A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations

Motion planning is one of the most important areas of robotics research. The complexity of the motion-planning problem has hindered the development of practical algorithms. This paper surveys the work on gross-motion planning, including motion planners for point robots, rigid robots, and manipulators in stationary, time-varying, constrained, and movable-object environments. The general issues in motion planning are explained. Recent approaches and their performances are briefly described, and possible future research directions are discussed.

Gross motion planning—a survey

Kuipers, B. and Byun, Y.T., A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations, Robotics and Autonomous Systems, 8 (1991) 47-63. We have developed a robust qualitative method for robot exploration, mapping, and navigation in large-scale spatial environments. Experiments with a simulated robot in a variety of complex 2D environments have demonstrated that our qualitative method can build an accurate map of a previously unknown environment in spite of substantial random and systematic sensorimotor error. Most current approaches to robot exploration and mapping analyze sensor input to build a geometrically precise map of the environment, then extract topological structure from the geometric description. Our approach recognizes and exploits qualitative properties of large-scale space before relatively error-prone geometrical properties. [sensorimotor ~ control] ~ topology ---, geometry At the control level, distinctive places and distinctive travel edges are identified based on the interaction between the robot's control strategies, its sensorimotor system, and the world. A distinctive place is defined as the local maximum of a distinctiveness measure appropriate to its immediate neighborhood, and is found by a hill-climbing control strategy. A distinctive travel edge, similarly, is defined by a suitable measure and a path-following control strategy. The topological network description is created by linking the distinctive places and travel edges. Metrical information is then incrementally assimilated into local geometric descriptions of places and edges, and finally merged into a global geometric map. Topological ambiguity arising from sensorily indistinguishable places can be resolved at, the topological level by the exploration strategy. With this representation, successful navigation is not critically dependent on the accuracy, or even the existence, of the geometrical description. We present examples demonstrating the process by which the robot explores and builds a map of a complex environment, including the effect of sensory errors. We also discuss new research directions that are suggested by this approach.

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A method is presented for combining two previously proposed algorithms for path-planning and dynamic steering control into a computationally feasible scheme for real-time feedback control of autonomous vehicles in uncertain environments. In the proposed approach to vehicle guidance and control, Path Relaxation is used to compute critical points along a globally desirable path using a priori information and sensor data. Generalized potential fields are then used for local feedback control to drive the vehicle along a collision-free path using the critical points as subgoals. Simulation results are presented to demonstrate the control scheme.

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Integrated path planning and dynamic steering control for autonomous vehicles

A modular system architecture has been developed to support visual navigation by an autonomous land vehicle. The system consists of vision modules performing image processing, three-dimensional shape recovery, and geometric reasoning, as well as modules for planning, navigating, and piloting. The system runs in two distinct modes, bootstrap and feedforward. The bootstrap mode requires analysis of entire images to find and model the objects of interest in the scene (e.g., roads). In the feedforward mode (while the vehicle is moving), attention is focused on small parts of the visual field as determined by prior views of the scene, to continue to track and model the objects of interest. General navigational tasks are decomposed into three categories, all of which contribute to planning a vehicle path. They are called long-, intermediate-, and short-range navigation, reflecting the scale to which they apply. The system has been implemented as a set of concurrent communicating modules and used to drive a camera (carried by a robot arm) over a scale model road network on a terrain board. A large subset of the system has been reimplemented on a VICOM image processor and has driven the DARPA Autonomous Land Vehicle (ALV) at Martin Marietta's test site in Denver, CO.

A visual navigation system for autonomous land vehicles

This paper represents part of the ongoing research into an Intelligent Mobile Autonomous System (IMAS). An intermediate path planning subsystem (Navigator), for the hierarchy of IMAS, is presented which operates in completely known, partially known, and completely unknown environments. Information is passed from a sensor to the Navigator via the "Cartographer" which performs map updating. The interaction and performance of these subsystems are demonstrated in a simulation of the IMAS hierarchy.

C. Isik

Papers

Simulation of path planning for a system with vision and map updating