Sensor based planning. I. The generalized Voronoi graph

This paper introduces a 1-dimensional network of curves termed the generalized Voronoi graph (GVG) and its extension, the hierarchical generalized Voronoi graph (HGVG), which can be used as a basis for a roadmap or retract-like structure. The GVG and HGVG provide a basis for sensor based path planning in an unknown static environment. In this paper, the GVG and HGVG are defined and some of their properties are exploited to show their utility for motion planning. A companion paper describes how to use the GVG and HGVG for the purposes of sensor based planning

Sensor based planning. II. Incremental construction of the generalized Voronoi graph

This paper prescribes an incremental procedure to construct the generalized Voronoi graph (GVG) and the hierarchical generalized Voronoi graph (HGVG) detailed in the companion paper. The procedure requires only local distance sensor measurements, and therefore the method can be used as a basis for sensor based planning algorithms

Sensor based planning and nonsmooth analysis

This paper describes some initial steps towards sensor based path planning in an unknown static environment. The method is a based on a sensor-based incremental construction of a one-dimensional retract of the free space. In this paper we introduce a retract termed the generalized Voronoi graph, and also analyze the roadmap of Canny and Lin's opportunistic path planner (1990, 1993). The bulk of this paper is devoted to the application of nonsmooth analysis to the Euclidean distance function. We show that the distance function is in fact nonsmooth at the points which are required to construct the plan. This analysis leads directly to the incorporation of simple and realistic sensor models into the planning scheme

Design and Sensor-Based Control for Hyper-Redundant Mechanisms

Toxic materials in DOE sites pose a significant threat to DOE personnel who must inspect these locations. Working in confined spaces further complicates the situation especially when the workers must wear heavy and cumbersome protective suits. A robot or conventional mechanism can clearly bypass the danger and perhaps expedite the characterization process because the person is removed from the site and neither the site nor the person require preparation. However, conventional robots are not suitable for these inspection tasks because they are not flexible enough to pass through and into target DOE inspection sites. This effort is developing an articulated probe, called a hyper redundant mechanism, which is a snake-like device that can exploit its many internal degrees of freedom to thread through tightly packed volumes transmitting images and data from remote locations inaccessible to conventional robots and people. This effort contains two parts: mechanism development and control of the device

Design and Sensor-Based COntrol for Hyper-Redundant Mechanisms

Toxic materials in DOE sites pose a significant threat to DOE personnel who must inspect these locations. Working in confined spaces further complicates the situation especially when the workers must wear heavy and cumbersome protective suits

A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding

Conventional multi-agent path planners typically compute an ensemble of paths while optimizing a single objective, such as path length. However, many applications may require multiple objectives, say fuel consumption and completion time, to be simultaneously optimized during planning and these criteria may not be readily compared and sometimes lie in competition with each other. Naively applying existing multi-objective search algorithms, such as multi-objective A* (MOA*), to multi-agent path finding may prove to be inefficient as the size of the space of possible solutions, i.e., the Pareto-optimal set, can grow exponentially with the number of agents (the dimension of the search space). This article presents an approach named Multi-Objective Conflict-Based Search (MO-CBS) that bypasses this so-called curse of dimensionality by leveraging prior Conflict-Based Search (CBS), a well-known algorithm for single-objective multi-agent path finding, and principles of dominance from multi-objective optimization literature. We also develop several variants of MO-CBS to further improve its performance. We prove that MO-CBS and its variants are able to compute the entire Pareto-optimal set. Numerical results show that MO-CBS outperforms both MOA* as well as MOM*, a recently developed state-of-the-art multi-objective multi-agent planner.Comment: 11 pages, preliminary version published in ICRA 2021, journal version submitte

Mobile robot navigation: issues in implementating the generalized Voronoi graph in the plane

This paper describes the procedures that are required to implement, on a conventional mobile robot, a sensor based motion planning algorithm based on the generalized Voronoi graph (GVG). The GVG is a roadmap of a static environment, and we describe how to incrementally construct this roadmap using only range information in an unknown environment. The GVG may then be used to guide future excursions into the explored environment. Experimental results validate the utility of this work