A comprehensive review of plume source detection using unmanned vehicles for environmental sensing

Lewis, Tyrell; Bhaganagar, Kiran

doi:10.1016/j.scitotenv.2020.144029

Cited by 17 publications

(8 citation statements)

References 80 publications

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“…For example, the use of semiautomatic shared control can assist in search and rescue operations [6] where the fidelity of sensing systems may be partially compromised by extreme environmental conditions, such as smoke, dust, water, and snow, or when an expert's experience can be used to guide a search to areas where victims are most likely to be found in a complex, cluttered disaster scene, such as after an earthquake, avalanche or building collapse. The use of shared control can also reduce mission times and limit risks to both first responders and victims by providing a means to assess the stability of damaged structures and preplan a rescue mission using visual observations from thermal cameras [7], or measurements of gas/chemical concentrations [8] obtained during a rapid UGV survey. Other applications which can greatly benefit from the shared control of uncrewed ground vehicles by taking advantage of the presence of a human expert include: 1) precision agriculture, where a farmer's experience can be used to guide spraying, inspection, and harvesting operations [9], [10], [11]; 2) infrastructure inspection [12]; 3) firefighting, where UGVs can be used to determine the location of fires and the presence of dangerous gases to assist with intervention planning [13]; 4) mining [14]; and 5) in humanitarian/relief operations, such as the detection and removal of land mines [15].…”

Section: Introductionmentioning

confidence: 99%

Shared control with obstacle avoidance for UGVs

Bou,

Beck,

Ellenrieder

et al. 2024

Preprint

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A shared human-robot trajectory tracking controller with collision avoidance is designed for a differentially steered uncrewed ground vehicle. A mixed-initiative interaction is considered, in which a linear blending law combines the human commands with the automatic controller using a passive measurement of the human intent. The automatic control input includes a trajectory tracking controller, designed using a Lyapunov-based approach, and a reactive collision avoidance controller, designed using a relaxed control barrier function. The asymptotic stability of the closed-loop shared human-robot system is analytically proven. An experimental demonstration for the proposed shared control was implemented in a crowded indoor environment with static and moving obstacles. The robot’s dimensions are 72 cm x 58 cm x 55 cm (length x width x height). The crowded indoor environment is a 7 m x 15 m room with two doors with a 90 cm width and a 2 m x 20 m corridor. The tracking error was less than 0.03 m, and the robot avoided collision with three static obstacles placed on the reference trajectory and one virtual moving obstacle. The human user can drive the robot away from the reference trajectory when desired, and when he/she relinquishes control, the robot quickly returns to the reference trajectory.

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Section: Introductionmentioning

confidence: 99%

Shared control with obstacle avoidance for UGVs

Bou,

Beck,

Ellenrieder

et al. 2024

Preprint

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“…The dynamics of the autonomous navigation landscape change and the effectiveness and robustness of path planning algorithms are critical for the operational success of Unmanned Ground Vehicles (UGVs) [1][2][3][4][5][6][7][8][9][10][11][12]. These vehicles are used in various applications including military operations [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], disaster reliefs, as well as agricultural automation and require sophisticated navigation techniques that can allow them to navigate safely and effectively through difficult environments [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. Traditional path planning methods mainly face an optimization task of path length and computational efficiency in dynamic and unpredictable terrains .…”

Section: Introductionmentioning

confidence: 99%

Path planning for a UGV using Salp Swarm Algorithm

AlShabi,

Ballous,

Bou Nassif

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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The paper researches a utilization of the Salp Swarm Algorithm (SSA), a bio-mimetic optimization technique, to improve path planning in Unmanned Ground Vehicles (UGVs). Because of the crucial role of the efficient and reliable path planning in the implementation of UGVs in such sectors as military, rescue operations, and agriculture, there is a need for algorithms that are capable of navigating complex environments. The concept of SSA, based on the natural swarming behavior of salps, represents a very promising approach that is characterized by the exploration and exploitation properties of the algorithm. This study evaluates the performance of the SSA relative to existing particle swarm optimization (PSO), in terms of path optimality, computational efficiency, and dynamic obstacle adaptability, through a number of simulated environments. Results show that the SSA has the potential to compete with the traditional algorithms in path efficiency and computational load. However, PSO shows slight superiority results compared to SSA. This study highlights the potency of bio-inspired algorithms, specifically the SSA, in enhancing the field of autonomous navigation for UGVs. It introduces new possibilities of practical application of SSA in real-life scenarios, demonstrating its scalability and resilience. The findings of this study make a contribution to the general discussion on the improvement of planning of autonomous routes and provide a possible way for more sustainable and effective UGV activities.

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“…Unmanned systems include the unmanned aerial vehicle (UAV), unmanned ground vehicle (UGV), unmanned surface vehicle (USV), autonomous underwater vehicle (AUV), field robot, and other unmanned platforms and equipment. Various types of unmanned systems for ground, sea, aviation, and aerospace application are oriented to different physical domain-sensing tasks, thereby integrating different multimode sensing chips and sensor devices and eventually forming distinctive configuration features and sensing advantages, respectively [7][8][9]. With the advent of the digital intelligence era, unmanned systems are gradually developing into anthropomorphic living subjects with a certain degree of autonomy.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Multi-Agent Cross Domain Cooperative Perception

Zhu

Wang

et al. 2022

Electronics

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Intelligent unmanned systems for ground, sea, aviation, and aerospace application are important research directions for the new generation of artificial intelligence in China. Intelligent unmanned systems are also important carriers of interactive mapping between physical space and cyberspace in the process of the digitization of human society. Based on the current domestic and overseas development status of unmanned systems for ground, sea, aviation, and aerospace application, this paper reviewed the theoretical problems and research trends of multi-agent cross-domain cooperative perception. The scenarios of multi-agent cooperative perception tasks in different areas were deeply investigated and analyzed, the scientific problems of cooperative perception were analyzed, and the development direction of multi-agent cooperative perception theory research for solving the challenges of the complex environment, interactive communication, and cross-domain tasks was expounded.

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A comprehensive review of plume source detection using unmanned vehicles for environmental sensing

Cited by 17 publications

References 80 publications

Shared control with obstacle avoidance for UGVs

Shared control with obstacle avoidance for UGVs

Path planning for a UGV using Salp Swarm Algorithm

A Survey of Multi-Agent Cross Domain Cooperative Perception

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