14 IEEE JOURNAL OF ROBOTICS AND AUTOMATION, VOL. RA-2, NO. I, MARCH 1986 A Robust Layered Control Syste For A Mobile Robot Abstract--A new architecture for controlling mobile robots is de- scribed. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks. I. INTRODUCTION A CONTROL SYSTEM for a completely autonomous mobile robot must perform many complex information processing tasks in real time. It operates in an environment where the boundary conditions (viewing the instantaneous control problem in a classical control theory formulation) are changing rapidly. In fact the determination of those boundary conditions is done over very noisy channels since there is no straightforward mapping between sensors (e.g. TV cameras) and the form required of the boundary conditions. The usual approach to building control systems for such robots is to decompose the problem into a series (roughly) of functional units as illustrated by a series of vertical slices in Fig. 1. After analyzing the computational requirements for a mobile robot we have decided to use task-achieving behav- iors as our primary decomposition of the problem. This is illustrated by a series of horizontal slices in Fig. 2. As with a functional decomposition, we implement each slice explicitly then tie them all together to form a robot control system. Our new decomposition leads to a radically different architecture for mobile robot control systems, with radically different implementation strategies plausible at the hardware level, and with a large number of advantages concerning robustness, buildability and testability. Manuscript revised February 3, 1986. This work was supported in part by an IBM Faculty Development Award, in part by a grant from the Systems Development Foundation, in part by an equipment grant from Motorola, and in part by the Advanced Research Projects Agency under Office of Naval Research contracts N00014-80-C-0505 and N00014-82-K-0334. The author is with the Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 545 Technology Square, Cambridge, MA 02139, USA. IEEE Log Number 8608069. Fig. 1. Traditional decomposition of a mobile robot control system into functional modules. reason about behavior of objects plan changes to the world identify objects monitor changes Sensors __+ ---b Actuators build maps explore wander avoid objects Fig. 2. Decomposition of a mobile robot control system based on task- achieving behaviors. A. Requirements We can identify a number of requirements of a control system for an intelligent autonomous mobile robot. They each put constraints on possible control systems that we may employ. They are identified as follows. Multiple Goals: Often the robot will have multiple goals, some conflicting, which it is trying to achieve. It may be trying to reach a certain point ahead of it while avoiding local obstacles. It may be trying to reach a certain place in minimal time while conserving power reserves. Often the relative importance of goals will be context-dependent. Getting off the railroad tracks when a train is heard becomes much more important than inspecting the last ten track ties of the current track section. The control system must be responsive to high priority goals, while still servicing necessary “low-level’’ goals (e.g., in getting off the railroad tracks, it is still important that the robot maintains its balance so it doesn’t fall down). Multiple Sensors: The robot will most likely have multiple sensors (e.g., TV cameras, encoders on steering and drive mechanisms, infrared beacon detectors, an inertial navigation 08824967/86/0300-0014$01 .OO O 1986 IEEEIEEE JOURNAL OF ROBOTICS AND AUTOMATION, VOL. RA-2, NO. 1, MARCH 1986 15 system, acoustic rangefinders, infrared rangefinders, access to a global positioning satellite system, etc.). All sensors have an error component in their readings. Furthermore, often there is no direct analytic mapping from sensor values to desired physical quantities. Some of the sensors will overlap in the physical quantities they measure. They will often give inconsistent readings-sometimes due to normal sensor error and sometimes due to the measurement conditions being such that the sensor (and subsequent processing) is used outside its domain of applicability. Often there will be no analytic characterization of the domain of applicability (e.g. under what precise conditions does the Sobel operator return valid edges?). The robot must make decisions under these condi- tions. Robustness: The robot ought to be robust. When some sensors fail it should be able to adapt and cope by relying on those still functional. When the environment changes drasti- cally it should be able to still achieve some modicum of sensible behavior, rather then sit in shock or wander aimlessly and irrationally around. Ideally it should also continue to function well when there are faults in parts of its processor(s). Extensibility: As more sensors and capabilities are added to a robot it needs more processing power; otherwise, the original capabilities of the robot will be impaired relative to the flow of time. B. Other Approaches Multiple Goals: Elfes and Talukdar [4] designed a control language for Moravec’s robot [ 111, which tried to accommo- date multiple goals. It mainly achieved this by letting the user explicitly code for parallelism and to code an exception