Optimization of 3D laser scanning speed by use of combined variable step

Garcia-Cruz, X.M.; Sergiyenko, Oleg; Tyrsa, Vera; Rivas-López, Moisés; Hernández-Balbuena, Daniel; Rodríguez-Quiñonez, Julio C.; Básaca-Preciado, Luís C.; Mercorelli, Paolo

doi:10.1016/j.optlaseng.2013.08.011

Cited by 51 publications

(29 citation statements)

References 20 publications

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“…Segmentation can recognize traversable planes from a visual image or point cloud [18][19][20]. Laser, sonar, and other range sensors can detect obstacles and their shape to determine in which direction to move [14,[21][22][23]. Especially, sensor readings from multiple robots through a centralized communications network are used [24].…”

Section: Related Workmentioning

confidence: 99%

Goal-Oriented Obstacle Avoidance with Deep Reinforcement Learning in Continuous Action Space

2020

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In this paper, we propose a goal-oriented obstacle avoidance navigation system based on deep reinforcement learning that uses depth information in scenes, as well as goal position in polar coordinates as state inputs. The control signals for robot motion are output in a continuous action space. We devise a deep deterministic policy gradient network with the inclusion of depth-wise separable convolution layers to process the large amounts of sequential depth image information. The goal-oriented obstacle avoidance navigation is performed without prior knowledge of the environment or a map. We show that through the proposed deep reinforcement learning network, a goal-oriented collision avoidance model can be trained end-to-end without manual tuning or supervision by a human operator. We train our model in a simulation, and the resulting network is directly transferred to other environments. Experiments show the capability of the trained network to navigate safely around obstacles and arrive at the designated goal positions in the simulation, as well as in the real world. The proposed method exhibits higher reliability than the compared approaches when navigating around obstacles with complex shapes. The experiments show that the approach is capable of avoiding not only static, but also dynamic obstacles.

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Section: Related Workmentioning

confidence: 99%

Goal-Oriented Obstacle Avoidance with Deep Reinforcement Learning in Continuous Action Space

2020

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“…To acquire the map data of tested environments, we firstly constructed grayscale maps via real data from laser sensors, and then transform them to grid maps (if a cell is occupied by an obstacle, this cell is set to be untraversable; otherwise, it is traversable). The distance measurement accuracy can be improved by optimization methods [36]. The dynamics of environments are caused by moving obstacles that imitate the simple behaviors of people (i.e., linear motion with constant speed).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments

Zeng

Qin

et al. 2019

Applied Sciences

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Since an individual approach can hardly navigate robots through complex environments, we present a novel two-level hierarchical framework called JPS-IA3C (Jump Point Search improved Asynchronous Advantage Actor-Critic) in this paper for robot navigation in dynamic environments through continuous controlling signals. Its global planner JPS+ (P) is a variant of JPS (Jump Point Search), which efficiently computes an abstract path of neighboring jump points. These nodes, which are seen as subgoals, completely rid Deep Reinforcement Learning (DRL)-based controllers of notorious local minima. To satisfy the kinetic constraints and be adaptive to changing environments, we propose an improved A3C (IA3C) algorithm to learn the control policies of the robots’ local motion. Moreover, the combination of modified curriculum learning and reward shaping helps IA3C build a novel reward function framework to avoid learning inefficiency because of sparse reward. We additionally strengthen the robots’ temporal reasoning of the environments by a memory-based network. These improvements make the IA3C controller converge faster and become more adaptive to incomplete, noisy information caused by partial observability. Simulated experiments show that compared with existing methods, this JPS-IA3C hierarchy successfully outputs continuous commands to accomplish large-range navigation tasks at shorter paths and less time through reasonable subgoal selection and rational motions.

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“…Extreme importance of one element relative to another 2, 4,6,8 Intermediate values between two adjacent judgments Specifically, O n represent the n-th objectives. When three objectives are considered, the RM is formalized by using the following structure:…”

Section: Intensity Of Importance Definitionmentioning

confidence: 99%

“…One of the important functions on mobile robots for the completion of the required tasks is to reach a target successfully by means of path planning. To this end, there have been many efforts to implement the capability of robot's path planning [1][2][3][4][5][6]. Beyond the consideration on single objective aspect, i.e., traveling distance or collision safety with obstacles, the majority of current optimal path planning research has focused on multi-objective solving problems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Analytic Hierarchy Process-Based Mobile Robot Path Planning

Kim

2020

Electronics

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This study presents a new path planning method based on Fuzzy Analytic Hierarchy Process (FAHP) for a mobile robot to be effectively operated through a multi-objective decision making problem. Unlike typical AHP, the proposed FAHP has a difference in using triangulation fuzzy number based extent analysis to derive weight vectors among the considerations. FAHP framework for finding the optimal position in this study is defined with the highest level (goal), middle level (objectives), and the lowest level (alternatives). It analytically selects an optimal position as a sub-goal among points on the sensing boundary of the mobile robot considering the three objectives: the travel distance to the target, robot’s rotation, and safety against collision between obstacles. Alternative solutions are evaluated by quantifying the relative importance for the objectives. Comparative results obtained from the artificial potential field, AHP, and FAHP simulations show that FAHP is much preferable for mobile robot’s path planning than typical AHP.

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Optimization of 3D laser scanning speed by use of combined variable step

Cited by 51 publications

References 20 publications

Goal-Oriented Obstacle Avoidance with Deep Reinforcement Learning in Continuous Action Space

Goal-Oriented Obstacle Avoidance with Deep Reinforcement Learning in Continuous Action Space

Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments

Fuzzy Analytic Hierarchy Process-Based Mobile Robot Path Planning

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