sonar is warranted here. In sonically hard environ- ments, pings from transducers can bounce around the natural corner reflectors formed by the walls, floor, and ceiling and then return to the robot long after they were ex- pected. Additionally, pings from other robots can arrive any time these robots are nearby. The signal processing of the sonar system must be capable of identifying and ignoring these signals. If this is not done, the robot will become extremely paranoid, even stopping violently for phantoms it detects. Because of the more rapid energy dispersal of wide-beam transducers, they are less susceptible to this problem.
Thus, the strengths of sonar are its low cost, ability to work without illumination, low power, and usefulness for collision avoidance. The weaknesses of sonar are its slow time of flight, tendency toward specular reflection, and susceptibility to inter- ference. Interference problems can be effectively eliminated by proper signal processing, and sonar can be an effective tool for navigation in indoor and a few outdoor appli- cations. Sonar’s relatively slow time of flight, however, means that it is of limited appeal for vehicles operating above 6 to 10 mph.
Fixed light beams
The availability of affordable lidar has reduced the interest in fixed light beam sys- tems, but they can still play a part in low-cost systems, or as adjuncts to collision avoid- ance systems in robots. Very inexpensive fixed-beam ranging modules are available that use triangulation to measure the range to most ordinary surfaces out to a meter or more. Other fixed-beam systems do not return range information, but can be used to detect reflective targets to ten meters or more.
Lidar
Lidar is one of the most popular sensors available for robot navigation. The most popular lidar systems are planar, using a rotating mirror to scan the beam from a solid-state laser over an arc of typically 180 degrees or more. Lidar systems with nodding mirrors have been developed that can image three-dimensional space, but their cost, refresh rate, and reliability have not yet reached the point of making them popular for most robot designs.
The biggest advantage of lidar systems is that they can detect most passive objects over a wide sweep angle to 10 meters or more and retroreflective targets to more than 100 meters. The effective range is even better to more highly reflective objects. The refresh (sweep) rate is usually between 1 and 100 Hz and there is a trade-off between angular resolution and speed. Highest resolutions are usually obtained at sweep rates of 3 to 10 Hz. This relatively slow sweep rate places some limitations on this sensor for high-speed vehicles. Even at low speed (less than 4.8 km ph/3 mph), the range readings from a lidar should be compensated for the movement of the vehicle and not taken as having occurred at a single instant.
Figure 12.5. Sick LMS lidar
(Courtesy of Sick Corp.)
The disadvantages of lidar systems are their cost, significant power consumption (typically over 10 watts), planar detection patterns, and mechanical complexity.
Early lidar systems were prone to damage from shock and vibration; however, more recent designs like the Sick LMS appear to have largely overcome these issues. Early designs also tended to use ranging techniques that yielded poor range-resolution at longer ranges. This limitation is also far less prevalent in newer designs.
Radar imaging
Radar imaging systems based on synthetic beam steering have been developed in re- cent years. These systems have the advantage of extremely fast refresh of data over a significant volume. They are also less susceptible to some of the weather conditions that affect lidar, but exhibit lower resolution. Radar imaging also exhibits specular reflections as well as insensitivity to certain materials. Radar can be useful for both collision avoidance and feature navigation, but not as the sole sensor for either.
Video
Video processing has made enormous strides in recent years. Because of the popular- ity of digital video as a component of personal computers, the cost of video digitizers and cameras has been reduced enormously.
Video systems have the advantages of low cost and modest power consumption. They also have the advantage of doubling as remote viewers. The biggest disadvantages of video systems are that the environment must be illuminated and they offer no inher- ent range data. Some range data can be extrapolated by triangulation using stereoscopic or triscopic images, or by techniques based on a priori knowledge of features.
The computational overhead for deriving range data from a 2D camera image de- pends on the features to be detected. Certain surfaces, like flat mono-colored walls can only be mapped for range if illuminated by a structured light. On the other hand, road following has been demonstrated from single cameras.
GPS
The Global Positioning System (GPS) is a network of 24 satellites that orbit the earth twice a day. With a simple and inexpensive receiver, an outdoor robot can be provided with an enormously powerful navigation tool. A GPS receiver provides continuous position estimates based on triangulation of the signals it is able to receive. If it can receive three or more signals, the receiver can provide latitude and longitude. If it
can receive four or more signals, it can provide altitude as well. Some services are even available that will download maps for the area in which a receiver is located.
The biggest disadvantage to GPS is the low resolution. A straight GPS receiver can only provide a position fix to within about 15 meters (50 feet). In the past, this was improved upon by placing a receiver in a known location and sending position cor- rections to the mobile unit. This technique was called differential GPS. Today there is a service called WAAS or Wide Area Augmentation System. A WAAS equipped receiver automatically receives corrections for various areas from a geostationary satellite. Even so, the accuracy of differential GPS or a WAAS equipped GPS is 1 to 3 meters. At this writing, WAAS is only available in North America.
The best accuracy of a WAAS GPS is not adequate to produce acceptable lane tracking on a highway. For this reason, it is best used with other techniques that can mini- mize the error locally.
Thus, the advantages of the GPS include its low cost, and its output of absolute posi- tion. The biggest disadvantages are its accuracy and the fact that it will not work in most indoor environments. In fact, overhead structures, tunnels, weather, and even heavy overhead vegetation can interfere with GPS signals.