Optimal and efficient path planning for partially-known environments

Stentz, Anthony

doi:10.1109/robot.1994.351061

Cited by 901 publications

(462 citation statements)

References 9 publications

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“…However, in real environments, obstacles that had not existed in the environment map previously happen to appear, and mobile robots operate in dynamic environments where obstacles are moving; it is hard to apply static environment maps to the real environment under the assumption that robots are already equipped with all the required information [22]. Therefore, as a solution to the aforementioned problem, there are algorithms, such as D* algorithm [23][24][25], Wavefront-propagation algorithm [1,4,26,27], etc., that can be applied to make path planning easier.…”

Section: Path Planningmentioning

confidence: 99%

Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles

et al. 2020

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Environment maps must first be generated to drive mobile robots automatically. Path planning is performed based on the information given in an environment map. Various types of sensors, such as ultrasonic and laser sensors, are used by mobile robots to acquire data on its surrounding environment. Among these, the laser sensor, which has the property of being able to go straight and high accuracy, is used most often. However, the beams from laser sensors are refracted and reflected when it meets a transparent obstacle, thus generating noise. Therefore, in this paper, a state-of-the-art algorithm was proposed to detect transparent obstacles by analyzing the pattern of the reflected noise generated when a laser meets a transparent obstacle. The experiment was carried out using the environment map generated by the aforementioned method and gave results demonstrating that the robot could avoid transparent obstacles while it was moving towards the destination.

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Section: Path Planningmentioning

confidence: 99%

Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles

et al. 2020

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“…One primary task for the autonomous mobility system of the rover is to find a route to a designated location and to avoid mobility hazards. Substantial studies have addressed path/motion‐planning algorithms for mobile robots, such as the A* and D* methods (Stentz, ; Stentz and Hebert, ), the potential field approach (Barraquand et al, ), the probabilistic roadmap technique (Kavraki et al, ), and the rapidly exploring random tree (RRT) algorithm (LaValle, ; LaValle and Kuffner, ). Also a heuristically biased expansion for the generation of efficient paths with satisfying dynamic constraints has been developed (Urmson and Simmons, ).…”

Section: Related Workmentioning

confidence: 99%

Range‐dependent Terrain Mapping and Multipath Planning using Cylindrical Coordinates for a Planetary Exploration Rover

Ishigami

Otsuki

Kubota

2013

Journal of Field Robotics

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This paper presents terrain mapping and path‐planning techniques that are key issues for autonomous mobility of a planetary exploration rover. In this work, a LIDAR (light detection and ranging) sensor is used to obtain geometric information on the terrain. A point cloud of the terrain feature provided from the LIDAR sensor is usually converted to a digital elevation map. A sector‐shaped reference grid for the conversion process is proposed in this paper, resulting in an elevation map with cylindrical coordinates termed as C2DEM. This conversion approach achieves a range‐dependent resolution for the terrain mapping: a detailed terrain representation near the rover and a sparse representation far from the rover. The path planning utilizes a cost function composed of terrain inclination, terrain roughness, and path length indices, each of which is subject to a weighting factor. The multipath planning developed in this paper first explores possible sets of weighting factors and generates multiple candidate paths. The most feasible path is then determined by a comparative evaluation between the candidate paths. Field experiments with a rover prototype at a Lunar/Martian analog site were performed to confirm the feasibility of the proposed techniques, including the range‐dependent terrain mapping with C2DEM and the multipath‐planning method.

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“…This algorithm assures complete coverage of the given area. The complete coverage D* algorithm (Dakulovi et al, 2011) implements the path transform algorithm (Zelinsky et al, 1993) with D* (Stentz, 1994), to give nearly 100% coverage. The Backtracking Spiral Algorithm (Gonzalez et al, 2005) is an extension of the basic BSA algorithm (Gonzalez et al, 2003).…”

Section: Jcsmentioning

confidence: 99%

Traversal Algorithm for Complete Coverage

Thiayagarajan¹

2012

Journal of Computer Science

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There are many applications which require complete coverage and obstacle avoidance. The classical A* algorithm provides the user a shortest path by avoiding the obstacle. As well, the Dijkstraâs algorithm finds the shortest path between the source and destination. But in many applications we require complete coverage of the proposed area with obstacle avoidance. There are LSP, LSSP, BSA, spiral-STC and Complete Coverage D* algorithms which do not realize complete (100%) coverage. The complete coverage using a critical point algorithm assures complete coverage, but it is not well suited for applications like mine detection. Also for covering the missed region it keeps the obstacle as a critical point which is not advisable in critical applications where obstacle may be a dangerous one. To overcome this and to achieve the complete coverage we propose a novel graph traversal algorithm Traversal Algorithm for Complete Coverage (TRACC). Here the area to be scanned is decomposed into a finite number of cells. The traversal is done through all the cells after making sure the next cell has no obstacle. TRACC assures thorough coverage of the proposed area and ensuring that all the obstacles are avoided. Hence the TRACC always have the safer path while covering the entire area. It also reports the obstacle placed or blocked cell

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Optimal and efficient path planning for partially-known environments

Cited by 901 publications

References 9 publications

Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles

Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles

Range‐dependent Terrain Mapping and Multipath Planning using Cylindrical Coordinates for a Planetary Exploration Rover

Traversal Algorithm for Complete Coverage

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