For autonomous robots it may seem like we can avoid needing the ability to make maps automatically. That is, it is sometimes assumed that a robot should be able to take for granted the a priori availability of a map. Unfortunately, this is rarely the case. Not only do architectural blueprints or related types of maps fail to be consistently reliable (since even during construction they are not always updated to reflect necessary alternations), but, furthermore, numerous aspects of an environment are not likely to appear on a map, such as tables, chairs, and transitory objects.

Although the vast majority of mobile robotic systems involve a single robot operating alone in its environment, a growing number of researchers are considering the challenges and potential advantages of having a group of robots cooperate in order to complete some required task. For some specific robotic tasks, such as exploring an unknown planet [374], search and rescue [812], pushing objects [608], [513], [687], [821], or cleaning up toxic waste [609], it has been suggested that rather than send one very complex robot to perform the task it would more effective to send a number of smaller, simpler robots.

Before delving into the harsh realities of real robots, it is worthwhile exploring some of the computational tasks that are associated with an autonomous system. This chapter provides a taste (an amuse bouche, if you will) of some of the computational problems that will be considered in later chapters. Here, these problems are considered in their simplest form, and many of the realities of autonomous systems are ignored. Rest assured, the full complexity of the problems are considered in later chapters.

Given the current state of mobile robotics, what can be considered essentially solved and what tasks remain? It is clear that for restrictive environments and for limited tasks, autonomous systems can be readily developed. Tasks such as parts delivery in a warehouse, materials transport in hospitals, limited autonomous driving, and so on can all be “solved” for tight definitions of the task and provided that sometime restrictive assumptions can be made concerning the environment.

Although many mobile robot systems are experimental in nature, systems devoted to specific practical applications are being developed and deployed. This chapter examines some of the tasks for which mobile robotic systems are beginning to appear and describes several existing experimental and production systems that have been developed.

For many tasks, a mobile robot needs to know “where it is” either on an ongoing basis or when specific events occur. A robot may need to know its location in order to be able to plan appropriate paths or to know if the current location is the appropriate place at which to perform some operation. Knowing “where the robot is” has many different connotations. In the strongest sense, “knowing where the robot is” involves estimating the location of the robot (in either qualitative or quantitative terms) with respect to some global representation of space: we refer to this as strong localization.

The use of machine learning in robotics is a vast and growing area of research. In this chapter we consider a few key variations using: the use of deep neural networks, the applications of reinforcement learning and especially deep reinforcement learning, and the rapidly emerging potential for large language models.

Although the vast majority of mobile robotic systems involve a single robot operating alone in its environment, a growing number of researchers are considering the challenges and potential advantages of having a group of robots cooperate in order to complete some required task. For some specific robotic tasks, such as exploring an unknown planet [374], search and rescue [812], pushing objects [608], [513], [687], [821], or cleaning up toxic waste [609], it has been suggested that rather than send one very complex robot to perform the task it would more effective to send a number of smaller, simpler robots. Such a collection of robots is sometimes described as a swarm [81], a colony [255], or a collective [436], or the robots may be said to exhibit cooperative behavior [607].

Robots in fiction seem to be able to engage in complex planning tasks with little or no difficulty. For example, in the novel 2001: A Space Odyssey, HAL is capable of long-range plans and reasoning about the effects and consequences of his actions [167]. It is indeed fortunate that fictional autonomous systems can be presented without having to specify how such devices represent and reason about their environment. Unfortunately, real autonomous systems often make explicit internal representations and mechanisms for reasoning about them.