|
Rapidly Exploring Random Tree
A rapidly exploring random tree (RRT) is an algorithm designed to efficiently search nonconvex, high-dimensional spaces by randomly building a space-filling tree. The tree is constructed incrementally from samples drawn randomly from the search space and is inherently biased to grow towards large unsearched areas of the problem. RRTs were developed by Steven M. LaValle and James J. Kuffner Jr. They easily handle problems with obstacles and differential constraints ( nonholonomic and kinodynamic) and have been widely used in autonomous robotic motion planning. RRTs can be viewed as a technique to generate open-loop trajectories for nonlinear systems with state constraints. An RRT can also be considered as a Monte-Carlo method to bias search into the largest Voronoi regions of a graph in a configuration space. Some variations can even be considered stochastic fractals. RRTs can be used to compute approximate control policies to control high dimensional nonlinear systems wi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithm
In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algorithms are used as specifications for performing calculations and data processing. More advanced algorithms can use Conditional (computer programming), conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning). In contrast, a Heuristic (computer science), heuristic is an approach to solving problems without well-defined correct or optimal results.David A. Grossman, Ophir Frieder, ''Information Retrieval: Algorithms and Heuristics'', 2nd edition, 2004, For example, although social media recommender systems are commonly called "algorithms", they actually rely on heuristics as there is no truly "correct" recommendation. As an e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Underactuation
Underactuation is a technical term used in robotics and control theory to describe mechanical systems that cannot be commanded to follow arbitrary trajectories in configuration space. This condition can occur for a number of reasons, the simplest of which is when the system has a lower number of actuators than degrees of freedom. In this case, the system is said to be ''trivially underactuated''. The class of underactuated mechanical systems is very rich and includes such diverse members as automobiles, airplanes, and even animal Animals are multicellular, eukaryotic organisms in the Biology, biological Kingdom (biology), kingdom Animalia (). With few exceptions, animals heterotroph, consume organic material, Cellular respiration#Aerobic respiration, breathe oxygen, ...s. Definition To understand the mathematical conditions which lead to underactuation, one must examine the dynamics that govern the systems in question. Newton's laws of motion dictate that the dyn ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Space-filling Tree
Space-filling trees are geometric constructions that are analogous to space-filling curves, but have a branching, tree-like structure and are rooted. A space-filling tree is defined by an incremental process that results in a tree for which every point in the space has a finite-length path that converges to it. In contrast to space-filling curves, individual paths in the tree are short, allowing any part of the space to be quickly reached from the root. The simplest examples of space-filling trees have a regular, self-similar, fractal structure, but can be generalized to non-regular and even randomized/ Monte-Carlo variants (see Rapidly exploring random tree). Space-filling trees have interesting parallels in nature, including fluid distribution systems, vascular networks, and fractal plant growth, and many interesting connections to L-systems in computer science. Definition A space-filling tree is defined by an iterative process whereby a single point in a continuous space ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Probabilistic Roadmap
The probabilistic roadmap planner is a motion planning algorithm in robotics, which solves the problem of determining a path between a starting configuration of the robot and a goal configuration while avoiding collisions. The basic idea behind PRM is to take random samples from the Configuration space (physics), configuration space of the robot, testing them for whether they are in the free space, and use a local planner to attempt to connect these configurations to other nearby configurations. The starting and goal configurations are added in, and a graph search algorithm is applied to the resulting Graph (discrete mathematics), graph to determine a path between the starting and goal configurations. The probabilistic roadmap planner consists of two phases: a construction and a query phase. In the construction phase, a roadmap (graph) is built, approximating the motions that can be made in the environment. First, a random configuration is created. Then, it is connected to some n ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Any-angle Path Planning
Any-angle path planning algorithms are pathfinding algorithms that search for a Euclidean shortest path between two points on a grid map while allowing the turns in the path to have any angle. The result is a path that cuts directly through open areas and has relatively few turns. More traditional pathfinding algorithms such as A* either lack in performance or produce jagged, indirect paths. Background Real-world and many game maps have open areas that are most efficiently traversed in a direct way. Traditional algorithms are ill-equipped to solve these problems: * A* with an 8-connected discrete grid graph (2D; 26 for the 3D triple cubic graph) is very fast, but only looks at paths in 45-degree increments. This behavior gives on average 8% extra path length in 2D and 13% in 3D. A quick post-smoothing step can be used to straighten (thus shorten) the jagged output, but the result is not guaranteed to be optimal as it does not look at all the possible paths. (More specifically, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
IEEE Transactions On Control Systems Technology
The ''IEEE Transactions on Control Systems Technology'' is published bimonthly by the IEEE Control Systems Society. The journal publishes papers, letters, tutorials, surveys, and perspectives on control systems technology. The editor-in-chief is Prof. Andrea Serrani (Ohio State University). According to the ''Journal Citation Reports'', the journal has a 2019 impact factor The impact factor (IF) or journal impact factor (JIF) of an academic journal is a type of journal ranking. Journals with higher impact factor values are considered more prestigious or important within their field. The Impact Factor of a journa ... of 5.312. References External links * Transactions on Control Systems Technology Control engineering Computer science journals Academic journals established in 1993 Bimonthly journals English-language journals {{comp-sci-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nonholonomic
A nonholonomic system in physics and mathematics is a physical system whose state depends on the path taken in order to achieve it. Such a system is described by a set of parameters subject to differential constraints and non-linear constraints, such that when the system evolves along a path in its parameter space (the parameters varying continuously in values) but finally returns to the original set of parameter values at the start of the path, the system itself may not have returned to its original state. Nonholonomic mechanics is an autonomous division of Newtonian mechanics. Details More precisely, a nonholonomic system, also called an ''anholonomic'' system, is one in which there is a continuous closed circuit of the governing parameters, by which the system may be transformed from any given state to any other state. Because the final state of the system depends on the intermediate values of its trajectory through parameter space, the system cannot be represented by a conserva ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Any-angle Search
Any-angle path planning algorithms are pathfinding algorithms that search for a Euclidean shortest path between two points on a grid map while allowing the turns in the path to have any angle. The result is a path that cuts directly through open areas and has relatively few turns. More traditional pathfinding algorithms such as A* either lack in performance or produce jagged, indirect paths. Background Real-world and many game maps have open areas that are most efficiently traversed in a direct way. Traditional algorithms are ill-equipped to solve these problems: * A* with an 8-connected discrete grid graph (2D; 26 for the 3D triple cubic graph) is very fast, but only looks at paths in 45-degree increments. This behavior gives on average 8% extra path length in 2D and 13% in 3D. A quick post-smoothing step can be used to straighten (thus shorten) the jagged output, but the result is not guaranteed to be optimal as it does not look at all the possible paths. (More specifically, th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sven Koenig (computer Scientist)
Sven Koenig is a full professor in computer science at the University of California, Irvine. He received an M.S. degree in computer science from the University of California at Berkeley in 1991 and a Ph.D. in computer science from Carnegie Mellon University in 1997, advised by Reid Simmons. Research Koenig is an artificial intelligence and robotics researcher who develops techniques for planning and learning under uncertainty and time constraints, both for single agents and teams of agents. His research often combines ideas from artificial intelligence and robotics with ideas from other disciplines, such as decision theory, theoretical computer science, operations research and economics. Scientific Achievements In his pre-dissertation work, Koenig applied Markov Decision Processes (MDPs) to artificial intelligence planning. The standard textbook in artificial intelligence, Artificial Intelligence: A Modern Approach (second edition), states "The connection between MDPs and A ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Real-time Computing
Real-time computing (RTC) is the computer science term for Computer hardware, hardware and software systems subject to a "real-time constraint", for example from Event (synchronization primitive), event to Event (computing), system response. Real-time programs must guarantee response within specified time constraints, often referred to as "deadlines".Mordechai Ben-Ari, Ben-Ari, Mordechai; "Principles of Concurrent and Distributed Programming", ch. 16, Prentice Hall, 1990, , p. 164 The term "real-time" is also used in Computer simulation, simulation to mean that the simulation's clock runs at the same speed as a real clock. Real-time responses are often understood to be in the order of milliseconds, and sometimes microseconds. A system not specified as operating in real time cannot usually ''guarantee'' a response within any timeframe, although ''typical'' or ''expected'' response times may be given. Real-time processing ''fails'' if not completed within a specified deadline rela ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dijkstra's Algorithm
Dijkstra's algorithm ( ) is an algorithm for finding the shortest paths between nodes in a weighted graph, which may represent, for example, a road network. It was conceived by computer scientist Edsger W. Dijkstra in 1956 and published three years later. Dijkstra's algorithm finds the shortest path from a given source node to every other node. It can be used to find the shortest path to a specific destination node, by terminating the algorithm after determining the shortest path to the destination node. For example, if the nodes of the graph represent cities, and the costs of edges represent the distances between pairs of cities connected by a direct road, then Dijkstra's algorithm can be used to find the shortest route between one city and all other cities. A common application of shortest path algorithms is network routing protocols, most notably IS-IS (Intermediate System to Intermediate System) and OSPF (Open Shortest Path First). It is also employed as a subroutine in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
A* Search Algorithm
A* (pronounced "A-star") is a graph traversal and pathfinding algorithm that is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. Given a weighted graph, a source node and a goal node, the algorithm finds the shortest path (with respect to the given weights) from source to goal. One major practical drawback is its O(b^d) space complexity where is the depth of the shallowest solution (the length of the shortest path from the source node to any given goal node) and is the branching factor (the maximum number of successors for any given state), as it stores all generated nodes in memory. Thus, in practical travel-routing systems, it is generally outperformed by algorithms that can pre-process the graph to attain better performance, as well as by memory-bounded approaches; however, A* is still the best solution in many cases. Peter Hart, Nils Nilsson and Bertram Raphael of Stanford Research Institute (now SRI International) ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
