The authors take on the difficult task of presenting, in a concise and detailed form, a central aspect of the motion planning problem, that is, the computation of movements away from the boundaries of obstacles and environment, which goes under the name of gross motion planning. This area has traditionally been the domain of motion planning from an AI perspective, whereas fine motion planning, which deals with movements that have a dynamic interaction with the environment, is mostly studied from the point of view of real-time control, because of its strong dependency on sensing and control. This paper is a comprehensive summary of most of the available algorithms for gross motion planning. The good organization of the abundant material makes it particularly worthwhile reading for people who want to quickly learn the concepts, terminology, and tools of gross motion planning, before undertaking the study of a specific research area.

The paper is organized in four major sections. The first presents the basic aspects of the motion planning problem. The authors then discuss general methods for solving it. Next, they present how these methods have been implemented by different researchers for specific cases. The conclusion gives helpful suggestions on how to develop efficient algorithms. An important feature of this paper is the description, within the limits of the available data, of the complete development of a solution to a gross motion planning problem, from its formulation, through the choice of problem representation, to its performance data. Overall, the quality of the paper is good, and the level of the presentation is accessible to most readers. Each section is structured according to a taxonomy of the problem, which helps the authors make correspondences and identify analogies among the various algorithms, although, at times, it tends to slow down the presentation. The authors cover most of the issues in gross motion planning, with the exception of a few missing references, particularly in the areas of planning under uncertainty and constructive geometry methods for motion planning.

Of the four sections composing the paper, the first is a brief introduction followed by a presentation of the nature of the motion planning problem. The authors explain the terminology of the problem and review the relevant concepts of computer science. Section 2 summarizes the different tools that can be used in solving a motion planning problem. Section 3 is the main part of the paper, where the authors list and discuss the main approaches presented in the literature on motion planning. The presentation is summarized at the end by a set of tables listing each paper discussed in terms of the approach used and of its applicability to a specific motion planning problem. In the conclusion, the authors emphasize the importance of developing more practical algorithms for motion planning.

In general, the authors’ writing style is clear and easy to follow. Good explanations are given of the main features of the various algorithms, although references to a particular approach are mainly provided when that approach is discussed, and not in the introductory sections. The bibliography at the end of the paper, covering most of the significant work in gross motion planning, is particularly useful.