The Mobile Robot RHINO

AI Magazine Volume 16 Number 2 (1995) (© AAAI) Articles The Mobile Robot RHINO Joachim Buhmann, Wolfram Burgard, Armin B. Cremers, Dieter Fox, Thomas Hofmann, Frank E. Schneider, Jiannis Strikos, and Sebastian Thrun ■ RHINO was the University of Bonn’s entry in the learning is employed to interpret sonar mea- 1994 AAAI Robot Competition and Exhibition. surements. RHINO is a mobile robot designed for indoor navi- Real-time operation: To act continuously gation and manipulation tasks. The general scien- in real time, any-time solutions (Dean and tific goal of the RHINO project is the development Boddy 1988) are employed wherever possible. and the analysis of autonomous and complex Any-time algorithms are able to make deci- learning systems. This article briefly describes the major components of the RHINO control software sions regardless of the time spent for compu- as they were exhibited at the competition. It also tation. The more time that is available, how- sketches the basic philosophy of the RHINO archi- ever, the better the results are. tecture and discusses some of the lessons that we Reactive control and deliberation: RHINO’s learned during the competition. navigation system integrates a fast, reactive on-board obstacle-avoidance routine with knowledge- and computation-intense map HINO, shown in figure 1, is a B21 mobile building and planning algorithms. robot platform manufactured by Real- RHINO’s software consists of a dozen differ- RWorld Interface. It is equipped with 24 ent modules. The interface modules (a base- sonar proximity sensors, a dual-color camera sonar sensor interface, a camera interface, and system mounted on a pan-tilt unit, and 2 on- a speech interface) control the basic commu- board I486 computers. Sonar information is nication to and from the hardware compo- obtained at a rate of 1.3 hertz (Hz), and cam- nents of the robot. On top of these, a fast era images are processed at a rate of 0.7 Hz. obstacle-avoidance routine analyzes sonar RHINO communicates with external computers measurements to avoid collisions with obsta- (two Sun SPARCSTATIONs) by a tetherless Ether- cles and walls at a speed as high as 90 cen- net link. timeters a second. Global metric and topolog- The RHINO project is generally concerned ical maps are constructed on the fly using a with the design of autonomous and complex neural network–based approach combined learning systems (Thrun 1994). The 1994 with a database of maps showing typical AAAI Robot Competition and Exhibition, rooms, doors, and hallways. RHINO employs a sponsored by the American Association for dynamic programming planner to explore Artificial Intelligence (AAAI), ended an initial unknown terrain and navigate to arbitrary six-month period of software design. Key fea- target locations. It locates itself by continu- tures of RHINO’s control software, as exhibited ously analyzing sonar information. In addi- at the competition, are as follows: tion, a fast vision module segments images Autonomy: RHINO operates completely from two color cameras to find target objects autonomously. It has been operated repeated- and obstacles that block the path of the ly for durations as long as one hour in popu- robot. RHINO’s control flow is monitored by an lated office environments without human integrated task planner and a central user intervention. interface. Learning: To increase the flexibility of the The integration of a dozen different soft- software, learning mechanisms support the ware modules, which all exhibit different tim- adaptation of the robot to its sensors and the ing and response characteristics, requires a environment. For example, neural network flexible scheme for the flow and synchroniza- Copyright © 1995, AAAI. All rights reserved. 0738-4602-1994 / $2.00 SPRING 1994 31 Articles ule, (2) the modules concerned with sensor interpretation and map building, (3) the planner and explorer, and (4) the visual rou- tines. The article concludes with a discussion that highlights some of the lessons that were learned during the AAAI competition. Fast Obstacle Avoidance The obstacle avoidance runs on board, inde- pendent of other software components such as the planner. Every 0.25 seconds, a new velocity and motion direction are chosen according to the most recent sonar measurements. To rapidly adapt to new situations, only the last three sonar sweeps are considered. RHINO can react immediately to changes in the environment and hard-to-see and moving obstacles such as humans. The obstacle-avoidance module controls both the velocity and the motion direction of the robot. At every instant in time, the velocity is determined such that no collision will occur within the next two seconds (two-second rule). The motion direction is determined based on target points, which are gen- erated by the planner (see discussion later). To reach a given target, the robot can choose among different trajectories on which it will travel with different velocities. RHINO selects Figure 1. The RHINO Robot from the its motion direction by maximizing its trans- University of Bonn. lational velocity, denoted by v, while mini- mizing the angle to the target point, denoted by θ. tion of information. The key principles for To determine v, a simplified model of the the design of RHINO’s software are as follows: robot’s environment is constructed. Proximi- Distributed control and communication: ty information, obtained from RHINO’s sonar Each module communicates with several oth- sensors, is used to construct a two-dimension- er modules through Ethernet (Fedor 1993). al obstacle line field. Every sonar reading is There is no single control unit, and commu- converted to a line in this field, as depicted in nication is not centralized. figure 2. To avoid collisions with obstacles, Asynchronous communication: RHINO’s the obstacle-avoidance routine considers a software lacks a central clock. Each of the variety of circular trajectories, one of which is modules runs independently of the other shown in figure 2. For each trajectory, the dis- modules. To resolve conflicts, certain mod- tance between the robot and the closest ules (such as the on-board obstacle-avoidance obstacle line along the projected trajectory is module) can take priority over other modules computed. This distance determines the (such as the planner) in determining the translational velocity v, according to the two- robot’s motion direction. second rule. The projected angle to the target Software fault tolerance: RHINO’s software point, θ, is calculated for the estimated robot is designed to accommodate sudden failures position and orientation after 0.25 seconds. of most of its software components. Almost For both values v and θ, a smoothed his- all modules can be stopped and restarted at togram is constructed. Because of the dynam- any time. Effective mechanisms ensure that ic constraints, only a small number of trajec- restarted modules will immediately obtain tories are reachable within the next 0.25 the currently available global information. seconds and are consequently considered in The following sections present some of the the histogram. Finally, the trajectory that key components of the RHINO approach in maximizes a weighted difference of v and θ is more detail: (1) the obstacle-avoidance mod- chosen. To increase the safety of the robot, a 32 AI MAGAZINE Articles security distance of 10 centimeters is kept to surrounding objects. This security distance is increased to as much as 30 centimeters as the robot’s velocity increases. RHINO’s obstacle-avoidance approach is easi- ly extendible to other sensors. For example, prior to the competition, we successfully employed camera information to identify small obstacles on the floor that block the path of the robot, as described later. Each visually detected obstacle is mapped into a few lines in the obstacle field, much like the sonar information described earlier. However, visual information was not used by the obstacle-avoidance routine during the AAAI competition, basically because sonar information was fast and accurate enough in the competition ring. Map Building and Position Control RHINO’s global-navigation system builds and uses occupancy maps of the robot’s environ- Figure 2. Obstacle Line Field. ment. More specifically, when traveling through possibly unknown terrain, RHINO Each sonar reading is indicated by a line centered around the robot. interprets its sonar readings to generate a The trajectory, which is finally chosen by RHINO, is also shown. two-dimensional, discrete probabilistic occupancy map. Sonar sensors are interpreted using an artificial neural network, which esti- been found to be effective for the detection mates the likelihood of occupancy of any of rotational errors at the competition as well point in a three-meter circle around the robot as in various office environments. If RHINO (Thrun 1993). Multiple measurements are operates some 30 minutes with velocities as integrated using Bayesian inference (Moravec high as 90 centimeters a second in unknown 1993). Figure 3a shows a map that was con- terrain, the total error is usually smaller than structed while we manually steered the robot 30 centimeters. Without correcting the dead through the competition arena. This map reckoning, this error often accumulates as describes an area of approximately 30 x 20 much as 30 meters. meters. The hallways, rooms, large obstacles, To obtain topological information concern- and doors can clearly be recognized. ing the location of rooms, doors, and hall- To navigate based on global metric infor- ways, RHINO analyzes its metric occupancy mation, it is imperative that the robot be able map continuously. Walls are identified by to locate itself accurately in its map. RHINO is thresholding. A large database of examples of equipped with fairly accurate wheel encoders. door regions, hallways, and rooms (and parts However, even small angular errors in dead thereof) is continuously matched to assign reckoning can have devastating effects on the topological labels to the unoccupied areas in internal position estimation.

Load more