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28 pages, 7059 KiB

Open AccessArticle

Guidance Point Generation-Based Cooperative UGV Teleoperation in Unstructured Environment

by Sen Zhu, Guangming Xiong, Huiyan Chen and Jianwei Gong

Sensors 2021, 21(7), 2323; https://doi.org/10.3390/s21072323 - 26 Mar 2021

Cited by 5 | Viewed by 2168

Teleoperation is widely used for unmanned ground vehicle (UGV) navigation in military and civilian fields. However, the human operator has to limit speed to ensure the handling stability because of the low resolution of video, limited field of view and time delay in [...] Read more.

Teleoperation is widely used for unmanned ground vehicle (UGV) navigation in military and civilian fields. However, the human operator has to limit speed to ensure the handling stability because of the low resolution of video, limited field of view and time delay in the control loop. In this paper, we propose a novel guidance point generation method that is well suited for human–machine cooperative UGV teleoperation in unstructured environments without a predefined goal position. The key novelty of this method is that the guidance points used for navigation can be generated with only the local perception information of the UGV. Firstly, the locally occupied grid map (OGM) was generated utilizing a probabilistic grid state description method, and converted into binary image to constructed the convex hull of obstacle area. Secondly, we proposed an improved thinning algorithm to extract skeletons of navigable regions from binary images, and find out the target skeleton related to the position of the UGV utilizing the k-nearest neighbor (kNN) algorithm. The target skeleton was reconstructed at the midline position of the navigable region using the decreasing gradient algorithm in order to obtain the appropriate skeleton end points for use as candidate guidance points. For visually presenting the driving trend of the UGV and convenient touch screen operation, we transformed guidance point selection into trajectory selection by generating the predicted trajectory correlative to candidate guidance points based on the differential equation of motion. Experimental results show that the proposed method significantly increases the speed of teleoperated UGV. Full article

(This article belongs to the Special Issue Advanced Sensing and Control for Mobile Robotic Systems)

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22 pages, 7579 KiB

Open AccessArticle

Reactive Self-Collision Avoidance for a Differentially Driven Mobile Manipulator

by Keunwoo Jang, Sanghyun Kim and Jaeheung Park

Sensors 2021, 21(3), 890; https://doi.org/10.3390/s21030890 - 28 Jan 2021

Cited by 9 | Viewed by 3321

Abstract

This paper introduces a reactive self-collision avoidance algorithm for differentially driven mobile manipulators. The proposed method mainly focuses on self-collision between a manipulator and the mobile robot. We introduce the concept of a distance buffer border (DBB), which is a 3D curved surface [...] Read more.

This paper introduces a reactive self-collision avoidance algorithm for differentially driven mobile manipulators. The proposed method mainly focuses on self-collision between a manipulator and the mobile robot. We introduce the concept of a distance buffer border (DBB), which is a 3D curved surface enclosing a buffer region of the mobile robot. The region has the thickness equal to buffer distance. When the distance between the manipulator and mobile robot is less than the buffer distance, which means the manipulator lies inside the buffer region of the mobile robot, the proposed strategy is to move the mobile robot away from the manipulator in order for the manipulator to be placed outside the border of the region, the DBB. The strategy is achieved by exerting force on the mobile robot. Therefore, the manipulator can avoid self-collision with the mobile robot without modifying the predefined motion of the manipulator in a world Cartesian coordinate frame. In particular, the direction of the force is determined by considering the non-holonomic constraint of the differentially driven mobile robot. Additionally, the reachability of the manipulator is considered to arrive at a configuration in which the manipulator can be more maneuverable. In this respect, the proposed algorithm has a distinct advantage over existing avoidance methods that do not consider the non-holonomic constraint of the mobile robot and push links away from each other without considering the workspace. To realize the desired force and resulting torque, an avoidance task is constructed by converting them into the accelerations of the mobile robot. The avoidance task is smoothly inserted with a top priority into the controller based on hierarchical quadratic programming. The proposed algorithm was implemented on a differentially driven mobile robot with a 7-DOFs robotic arm and its performance was demonstrated in various experimental scenarios. Full article

(This article belongs to the Special Issue Advanced Sensing and Control for Mobile Robotic Systems)

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22 pages, 1483 KiB

Open AccessArticle

Development of a Hybrid Path Planning Algorithm and a Bio-Inspired Control for an Omni-Wheel Mobile Robot

by Changwon Kim, Junho Suh and Je-Heon Han

Sensors 2020, 20(15), 4258; https://doi.org/10.3390/s20154258 - 30 Jul 2020

Cited by 32 | Viewed by 3891

Abstract

This research presents a control structure for an omni-wheel mobile robot (OWMR). The control structure includes the path planning module and the motion control module. In order to secure the robustness and fast control performance required in the operating environment of OWMR, a [...] Read more.

This research presents a control structure for an omni-wheel mobile robot (OWMR). The control structure includes the path planning module and the motion control module. In order to secure the robustness and fast control performance required in the operating environment of OWMR, a bio-inspired control method, brain limbic system (BLS)-based control, was applied. Based on the derived OWMR kinematic model, a motion controller was designed. Additionally, an optimal path planning module is suggested by combining the advantages of A* algorithm and the fuzzy analytic hierarchy process (FAHP). In order to verify the performance of the proposed motion control strategy and path planning algorithm, numerical simulations were conducted. Through a point-to-point movement task, circular path tracking task, and randomly moving target tracking task, it was confirmed that the suggesting motion controller is superior to the existing controllers, such as PID. In addition, A*–FAHP was applied to the OWMR to verify the performance of the proposed path planning algorithm, and it was simulated based on the static warehouse environment, dynamic warehouse environment, and autonomous ballet parking scenarios. The simulation results demonstrated that the proposed algorithm generates the optimal path in a short time without collision with stop and moving obstacles. Full article

(This article belongs to the Special Issue Advanced Sensing and Control for Mobile Robotic Systems)

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27 pages, 11335 KiB

Open AccessArticle

An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments

by Valentina Karolj, Alberto Viseras, Luis Merino and Dmitriy Shutin

Sensors 2020, 20(13), 3663; https://doi.org/10.3390/s20133663 - 30 Jun 2020

Cited by 3 | Viewed by 2406

Abstract

Exploration of spatial processes, such as radioactivity or temperature is a fundamental task in many robotic applications. In the literature, robotic exploration is mainly carried out for applications where the environment is a priori known. However, for most real life applications this assumption [...] Read more.

Exploration of spatial processes, such as radioactivity or temperature is a fundamental task in many robotic applications. In the literature, robotic exploration is mainly carried out for applications where the environment is a priori known. However, for most real life applications this assumption often does not hold, specifically for disaster scenarios. In this paper, we propose a novel integrated strategy that allows a robot to explore a spatial process of interest in an unknown environment. To this end, we build upon two major blocks. First, we propose the use of GP to model the spatial process of interest, and process entropy to drive the exploration. Second, we employ registration algorithms for robot mapping and localization, and frontier-based exploration to explore the environment. However, map and process exploration can be conflicting goals. Our integrated strategy fuses the two aforementioned blocks through a trade-off between process and map exploration. We carry out extensive evaluations of our algorithm in simulated environments with respect to different baselines and environment setups using simulated GP data as a process at hand. Additionally, we perform experimental verification with a mobile holonomic robot exploring a simulated process in an unknown labyrinth environment. Demonstrated results show that our integrated strategy outperforms both frontier-based and GP entropy-driven exploration strategies. Full article

(This article belongs to the Special Issue Advanced Sensing and Control for Mobile Robotic Systems)

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14 pages, 5194 KiB

Open AccessArticle

Visual Saliency Detection for Over-Temperature Regions in 3D Space via Dual-Source Images

by Dawei Gong, Zhiheng He, Xiaolong Ye and Ziyun Fang

Sensors 2020, 20(12), 3414; https://doi.org/10.3390/s20123414 - 17 Jun 2020

Cited by 2 | Viewed by 1869

Abstract

To allow mobile robots to visually observe the temperature of equipment in complex industrial environments and work on temperature anomalies in time, it is necessary to accurately find the coordinates of temperature anomalies and obtain information on the surrounding obstacles. This paper proposes [...] Read more.

To allow mobile robots to visually observe the temperature of equipment in complex industrial environments and work on temperature anomalies in time, it is necessary to accurately find the coordinates of temperature anomalies and obtain information on the surrounding obstacles. This paper proposes a visual saliency detection method for hypertemperature in three-dimensional space through dual-source images. The key novelty of this method is that it can achieve accurate salient object detection without relying on high-performance hardware equipment. First, the redundant point clouds are removed through adaptive sampling to reduce the computational memory. Second, the original images are merged with infrared images and the dense point clouds are surface-mapped to visually display the temperature of the reconstructed surface and use infrared imaging characteristics to detect the plane coordinates of temperature anomalies. Finally, transformation mapping is coordinated according to the pose relationship to obtain the spatial position. Experimental results show that this method not only displays the temperature of the device directly but also accurately obtains the spatial coordinates of the heat source without relying on a high-performance computing platform. Full article

(This article belongs to the Special Issue Advanced Sensing and Control for Mobile Robotic Systems)

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23 pages, 3309 KiB

Open AccessArticle

Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots

by Zheyu Liu, Craig West, Barry Lennox and Farshad Arvin

Sensors 2020, 20(11), 3308; https://doi.org/10.3390/s20113308 - 10 Jun 2020

Cited by 10 | Viewed by 3938

Abstract

Swarm robotics focuses on decentralised control of large numbers of simple robots with limited capabilities. Decentralised control in a swarm system requires a reliable communication link between the individuals that is able to provide linear and angular distances between the individuals—Range & [...] Read more.

Swarm robotics focuses on decentralised control of large numbers of simple robots with limited capabilities. Decentralised control in a swarm system requires a reliable communication link between the individuals that is able to provide linear and angular distances between the individuals—Range & Bearing. This study presents the development of an open-source, low-cost communication module which can be attached to miniature sized robots; e.g., Mona. In this study, we only focused on bearing estimation to mathematically model the bearings of neighbouring robots through systematic experiments using real robots. In addition, the model parameters were optimised using a genetic algorithm to provide a reliable and precise model that can be applied for all robots in a swarm. For further investigation and improvement of the system, an additional layer of optimisation on the hardware layout was implemented. The results from the optimisation suggested a new arrangement of the sensors with slight angular displacements on the developed board. The precision of bearing was significantly improved by optimising in both software level and re-arrangement of the sensors’ positions on the hardware layout. Full article

(This article belongs to the Special Issue Advanced Sensing and Control for Mobile Robotic Systems)

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19 pages, 1641 KiB

Open AccessArticle

A Fuzzy Analytic Hierarchy Process and Cooperative Game Theory Combined Multiple Mobile Robot Navigation Algorithm

by Changwon Kim and Jong-Seob Won

Sensors 2020, 20(10), 2827; https://doi.org/10.3390/s20102827 - 16 May 2020

Cited by 12 | Viewed by 2754

Abstract

This study presents a multi-robot navigation strategy based on a multi-objective decision-making algorithm, the Fuzzy Analytic Hierarchy Process (FAHP). FAHP analytically selects an optimal position as a sub-goal among points on the sensing boundary of a mobile robot considering the following three objectives: [...] Read more.

This study presents a multi-robot navigation strategy based on a multi-objective decision-making algorithm, the Fuzzy Analytic Hierarchy Process (FAHP). FAHP analytically selects an optimal position as a sub-goal among points on the sensing boundary of a mobile robot considering the following three objectives: the travel distance to the target, collision safety with obstacles, and the rotation of the robot to face the target. Alternative solutions are evaluated by quantifying the relative importance of the objectives. As the FAHP algorithm is insufficient for multi-robot navigation, cooperative game theory is added to improve it. The performance of the proposed multi-robot navigation algorithm is tested with up to 12 mobile robots in several simulation conditions, altering factors such as the number of operating robots and the warehouse layout. Full article

(This article belongs to the Special Issue Advanced Sensing and Control for Mobile Robotic Systems)

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Other

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15 pages, 6110 KiB

Open AccessLetter

Deep Reinforcement Learning for Indoor Mobile Robot Path Planning

by Junli Gao, Weijie Ye, Jing Guo and Zhongjuan Li

Sensors 2020, 20(19), 5493; https://doi.org/10.3390/s20195493 - 25 Sep 2020

Cited by 96 | Viewed by 10860

Abstract

This paper proposes a novel incremental training mode to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the related graphic search algorithms and Reinforcement Learning (RL) algorithms in a lightweight 2D environment. Then, [...] Read more.

This paper proposes a novel incremental training mode to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the related graphic search algorithms and Reinforcement Learning (RL) algorithms in a lightweight 2D environment. Then, we design the algorithm based on DRL, including observation states, reward function, network structure as well as parameters optimization, in a 2D environment to circumvent the time-consuming works for a 3D environment. We transfer the designed algorithm to a simple 3D environment for retraining to obtain the converged network parameters, including the weights and biases of deep neural network (DNN), etc. Using these parameters as initial values, we continue to train the model in a complex 3D environment. To improve the generalization of the model in different scenes, we propose to combine the DRL algorithm Twin Delayed Deep Deterministic policy gradients (TD3) with the traditional global path planning algorithm Probabilistic Roadmap (PRM) as a novel path planner (PRM+TD3). Experimental results show that the incremental training mode can notably improve the development efficiency. Moreover, the PRM+TD3 path planner can effectively improve the generalization of the model. Full article

(This article belongs to the Special Issue Advanced Sensing and Control for Mobile Robotic Systems)

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