4,646 research outputs found
A Minimum Cost Path Search Algorithm Through Tile Obstacles
ABSTRACT In this paper, based on tile connection graph, we propose an efficient minimum cost path search algorithm through tile obstacles. This search algorithm is faster than previous graph based algorithm and unlike previous tile based algorithms, this algorithm finds the minimum cost path
Sampling-based Algorithms for Optimal Motion Planning
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.Comment: 76 pages, 26 figures, to appear in International Journal of Robotics
Researc
Move Table: An Intelligent Software Tool for OptimalPath Finding and Halt Schedule Generation
This study aims to help army officials in taking decisions before war to decide the optimalpath for army troops moving between two points in a real world digital terrain, consideringfactors like traveled distance, terrain type, terrain slope, and road network. There can optionallybe one or more enemies (obstacles) located on the terrain which should be avoided. A tile-basedA* search strategy with diagonal distance and tie-breaker heuristics is proposed for finding theoptimal path between source and destination nodes across a real-world 3-D terrain. A performancecomparison (time analysis, search space analysis, and accuracy) has been made between themultiresolution A* search and the proposed tile-based A* search for large-scale digital terrainmaps. Different heuristics, which are used by the algorithms to guide these to the goal node,are presented and compared to overcome some of the computational constraints associated withpath finding on large digital terrains. Finally, a halt schedule is generated using the optimal path,weather condition, moving time, priority and type of a column, so that the senior military plannerscan strategically decide in advance the time and locations where the troops have to halt orovertake other troops depending on their priority and also the time of reaching the destination
Improving Lookahead search for grid-based pathfinding
Pathfinding is an essential part of navigation systems, often used in video games, route planning and robotic navigation. A* search has been one of the most well-known and frequently used algorithms for pathfinding. A* uses an open list and a closed list to keep track of all nodes generated and expanded. The size and performance of these data structures are major drawbacks of A*. Lookahead is used to investigate future outcomes and improve the quality of available choices. Lookaheads are done on a DFS manner from the frontier of A* search. This combination of A* and DFS lookahead has been shown to save space when working with puzzles. We leverage this concept with grid-based pathfinding in video games to save the amount of space consumed. However, because grids contain redundant paths, the DFS lookaheads end up being an overhead as they do not maintain a list of nodes visited or expanded. By using a domain-specific pruning technique, we significantly improve the time taken by the algorithm and further improve upon the space consumed. A combination of lookahead and A* search with this pruning technique is, therefore, able to achieve improvement in both space-consumed and time-taken over the standard A* search algorithm for grid-based pathfinding
Hybrid approaches for mobile robot navigation
The work described in this thesis contributes to the efficient solution of mobile robot navigation problems. A series of new evolutionary approaches is presented.
Two novel evolutionary planners have been developed that reduce the computational
overhead in generating plans of mobile robot movements. In comparison with the
best-performing evolutionary scheme reported in the literature, the first of the
planners significantly reduces the plan calculation time in static environments. The
second planner was able to generate avoidance strategies in response to unexpected events arising from the presence of moving obstacles. To overcome limitations in responsiveness and the unrealistic assumptions regarding a priori knowledge that are inherent in planner-based and a vigation systems, subsequent work concentrated on hybrid approaches. These included a reactive component to identify rapidly and autonomously environmental features that were represented by a small number of critical waypoints. Not only is memory usage dramatically reduced by such a simplified representation, but also the calculation time to determine new plans is significantly reduced. Further significant enhancements of this work were firstly, dynamic avoidance to limit the likelihood of potential collisions with moving obstacles and secondly, exploration to identify statistically the dynamic
characteristics of the environment. Finally, by retaining more extensive environmental knowledge gained during previous navigation activities, the capability of the hybrid navigation system was enhanced to allow planning to be performed for any start point and goal point
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Fully-autonomous miniaturized robots (e.g., drones), with artificial
intelligence (AI) based visual navigation capabilities are extremely
challenging drivers of Internet-of-Things edge intelligence capabilities.
Visual navigation based on AI approaches, such as deep neural networks (DNNs)
are becoming pervasive for standard-size drones, but are considered out of
reach for nanodrones with size of a few cm. In this work, we
present the first (to the best of our knowledge) demonstration of a navigation
engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based
visual navigation. To achieve this goal we developed a complete methodology for
parallel execution of complex DNNs directly on-bard of resource-constrained
milliwatt-scale nodes. Our system is based on GAP8, a novel parallel
ultra-low-power computing platform, and a 27 g commercial, open-source
CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the
software mapping techniques that enable the state-of-the-art deep convolutional
neural network presented in [1] to be fully executed on-board within a strict 6
fps real-time constraint with no compromise in terms of flight results, while
all processing is done with only 64 mW on average. Our navigation engine is
flexible and can be used to span a wide performance range: at its peak
performance corner it achieves 18 fps while still consuming on average just
3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication
in the IEEE Internet of Things Journal (IEEE IOTJ
Direction based Heuristic for Pathfinding in Video Games
Pathfinding has been one of major research areas in video games for many years. It is a key problem that most of the video games are confronted with. Search algorithms such as the A* algorithm and the Dijkstra2019;s algorithm representing such regular grid, visibility graphs also have significant impact on the performance. This paper reviews the current widely used solutions for pathfinding and proposes a new method which is expected to generate a higher quality path using less time and memory than other existing solutions. The deployment of the methodologies and techniques is described in detail. The significance of the proposed method in future video games is addressed and the conclusion is given at the end
HCTNav: A path planning algorithm for low-cost autonomous robot navigation in indoor environments
© 2013 by MDPI (http://www.mdpi.org). Reproduction is permitted for noncommercial purposes.Low-cost robots are characterized by low computational resources and limited energy supply. Path planning algorithms aim to find the optimal path between two points so the robot consumes as little energy as possible. However, these algorithms were not developed considering computational limitations (i.e., processing and memory capacity). This paper presents the HCTNav path-planning algorithm (HCTLab research group’s navigation algorithm). This algorithm was designed to be run in low-cost robots for indoor navigation. The results of the comparison between HCTNav and the Dijkstra’s algorithms show that HCTNav’s memory peak is nine times lower than Dijkstra’s in maps with more than 150,000 cells.This work has been partially supported by the Spanish “Ministerio de Ciencia e Innovación”, under project TEC2009-09871
Motion-Planning and Control of Autonomous Vehicles to Satisfy Linear Temporal Logic Specifications
Motion-planning is an essential component of autonomous aerial and terrestrial vehicles. The canonical Motion-planning problem, which is widely studied in the literature, is of planning point-to-point motion while avoiding obstacles. However, the desired degree of vehicular autonomy has steadily risen, and has consequently led to motion-planning problems where a vehicle is required to accomplish a high-level intelligent task, rather than simply move between two points. One way of specifying such intelligent tasks is via linear temporal logic (LTL) formulae. LTL is a formal logic system that includes temporal operators such as always, eventually, and until besides the usual logical operators. For autonomous vehicles, LTL formulae can concisely express tasks such as persistent surveillance, safety requirements, and temporal orders of visits to multiple locations. Recent control theoretic literature has discussed the generation of reference trajectories and/or the synthesis of feedback control laws to enable a vehicle to move in manners that satisfy LTL specifications. A crucial step in such synthesis is the generation of a so-called discrete abstraction of a vehicle kinematic/dynamic model. Typical techniques of generating a discrete abstraction require strong assumptions on controllability and/or linearity. This dissertation discusses fast motion-planning and control techniques to satisfy LTL specifications for vehicle models with nonholonomic kinematic constraints, which do not satisfy the aforesaid assumptions. The main contributions of this dissertation are as follows.
First, we present a new technique for constructing discrete abstractions of a Dubins vehicle model (namely, a vehicle that moves forward at a constant speed with a minimum turning radius). This technique relies on the so-called method of lifted graphs and precomputed reachable set calculations. Using this technique, we provide an algorithm to generate vehicle reference trajectories satisfying LTL specifications without requiring complete controllability in the presence of workspace constraints, and without requiring linearity or linearization of the vehicle model. Second, we present a technique for centralized motion-planning for a team of vehicles to collaboratively satisfy a common LTL specification. This technique is also based on the method of lifted graphs. Third, we present an incremental version of the proposed motion-planning techniques, which has an “anytime property. This property means that a feasible solution is computed quickly, and the iterative updates are made to this solution with a guarantee of convergence to an optimal solution. This version is suited for real-time implementation, where a hard bound on the computation time is imposed. Finally, we present a randomized sampling-based technique for generating reference trajectories that satisfy given LTL specifications. This technique is an alternative to the aforesaid technique based on lifted graphs. We illustrate the proposed techniques using numerical simulation examples. We demonstrate the superiority of the proposed techniques in comparison to the existing literature in terms of computational time and memory requirements
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