Showing papers on "Motion planning published in 1990"

Path planning using Laplace's equation

Abstract: A method for planning smooth robot paths is presented. The method relies on the use of Laplace's equation to constrain the generation of a potential function over regions of the configuration space of an effector. Once the function is computed, paths may be found very quickly. These functions do not exhibit the local minima which plague the potential field method. Unlike decompositional and algebraic techniques. Laplace's equation is very well suited to computation on massively parallel architectures. >

Autonomous robot vehicles

Abstract: Contents: Guidance (Kinematics, Control, and Trajectory Generation.- Sensors.- Navigation (Position and Copurse Estimation).- Map Representation.- Sensing Strategies.- Motion Planning.- Systems.

Nonholonomic path planning of space robots via a bidirectional approach

Abstract: The path planning of nonholonomic motion of space robot systems is discussed. A space vehicle with a 6-DOF (degrees of freedom) manipulator is described as a nine-variable system with six inputs. It is shown that, by carefully utilizing the nonholonomic mechanical structure, the vehicle orientation in addition to the joint variables of the manipulator can be controlled by actuating only the joint variables. The nonholonomic mechanical structure of space robot systems is shown. A rigorous mathematical proof of the nonholonomic nature of the free-flying space robot systems is provided using Frobenius's theorem. A method for nonholonomic motion planning for space robot systems is established by using a Lyapunov function. >

Incorporating range sensing in the robot navigation function

Abstract: A model of mobile robot navigation is considered in which the robot is a point automaton operating in an environment with unknown obstacles of arbitrary shapes. The robot's input information includes its own and the target-points coordinates as well as local sensing information such as that from stereo vision or a range finder. These algorithmic issues are addressed: (1) Is it possible to combine sensing and planning functions to produce 'active sensing' guided by the needs of planning? (The answer is yes). (2) Can richer sensing (e.g., stereo vision versus tactile) guarantee better performance, that is, resulting in shorter paths? (The general answer is no). A paradigm for combining range data with motion planning is presented. It turns out that extensive modifications of simpler tactile algorithms are needed to take full advantage of additional sensing capabilities. Two algorithms that guarantee convergence and exhibit different styles of behavior are described, and their performance is demonstrated in simulated examples. >

A Monte-Carlo algorithm for path planning with many degrees of freedom

Abstract: A stochastic technique is described for planning collision-free paths of robots with many degrees of freedom (DOFs). The algorithm incrementally builds a graph connecting the local minima of a potential function defined in the robot's configuration space and concurrently searches the graph until a goal configuration is attained. A local minimum is connected to another one by executing a random motion that escapes the well of the first minimum, succeeded by a gradient motion that follows the negated gradient of the potential function. All the motions are executed in a grid shown through the robot's configuration space. The random motions are implemented as random walks which are known to converge toward Brownian motions when the steps of the walks tend toward zero. The local minima graph is searched using a depth-first strategy with random backtracking. In the technique, the planner does not explicitly represent the local-minima graph. The path-planning algorithm has been fully implemented and has run successfully on a variety of problems involving robots with many degrees of freedom. >

Multiple robot path coordination using artificial potential fields

Abstract: A technique for coordinating the paths of multiple robots in the presence of obstacles is presented. To accomplish this, the robots are prioritized. A path that avoids only the stationary obstacles is planned for the highest-priority robot. A trajectory for the next-lowest priority robot is planned so that it avoids both the stationary obstacles and the higher-priority robot, which is treated as a moving obstacle. This process is continued until trajectories for all of the robots have been planned. The planning is accomplished by first mapping the real space of the robots into configuration-space-time. Potential fields are applied around the c-space-time obstacles and are used to modify the path of the robot. The advantage of using artificial potential fields is that they offer a relatively fast and efficient way to solve for safe trajectories around both stationary and moving obstacles. In the method used to perform path planning, a trial path through the c-span-time is chosen and then modified under the influence of the potential fields until an appropriate path is found. >

A Distributed Model for Mobile Robot Environment-Learning and Navigation

Abstract: A distributed method for mobile robot navigation, spatial learning, and path planning is presented. It is implemented on a sonar-based physical robot, Toto, consisting of three competence layers: 1) Low-level navigation: a collection of reflex-like rules resulting in emergent boundary-tracing. 2) Landmark detection: dynamically extracts landmarks from the robot''s motion. 3) Map learning: constructs a distributed map of landmarks. The parallel implementation allows for localization in constant time. {\it Spreading of activation} computes both topological and physical shortest paths in linear time. The main issues addressed are: distributed, procedural, and qualitative representation and computation, emergent behaviors, dynamic landmarks, minimized communication.

Dynamic path planning in sensor-based terrain acquisition

Abstract: 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. >

Smooth local path planning for autonomous vehicles

Abstract: We propose a new method for generating smooth paths for vehicle path planning. The problem of finding the "smoothest path" joining two given configurations is solved (a configu ration is a position and a direction). Generally, we solve the problem using two "simple " path segments. The most important issue is how to define the smoothness cost of a path. We pro pose two distinct definitions: the first is the path curvature, and the second is the derivative of the path curvature. We use cir cular arcs with the first definition, and we use "cubic spirals" with the second for "simple curves" respectively. The set of cubic spirals has several advantages when used in smooth-path planning, one of which is curvature continuity. This theory also reveals other important concepts in robotics, such as "symmet ric configurations " and "symmetric means. " This algorithm has been successfully implemented on the autonomous mobile robot Yamabico-11 at the University of California at Santa Barbara and at the Naval Postgrad...

Indoor automation with many mobile robots

Abstract: The goal of the GOFER project is to control the operations of many mobile robots (several dozens) in an indoor environment. This project raises many research issues: implementation of nonconflicting sensor systems, man-robot and robot-robot communication systems and protocols, contingency-tolerant motion control, multi-robot motion planning, multi-robot task planning and scheduling. The aim of this paper is to provide an overview of the project and present the current solutions to these problems. >

Steering nonholonomic systems using sinusoids

Abstract: Methods for steering systems with nonholonomic constraints between arbitrary configurations are investigated. R.W. Brockett (1981) derived the optimal controls for a set of canonical systems in which the tangent space to the configuration manifold is spanned by the input vector fields and their (first-order) Lie brackets. Using Brockett's result as motivation, the authors derive suboptimal trajectories for systems which are not in canonical form and consider systems in which it takes more than one level of bracketing to achieve controllability. These trajectories use sinusoids at integrally related frequencies to achieve motion at a given bracketing level. Examples and simulation results are presented. >

A technique for autonomous underwater vehicle route planning

Abstract: If an underwater vehicle is to be completely autonomous, it must have the ability to plan paths around obstacles in order to operate safely. Many solutions to the problem of planning the path of a robot around obstacles have been proposed, but all are limited in some way. An algorithm using artificial potential fields to aid in path planning is presented. The planning consists of applying potential fields around obstacles and using these fields to select a safe path. The advantage of using potential fields is that they offer a relatively fast and effective way to solve for safe paths. A trial path is chosen and then modified under the influence of the potential field until an appropriate path is found. By considering the entire path, the problem of being trapped in a local minimum is greatly reduced, allowing the method to be used for global planning. The algorithm was tried with success on many different planning problems. The examples provided illustrate the algorithm's application to two- and three-dimensional planning problems. >

A motion planner for multiple mobile robots

Abstract: An algorithm is described for planning the motions of several mobile robots which share the same workspace. Each robot is capable of independent translational motion in two dimensions, and the workspace contains polygonal obstacles. The algorithm computes a path for each robot which avoids all obstacles in the workspace as well as the other robots. It is guaranteed to find a solution if one exists. The algorithm takes a cell decomposition approach, where the decomposition used is based on the idea of a product operation defined on the cells in a decomposition of a two-dimensional free space. This algorithm is being implemented for the case of two robots as part of ongoing research into useful algorithms for task-level programming of the RobotWorld system. >

Solving findpath by combination of goal-directed and randomized search

Abstract: An application of the findpath problem is briefly introduced, and method solving findpath by a combination of goal-directed straight-and-slide search and randomized generation of subgoals is outlined. The major parts and details of the implemented plane-findpath algorithm are discussed, and experimental results are given. The future extensions of the algorithm to a three-dimensional environment and a six-degree-of-freedom manipulator are considered. >

A new path planning algorithm for moving a point object amidst unknown obstacles in a plane

Abstract: A nonheuristic path planning for moving a point object, or mobile automation (MA), in a two-dimensional plane, amidst unknown obstacles, is considered. A path is to be generated, point by point, using only the local information, like the MA's current position and whether it is in contact with an obstacle. A path-planning algorithm to solve this problem is proposed. The algorithm is used to realize the smallest worst-case path length possible in its category. The procedure for the algorithm is presented with explanations. Its various characteristics, such as local cycle creation, worst-case path length, target reachability conditions, etc. are dealt with. Its performance is compared with that of the existing algorithms. Examples showing the operation of the algorithm are presented. >

A unified approach for robot motion planning with moving polyhedral obstacles

Abstract: A unified approach suitable for path planning with moving polyhedral obstacles is presented. The planner views the space-time configuration of free space as disjoint polytopes that represent a time-dependent environment consisting of moving and stationary objects. Each point in the space-time domain is mapped into a unique polytope set. The planner then constructs a family of feasible collision-free trajectories by searching connected polytopes between the start polytope and the goal polytope that satisfy the speed and time constraints. Finally, a near-optimal trajectory is determined by constrained optimization. This approach does not require that obstacles be nonoverlapping or noncolliding. In addition, the obstacle is allowed to move faster than the planned robot. However, the speed of the obstacle must be piecewise-constant. The proposed approach can be easily extended to motion planning in higher dimensional spaces. >

Constrained minimum-time path planning for robot manipulators via virtual knots of the cubic B-spline functions

Abstract: The B-spline functions are used to generate the constrained minimum-time path for a robot manipulator. The interpolated path via B-spline functions is determined via a unique set of virtual knots so that the robot path can pass every intermediate knot and satisfy the boundary conditions. The local control property of the B-spline functions can also be utilized to simplify the involved computational complexities. The flexible polyhedron search algorithm is used to generate the near minimum-time path with velocity, acceleration, and jerk constraints on every intermediate point. This approach can simplify the formulation and procedures for generating the near minimum-time cubic B-spline path. The use of this method to generate the constrained minimum-time joint trajectories for a PUMA 560 robot is discussed as an example. >

A technique for autonomous underwater vehicle route planning

Abstract: An algorithm that uses artificial potential fields to aid in the path planning of autonomous underwater vehicles is presented. The planning consists of applying potential fields around the obstacles and using the field to select a safe path for the robot to follow. In the method presented, a trial path is chosen and then modified under the influence of the potential field until an appropriate path is found. By considering the entire path, the problem of being trapped in a local minimum is greatly reduced, allowing the method to be used for global planning. The algorithm was tried with success on many different planning problems. The examples illustrate the algorithm applied to 2D and 3D planning problems. >

Control for mobile robots in the presence of moving objects

Abstract: The problem of collision-free navigation and guidance for mobile robots is essential to the survival of unmanned terrain vehicles. This communication adds complexity to the guidance problem by introducing moving objects that may cross the desired navigational path of an autonomous vehicle. The vehicle is equipped with sensors that report locations of the moving objects. Speed and direction of each moving object are modeled as random walk processes. An optimization approach that provides for the acceleration or deceleration of the vehicle is developed, as is the duration of the time for which this control must be applied to avoid a collision with moving objects. The constraints on the objective function are given in terms of collision probabilities, and simulation results are presented for a collision-free environment. Implications of this approach for autonomous vehicles are given in terms of the flexibility of path planning. >

Fast collision avoidance for manipulator arms: a sequential search strategy

Abstract: A sequential strategy is presented for planning collision-free motions for a manipulator arm. The basic idea behind the approach is to plan the motion of each link successively, starting from the base link. Suppose that the motion of links to link i (including link i) has been planned. This already determines the path of one end (the proximal end) of link i+1. The motion of link i+1 is now planned along this path by controlling the degree of freedom associated with it, which is a 2-D motion planning problem. This strategy results in one 1-D (the first link is degenerate) and (n-1) 2-D planning problems. The 2-D motion planning problem is to plan the motion of a single link as one end of this link moves along a fixed path. This problem is posed in t* theta space, where t is the parameter along the path and theta the angle to be planned. The obstacles in t* theta space are approximated by discretizing t. Fast and efficient techniques are then used to plan a path in t* theta space. Thus, the strategy leads to fast and efficient algorithms and is especially suited for highly redundant arms. >

Mobile robot navigating method

Abstract: A mobile robot navigating method for navigating an autonomous mobile robot utilizes a path planning unit, a posture control unit, a command signal converting unit, a current posture computing unit and a running mechanism, such as an autonomous carrier vehicle, capable of quickly and accurately controlling the mobile robot for steady running to a desired posture along a specified path. The mobile robot is controlled for running by a speed command including a desired speed for feed-forward control a speed correction for feed back control, and an angular velocity command including a desired angular velocity for feed-forward control and an angular velocity correction for feedback control. The mobile robot is controlled in a feed back and feed-forward control mode using the speed command and the angular velocity command so as to run steadily along an optimum path to a desired posture without meandering.

Dynamics and optimal control of a legged robot in flight phase

Abstract: A discussion is presented of the control of legged-robot body orientation in flight using the internal motion of the leg. The angular momentum constraint (nonholonomic) is used to recast the problem into a nonholonomic motion planning problem. Chow's theorem is then applied to verify that the system is controllable, and the concept of holonomy is introduced for constructing an optimal path. Finally, linearization control is used in the internal motion space to realize the planned path. An additional degree of control to dynamically balance a legged robot that runs is provided with this strategy. >

A Reactive Robot Navigation System Based on a Fluid Dynamics Metaphor

Abstract: Mobile robots need a powerful and flexible navigation system in order to move around autonomously in an incompletely known and changing environment In this paper we present a computational metaphor for path generation which is gleaned from fluid dynamics It is powerful because it can find optimal paths in a maze of arbitrary complexity and it is flexible because it readily adapts to any change in the topology of the maze Moreover, it can be proven that the path generator does not suffer from local minima, a defect which hampers some of the other metaphor based methods We show that the fluid dynamics metaphor also provides ample possibilities to solve more complicated navigation problems including navigation through weighted regions The paper discusses the natural parallelism of the metaphor and its implementation on the DAP computer, which is an SIMD machine

A motion planner for car-like robots based on a mixed global/local approach

Abstract: Deals with the problem of motion planning for a car-like robot (i.e. nonholonomic mobile robot whose turning radius is lower bounded). The main contribution is the introduction of a new metric in the configuration space R/sup 2/*S/sup 1/ of such a system. This metric is defined from the length of the shortest paths in the absence of obstacles. The authors study the relations between the new induced topology and the classical one. This study leads to new theoretical issues about sub-Riemannian geometry and to practical results for motion planning. In particular they prove an inclusion relation of neighbourhoods in both topologies, which is the basis of an efficient obstacle avoidance local method. >

Environment learning using a distributed representation

Abstract: A method for robust mobile robot navigation and environmental learning is presented. It was implemented and tested on a physical robot. The method consists of a collection of simple, incrementally designed robot behaviors. The behaviors receive sonar and compass data which they use to dynamically detect landmarks and construct a distributed map of the environment. The map is represented as a graph in which each node is a collection of augmented finite state machines functioning in parallel. The distributed nature of the map allows for localization in constant time. The method utilizes a modified spreading of activation scheme to accomplish robust linear-time path planning. It is capable of generating both topologically and physically shortest paths to the goal. The method uses local information to achieve the global task without having to replan if the robot becomes lost or strays off the desired path. >

Path planning for moving a point object amidst unknown obstacles in a plane: a new algorithm and a general theory for algorithm development

Abstract: A new algorithm Alg2 is developed to solve the problem of nonheuristically moving a point object (MA) between two given points, amidst unknown obstacles in a plane. The existing algorithms to solve this are Bug1 and Bug2 (J. Lumelsky and A.A. Stepanov, 1987) and Alg1 (A. Sankaranarayanan and M. Vidyasagar, 1990). The existing algorithms make MA follow mostly the contours of the obstacle boundaries. Under Alg2, MA follows the obstacles boundaries less and travels more along straight line segments towards the target point. The worst case path length, convergence, local cycle creation, etc. of Alg2 are presented along the examples of its operation. Alg2 is developed step by step, by adding new procedures to a certain primitive algorithm. This algorithm development brings out the basic requirements of a nonheuristic path planning algorithm, and this knowledge leads to a general algorithm Gen of which the algorithms Bug2, Alg1, and Alg2 are particular cases. This generalization helps understand the basic operating principles of a nonheuristic path planning algorithm. >

Connectionist Robot Motion Planning: A Neurally-Inspired Approach to Visually-Guided Reaching

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The use of simulated annealing to solve the mobile manipulator path planning problem

Abstract: The planning problem is considered for a mobile manipulator system which must perform a sequence of tasks defined by position, orientation, force, and moment vectors at the end effector. Each task can be performed in multiple configurations due to the redundancy introduced by mobility. The planning problem is formulated as an optimization problem in which the decision variables for mobility (base position) are separated from the manipulator joint angles in the cost function. The resulting numerical problem is nonlinear with nonconvex, unconnected feasible regions in the decision space. Simulated annealing is proposed as a general solution method for obtaining near-optimal results. The problem formulation and numerical solution by simulated annealing are illustrated for a positioning system with five degrees of freedom. These results are compared with results obtained by conventional nonlinear programming techniques customized for the particular example system. >

Optimal motion planning of autonomous vehicles in three dimensional terrains

Abstract: A method is presented for optimally planning the motions of autonomous vehicles, considering vehicle dynamics, terrain topography, obstacles, and surface mobility. The terrain is represented by a smooth cubic B patch, and the geometric path consists of a two dimensional B spline curve mapped to the three-dimensional surface. The path is optimized with a parameter optimization procedure, using motion time as the cost function. The optimal motion time along the path is computed by transforming the constraints between the vehicle and ground to limits on vehicle speeds. The optimal speeds which minimize motion time are then obtained below the velocity limits, using the maximum acceleration of deceleration at all times. Examples are presented which demonstrate the method for a simple dynamic model of a vehicle moving on mountainous terrain. >