Book•
Real-time obstacle avoidance for manipulators and mobile robots
01 Jul 1990-pp 396-404
TL;DR: This paper reformulated the manipulator control problem as direct control of manipulator motion in operational space-the space in which the task is originally described-rather than as control of the task's corresponding joint space motion obtained only after geometric and kinematic transformation.
Abstract: This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi tionally considered a high level planning problem, can be effectively distributed between different levels of control, al lowing real-time robot operations in a complex environment. This method has been extended to moving obstacles by using a time-varying artificial patential field. We have applied this obstacle avoidance scheme to robot arm mechanisms and have used a new approach to the general problem of real-time manipulator control. We reformulated the manipulator con trol problem as direct control of manipulator motion in oper ational space—the space in which the task is originally described—rather than as control of the task's corresponding joint space motion obtained only after geometric and kine matic transformation. Outside the obstacles' regions of influ ence, we caused the end effector to move in a straight line with an...
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01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.
6,340 citations
Cites methods from "Real-time obstacle avoidance for ma..."
...The idea of using potential functions in this way was proposed for robotics by Khatib [528, 529] and can be considered as a form of gradient descent, which is a general optimization technique....
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TL;DR: A theoretical framework for design and analysis of distributed flocking algorithms, and shows that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders."
Abstract: In this paper, we present a theoretical framework for design and analysis of distributed flocking algorithms. Two cases of flocking in free-space and presence of multiple obstacles are considered. We present three flocking algorithms: two for free-flocking and one for constrained flocking. A comprehensive analysis of the first two algorithms is provided. We demonstrate the first algorithm embodies all three rules of Reynolds. This is a formal approach to extraction of interaction rules that lead to the emergence of collective behavior. We show that the first algorithm generically leads to regular fragmentation, whereas the second and third algorithms both lead to flocking. A systematic method is provided for construction of cost functions (or collective potentials) for flocking. These collective potentials penalize deviation from a class of lattice-shape objects called /spl alpha/-lattices. We use a multi-species framework for construction of collective potentials that consist of flock-members, or /spl alpha/-agents, and virtual agents associated with /spl alpha/-agents called /spl beta/- and /spl gamma/-agents. We show that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders." A "universal" definition of flocking for particle systems with similarities to Lyapunov stability is given. Several simulation results are provided that demonstrate performing 2-D and 3-D flocking, split/rejoin maneuver, and squeezing maneuver for hundreds of agents using the proposed algorithms.
4,693 citations
Cites background or result from "Real-time obstacle avoidance for ma..."
...[37] on coordination of mobile sensor networks; Khatib [38] and Rimon and Koditschek [39] on using artificial potentials for obstacle avoidance; Strogatz [40] on complex biological and social networks; and Olfati-Saber [41] on ultrafast small-world networks....
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...This approach is partially motivated by the work of Khatib [38] and Helbing et al....
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...Some past research with strong connections to this paper include the work of Fax and Murray [30] on formation control and graph Laplacians; Mesbahi [31], [32] on state-dependent 0018-9286/$20.00 © 2006 IEEE graphs; Cortes and Bullo [15] and Cortes et al. [33] on placement of mobile sensors; Rabichini and Frazzoli [34] on energy-efficient splitting algorithms; Leonard and Fiorelli [35] and Olfati-Saber and Murray [36] on graph-induced potential functions for structural formation control; Ögren et al. [37] on coordination of mobile sensor networks; Khatib [38] and Rimon and Koditschek [39] on using artificial potentials for obstacle avoidance; Strogatz [40] on complex biological and social networks; and Olfati-Saber [41] on ultrafast small-world networks....
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...This approach is partially motivated by the work of Khatib [38] and Helbing et al. [10]....
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01 Jan 1991
TL;DR: A real-time obstacle avoidance method for mobile robots which has been developed and implemented, named the vector field histogram (VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target.
Abstract: A real-time obstacle avoidance method for mobile robots which has been developed and implemented is described. This method, named the vector field histogram (VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. The VFH method uses a two-dimensional Cartesian histogram grid as a world model. This world model is updated continuously with range data sampled by onboard range sensors. The VFH method subsequently uses a two-stage data-reduction process to compute the desired control commands for the vehicle. Experimental results from a mobile robot traversing densely cluttered obstacle courses in smooth and continuous motion and at an average speed of 0.6-0.7 m/s are shown. A comparison of the VFN method to earlier methods is given. >
2,352 citations
Cites background from "Real-time obstacle avoidance for ma..."
...The idea of imaginary forces acting on a robot has been suggested by Khatib [16]....
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...Khatib, O., "Real-Time Obstacle Avoidance for Manipulators and Mobile Robots."...
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...3 Potential Field Methods The idea of imaginary forces acting on a robot has been suggested by Khatib [16]....
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Posted Content•
TL;DR: The main contribution of the paper is the introduction of new algorithms, namely, PRM and RRT*, which are provably asymptotically optimal, i.e. such that the cost of the returned solution converges almost surely to the optimum.
Abstract: During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.
2,210 citations
TL;DR: Methods for steering systems with nonholonomic c.onstraints between arbitrary configurations are investigated and suboptimal trajectories are derived for systems that are not in canonical form.
Abstract: Methods for steering systems with nonholonomic c.onstraints between arbitrary configurations are investigated. Suboptimal trajectories are derived for systems that are not in canonical form. Systems in which it takes more than one level of bracketing to achieve controllability are considered. The trajectories use sinusoids at integrally related frequencies to achieve motion at a given bracketing level. A class of systems that can be steered using sinusoids (claimed systems) is defined. Conditions under which a class of two-input systems can be converted into this form are given. >
1,787 citations
Cites background from "Real-time obstacle avoidance for ma..."
...Other approaches include the use of potential functions for navigating in cluttered envi ronments [22, 21] and compliant motion planning for navigating in the presence of uncertainty [10, 11, 34]....
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References
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06 Jun 1984
TL;DR: In this paper, a unified approach to kinematically constrained motion, dynamic interaction, target acquisition and obstacle avoidance is presented, which results in a unified control of manipulator behaviour.
Abstract: Manipulation fundamentally requires a manipulator to be mechanically coupled to the object being manipulated. A consideration of the physical constraints imposed by dynamic interaction shows that control of a vector quantity such as position or force is inadequate and that control of the manipulator impedance is also necessary. Techniques for control of manipulator behaviour are presented which result in a unified approach to kinematically constrained motion, dynamic interaction, target acquisition and obstacle avoidance.
3,292 citations
"Real-time obstacle avoidance for ma..." refers background in this paper
...The potential field concept is indeed an attractive approach to the collision avoidance problem and much research has recently been focused on its applications to robot control (Kuntze and Schill 1982; Hogan 1984; Krogh 1984)....
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Book•
01 Jul 1990
TL;DR: Algorithms for computing constraints on the position of an object due to the presence of ther objects, which arises in applications that require choosing how to arrange or how to move objects without collisions are presented.
1,641 citations
Book•
01 Jan 1980
TL;DR: The Stanford AI Lab cart as discussed by the authors is a card-table sized mobile robot controlled remotely through a radio link, and equipped with a TV camera and transmitter equipped with an onboard TV system.
Abstract: : The Stanford AI Lab cart is a card-table sized mobile robot controlled remotely through a radio link, and equipped with a TV camera and transmitter A computer has been programmed to drive the cart through cluttered indoor and outdoor spaces, gaining its knowledge of the world entirely from images broadcast by the onboard TV system The cart uses several kinds of stereo to locate objects around it in 3D and to deduce its own motion It plans an obstacle avoiding path to a desired destination on the basis of a model built with this information The plan changes as the cart perceives new obstacles on its journey The system is reliable for short runs, but slow The cart moves one meter every ten to fifteen minutes, in lurches After rolling a meter it stops, takes some pictures and thinks about them for a long time Then it plans a new path, executes a little of it, and pauses again The program has successfully driven the cart through several 20 meter indoor courses (each taking about five hours) complex enough to necessitate three or four avoiding swerves A less successful outdoor run, in which the cart skirted two obstacles but collided with a third, was also done Harsh lighting (very bright surfaces next to very dark shadows) giving poor pictures and movement of shadows during the cart's creeping progress were major reasons for the poorer outdoor performance The action portions of these runs were filmed by computer controlled cameras (Author)
1,050 citations
01 Mar 1983
TL;DR: An algorithm is presented which efficiently finds good collision-free paths for convex polygonal bodies through space littered with obstacle polygons by characterizing the volume swept by a body as it is translated and rotated as a generalized cone.
Abstract: Free space is represented as a union of (possibly overlapping) generalized cones. An algorithm is presented which efficiently finds good collision-free paths for convex polygonal bodies through space littered with obstacle polygons. The paths are good in the sense that the distance of closest approach to an obstacle over the path is usually far from minimal over the class of topologically equivalent collision-free paths. The algorithm is based on characterizing the volume swept by a body as it is translated and rotated as a generalized cone, and determining under what conditions one generalized cone is a subset of another.
657 citations
Book•
01 Jul 1990
TL;DR: In this article, a collision-free path algorithm for convex polygonal bodies is presented, which is based on characterizing the volume swept by a body as it is translated and rotated as a generalized cone, and determining under what conditions one generalized cone is a subset of another.
Abstract: Free space is represented as a union of (possibly overlapping) generalized cones. An algorithm is presented which efficiently finds good collision-free paths for convex polygonal bodies through space littered with obstacle polygons. The paths are good in the sense that the distance of closest approach to an obstacle over the path is usually far from minimal over the class of topologically equivalent collision-free paths. The algorithm is based on characterizing the volume swept by a body as it is translated and rotated as a generalized cone, and determining under what conditions one generalized cone is a subset of another.
200 citations