Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models

Castaño, Fernando; Beruvides, Gerardo; Villalonga, Alberto; Haber, Rodolfo E.

doi:10.3390/s18051508

Cited by 45 publications

(37 citation statements)

References 34 publications

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“…These include Gazebo [29], USARSIM [30], Microsoft Robotics Developer Studio [31], and other simulators used to interactively design and debug autonomous systems in the early stage of development. Other more recent examples include customized simulations with simplified physics for closed-loop autonomy simulations in MATLAB [32,33].…”

Section: Discussionmentioning

confidence: 99%

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

et al. 2018

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Machine learning techniques have accelerated the development of autonomous navigation algorithms in recent years, especially algorithms for on-road autonomous navigation. However, off-road navigation in unstructured environments continues to challenge autonomous ground vehicles. Many off-road navigation systems rely on LIDAR to sense and classify the environment, but LIDAR sensors often fail to distinguish navigable vegetation from non-navigable solid obstacles. While other areas of autonomy have benefited from the use of simulation, there has not been a real-time LIDAR simulator that accounted for LIDAR–vegetation interaction. In this work, we outline the development of a real-time, physics-based LIDAR simulator for densely vegetated environments that can be used in the development of LIDAR processing algorithms for off-road autonomous navigation. We present a multi-step qualitative validation of the simulator, which includes the development of an improved statistical model for the range distribution of LIDAR returns in grass. As a demonstration of the simulator’s capability, we show an example of the simulator being used to evaluate autonomous navigation through vegetation. The results demonstrate the potential for using the simulation in the development and testing of algorithms for autonomous off-road navigation.

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Section: Discussionmentioning

confidence: 99%

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

et al. 2018

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“…Nowadays, several types of sensors can be used to detect obstacles. For example, in [38,39], light detection and ranging (LiDAR) sensors or stereoscopic cameras are used in obstacle avoidance control of robots. In [37,40], sonar is used as the sensor to detect obstacles for obstacle avoidance control of underwater robots.…”

Section: Obstacle Detection and Calculation Of The Penalty Term For Omentioning

confidence: 99%

Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

Yao

Wang

et al. 2020

Sensors

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This paper studies three-dimensional (3D) straight line path following and obstacle avoidance control for an underactuated autonomous underwater vehicle (AUV) without lateral and vertical driving forces. Firstly, the expected angular velocities are designed by using two different methods in the kinematic controller. The first one is a traditional method based on Line-of-sight (LOS) guidance law, and the second one is an improved method based on model predictive control (MPC). At the same time, a penalty item is designed by using the obstacle information detected by onboard sensors, which can realize the real-time obstacle avoidance of the unknown obstacle. Then, in order to overcome the uncertainty of the dynamics model and the saturation of actual control input, the dynamic controller is designed by using sliding mode control (SMC) technology. Finally, in the simulation experiment, the performance of the improved control method is verified by comparison with two traditional control methods based on LOS guidance law. Since the constraint of an AUV’s angular velocities are considered in MPC, simulation results show that the improved control method uses MPC, and SMC not only improves the tracking quality of the AUV when switching paths near the waypoints and realizes real-time obstacle avoidance but also effectively reduces the mean square error (MSE) and saturation rate of the rudder angle. Therefore, this control method is more conducive to the system stability and saves energy.

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“…In addition, the calculation of the accuracy of the estimated location of an object using the LiDAR sensor can be performed by other key performance indices. For example, the use of the Distance Root Mean Squared (DRMS) measure for the data that are tracked on the x-y plane (2D) and the Mean Radial Spherical Error (MRSE) measure for the data that are tracked in the x-y-z space (3D) were reported in [27,70]. Using derivable error formulas, any given random error and scan angle in the LiDAR range can be modelled and simulated.…”

Section: Sensor Reliability Assesmentmentioning

confidence: 99%

“…A reliability model is generated for both virtual and real IoT LiDAR sensors for predicting the accuracy error in obstacle detection. The procedure for developing these models is extracted from the methodology described in [27], with a different set of training data. In this study, a model-based procedure is used with a point-cloud clustering technique, in this case Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [92].…”

Section: Reliability Prediction Modelsmentioning

confidence: 99%

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

et al. 2019

Self Cite

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Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

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Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models

Cited by 45 publications

References 34 publications

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

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