Dynamic window based approaches for avoiding obstacles in moving

Molinos, Eduardo J.; Llamazares, Ángel; Ocaña, Manuel

doi:10.1016/j.robot.2019.05.003

Cited by 70 publications

(32 citation statements)

References 15 publications

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“…However, avoiding them like static ones can result in non-optimal trajectories and risky situations, especially if the measurements of the environment are not accurate enough or even the obstacles’ direction suddenly change. To take into account these matters, in our previous work [ 15 ] we proposed two algorithms that deals with dynamic obstacles.…”

Section: Test Bed and Resultsmentioning

confidence: 99%

“…In this section, we test the improvement of using iDOMap in Dynamic Curvature Velocity Method or DCVM [ 37 ] and Dynamic Window for Dynamic Obstacles or DW4DO [ 15 ] avoidance algorithms.…”

Section: Test Bed and Resultsmentioning

confidence: 99%

“…Although these mapping methods can be useful for the most of the obstacle-avoidance algorithms in static or quasi-static environments, when they have to deal with a high number of dynamic objects it might not be enough and some local planners have to include new innovations [ 14 , 15 ]. It is because they only take into account the occupancy of static obstacles providing, therefore, an incorrect modelling of the dynamic ones.…”

Section: Introductionmentioning

confidence: 99%

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Improved Dynamic Obstacle Mapping (iDOMap)

Llamazares

Molinos

Ocaña

et al. 2020

Sensors

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The goal of this paper is to improve our previous Dynamic Obstacle Mapping (DOMap) system by means of improving the perception stage. The new system must deal with robots and people as dynamic obstacles using LIght Detection And Range (LIDAR) sensor in order to collect the surrounding information. Although robot movement can be easily tracked by an Extended Kalman Filter (EKF), people’s movement is more unpredictable and it might not be correctly linearized by an EKF. Therefore, to deal with a better estimation of both types of dynamic objects in the local map it is recommended to improve our previous work. The DOMap has been extended in three key points: first the LIDAR reflectivity remission is used to make more robust the matching in the optical flow of the detection stage, secondly static and a dynamic occlusion detectors have been proposed, and finally a tracking stage based on Particle Filter (PF) has been used to deal with robots and people as dynamic obstacles. Therefore, our new improved-DOMap (iDOMap) provides maps with information about occupancy and velocities of the surrounding dynamic obstacles (robots, people, etc.) in a more robust way and they are available to improve the following planning stage.

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Section: Test Bed and Resultsmentioning

confidence: 99%

“…In this section, we test the improvement of using iDOMap in Dynamic Curvature Velocity Method or DCVM [ 37 ] and Dynamic Window for Dynamic Obstacles or DW4DO [ 15 ] avoidance algorithms.…”

Section: Test Bed and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved Dynamic Obstacle Mapping (iDOMap)

Llamazares

Molinos

Ocaña

et al. 2020

Sensors

Self Cite

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“…where x(t) and y(t) are the UE's coordinate at time t in the cartesian coordinate system, while the UE's orientation is dictated by θ(t) and t 0 is the initial time while t n can be any time when the UE is moving. The motion of the UE is constrained in a way that the translational velocity v always leads in the steering direction θ of the UE, which is called a non-holonomic constraint [42]. In the DWA, the search for commands controlling the UE is carried out directly in the space of velocities.…”

Section: Ue Trajectory Prediction Designmentioning

confidence: 99%

Deep Learning Enabled Beam Tracking for Non-Line of Sight Millimeter Wave Communications

Wang

Klaine

Onireti

et al. 2021

IEEE Open J. Commun. Soc.

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To solve the complex beam alignment issue in non-line-of-sight (NLOS) millimeter wave communications, this paper presents a deep neural network (DNN) based procedure to predict the angle of arrival (AOA) and angle of departure (AOD) both in terms of azimuth and elevation, i.e., AAOA/AAOD and EAOA/EAOD. In order to evaluate the performance of the proposed procedure under practical assumptions, we employ a trajectory prediction method by considering dynamic window approach (DWA) to estimate the location information of the user equipment (UE), which is utilized as the input parameter of the trained DNN to generate the prediction of AAOA/AAOD and EAOA/EAOD. The robustness of the prediction procedure is analyzed in the presence of prediction errors, which proves that the proposed DNN is a promising tool to predict AOA and AOD in NLOS scenarios based on the estimated UE location. Simulation results shows that the prediction errors of the AOA and AOD can be maintained within an acceptable range of ±2 • .

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“…Local obstacle avoidance techniques are employed online in the absence of global information, typically via popular methods such as artificial potential fields (APFs) [11], vector field histograms (VFH) [12], and the dynamic window approach (DWA) [13], which extends local avoidance approaches on account of kinematics constraints. Literature is abundant with various strategies proposed to address the problem of obstacle avoidance of automated robots [14]- [23].…”

Section: Introductionmentioning

confidence: 99%

Optimal Path-Planning of Nonholonomic Terrain Robots for Dynamic Obstacle Avoidance Using Single-Time Velocity Estimator and Reinforcement Learning Approach

Taghavifar

Taghavifar³

et al. 2019

IEEE Access

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A single-time velocity estimator-based reinforcement learning (RL) algorithm, integrated with a chaotic metaheuristic optimization technique is proposed in this article for the optimal path-planning of the nonholonomic robots considering a moving/stationary obstacle avoidance strategy. The additional contribution of the present study is by employing the Terramechanics principles to incorporate the effects of wheel sinkage into the deformable terrain on the dynamics of the robot aiming to find the optimal compensating force/torque magnitude to sustain a robust and smooth motion. The designed systematic control-oriented system incorporates a cost function of weighted components associated with the targettracking and the obstacle avoidance. The designed velocity estimator contributes to the finite-state Markov decision process (MDP) in order to train the transition probabilities of the problem objectives. Based on the obtained results, the optimal solution for the Q-learning in terms of the adjusting factor for the minimized tracking error and obstacle collision risk propagation profiles is found at 0.22. The results further confirm the promising capacity of the proposed optimization-based RL algorithm for the collision avoidance control of the nonholonomic robots on deformable terrains. INDEX TERMS Mechatronics, terramechanics, path-planning, artificial intelligence.

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Dynamic window based approaches for avoiding obstacles in moving

Cited by 70 publications

References 15 publications

Improved Dynamic Obstacle Mapping (iDOMap)

Improved Dynamic Obstacle Mapping (iDOMap)

Deep Learning Enabled Beam Tracking for Non-Line of Sight Millimeter Wave Communications

Optimal Path-Planning of Nonholonomic Terrain Robots for Dynamic Obstacle Avoidance Using Single-Time Velocity Estimator and Reinforcement Learning Approach

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