Vladimir J. Lumelsky

Dynamic path planning in sensor-based terrain acquisition

The terrain acquisition problem is formulated as that of continuous motion planning, and no constraints are imposed on obstacle geometry. Two algorithms are described for acquiring planar terrains with obstacles of arbitrary shape. Estimates of the algorithm performance are derived as upper bounds on the lengths of generated paths. >

Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

The problem of path planning for an automaton moving in a two-dimensional scene filled with unknown obstacles is considered. The automaton is presented as a point; obstacles can be of an arbitrary shape, with continuous boundaries and of finite size; no restriction on the size of the scene is imposed. The information available to the automaton is limited to its own current coordinates and those of the target position. Also, when the automaton hits an obstacle, this fact is detected by the automaton’s “tactile sensor.” This information is shown to be sufficient for reaching the target or concluding in finite time that the target cannot be reached. A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene. Algorithms that guarantee reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is studied, and worst-case upper bounds on the length of generated paths are produced.

A comparative study on the path length performance of maze-searching and robot motion planning algorithms

A number of existing maze-searching and robot motion planning algorithms are studied from the standpoint of a single performance criterion. The main motivation is to build a framework for selecting basic planning algorithms for autonomous vehicles and robot arm manipulators that operate in an environment filled with unknown obstacles of arbitrary shapes. In choosing an appropriate criterion, it is noted that besides convergence, minimizing the length of generated paths is a single major consideration in planning algorithms. In addition, since no complete information is ever available, optimal solutions are ruled out. Accordingly, the performance criterion is defined in terms of the upper bound on the length of generated paths as a function of the maze perimeter. The comparison shows that the special structure of graphs that correspond to planar environments with obstacles actually makes it possible to exceed the efficiency of general maze-searching algorithms. >

Proximity sensing in robot manipulator motion planning: system and implementation issues

Results of one effort to implement a motion planning system for a robot arm operating in an uncertain environment are discussed. It is known that path planning algorithms with proven convergence can be designed for some planar and three-dimensional robot arm manipulators operating among unknown obstacles of arbitrary shapes. The attractiveness of such systems lies in their ability to operate in a complex, perhaps even time-varying, unstructured environment. Implementation of these algorithms, however, presents a variety of hardware and algorithmic problems related to: covering the arm with arrays of sensors to form a sensitive skin; processing real-time sensor data; designing complementary algorithms for step-by-step motion planning based on limited local information; and integrating these components, together with global planning algorithms, in a single system. The authors discuss various tradeoffs and solutions implemented in the sensor-based planning system for a planar arm manipulator developed in their laboratory and present a summary of their experiments with it. >

Effect of kinematics on motion planning for planar robot arms moving amidst unknown obstacles

An approach of dynamic path planning (DPP) was introduced elsewhere, and nonheuristic algorithms were described for planning collision-free paths for a point automaton moving in an environment filled with unknown obstacles of arbitrary shape. The DPP approach was further extended to a planar robot arm with revolute joints; in this case, every point of the robot body is subject to collision. Under the accepted model, the robot, using information about its immediate surroundings provided by the sensory feedback, continuously (dynamically) generates its path. Various kinematic configurations of planar arms with revolute and sliding joints are analyzed in this paper from the standpoint of applying the same strategy. It is shown that, depending on the arm kinematics, specific modifications must be introduced in the path planning algorithm to preserve convergence. The approach presents an attractive method for robot motion planning in unstructured environments with uncertainty.

Algorithmic and complexity issues of robot motion in an uncertain environment

Abstract This paper presents a survey of one approach to planning collision-free paths for an automaton operating in an environment with obstacles. Path planning is one of the central problems in robotics. Typically, the task is presented in the two- or three-dimensional space, with the automaton being either an autonomous vehicle or an arm manipulator with a fixed base. The multiplicity of approaches one finds in this area revolves around two basic models: in one, called path planning with complete information, perfect information about the geometry and positions of the robot and the obstacles is assumed, whereas in the other, called path planning with incomplete information, an element of uncertainty about the environment is present. The approach surveyed here, called dynamic path planning, has been developed in the last few years; it is based on the latter model and gives rise to algorithmic and computational issues very different from those in the former model. The approach produces provable (nonheuristic) path planning algorithms for an automaton operating in a highly unstructured environment where no knowledge about the obstacles is available beforehand and no constraints on the geometry of the obstacles are imposed.

A paradigm for incorporating vision in the robot navigation function

The authors present a paradigm for combining vision with motion planning. It turns out that extensive modifications of so-called tactile algorithms are needed to take full advantage of the additional sensing capabilities, while not sacrificing the algorithm convergence. Different design principles can be introduced that result in algorithm versions exhibiting different styles of behavior and producing different paths, without, in general, being superior to each other.<<ETX>>

On human performance in telerobotics

Experimental attempts to build teleoperated master-slave robot arm manipulators revealed that a human operator has difficulty in interpreting input information (coming, e.g. directly via visual tract or from fixed or moving TV monitors at the scene), and consequently in teleoperation decision making. The problem becomes more pronounced when the slave arm has to operate in a complex environment where every point of the arm body is subject to potential collision. Results are presented of experimental tests with human operators that trace the source of the difficulty to the limitations in human abilities for space orientation and interpretation of geometrical data, and a solution that capitalizes on recent developments in sensor-based motion planning for whole-sensitive robot arms is proposed. The result would be a hybrid system in which global planning is done by a human operator, whereas local collision-free motion is controlled by an assisting autopilot. >

Motion planning for a whole-sensitive robot arm manipulator

A sensor-based motion planning system for a robot arm manipulator must include these four basic components: sensor hardware; real-time signal/sensory data processing hardware/software a local step planning subsystem that works at the basic sample rate of the arm; and a subsystem for global planning. The objective of this work is to develop the fourth component, a real-time implementable algorithm that realizes the upper, global level of planning. Based on the current collection of local normals, the algorithm generates preferable directions of motion around obstacles, in order to guarantee reaching the target position if it is reachable. Experimental results from testing the developed system are also discussed.<<ETX>>

Development of sensitive skin for a 3D robot arm operating in an uncertain environment

The authors describe an ongoing research project on the development of a sensitive skin and its control scheme for a 3D robot arm. The skin consists of hundreds of active infrared proximity sensors that cover the whole arm. This sensor-based motion planning system senses obstacles by emitting amplitude-modulated infrared light and then sensing the reflected signal. The problem of interpreting data from the sensors is handled by the step planning algorithm, which transforms the sensor data into local tangent normals in the configuration space. Once this information is available, a global path planning algorithm can then be used to move the arm through freespace or to follow the contour of obstacles that are encountered. Details of the skin design are presented, along with the computer hardware and operating logistics and the sensor data interpretation algorithm that connects this subsystem to the global motion planning subsystem.<<ETX>>