A Real-Time Reaction Obstacle Avoidance Algorithm for Autonomous Underwater Vehicles in Unknown Environments

Yan, Zheping; Li, Jiyun; Zhang, Gengshi; Wu, Yi

doi:10.3390/s18020438

Cited by 29 publications

(23 citation statements)

References 24 publications

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“…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. In this paper, the AUV is configured with sonaras shown in Figure 2 received data ρ, it means there is an obstacle ahead.…”

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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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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“…Zhang et al [26] improved the wolf pack algorithm to solve the 3-D underwater path planning considering terrain obstacles in the peak shape. Yan et al [27] classified irregular obstacles into four types and accordingly designed obstacle avoidance rules.…”

Section: B Auv Path Planning Under Dense Obstaclesmentioning

confidence: 99%

ACO-A: Ant Colony Optimization Plus A for 3-D Traveling in Environments With Dense Obstacles

Xue

Chen

et al. 2019

IEEE Trans. Evol. Computat.

114

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Path planning is one of the most important problems in the development of autonomous underwater vehicles (AUVs). In some common AUV missions, e.g., wreckage search for rescue, an AUV is often required to traverse multiple targets in a complex environment with dense obstacles. In such case, the AUV path planning problem becomes even more challenging. In order to address the problem, this paper develops a two-layer algorithm, namely ACO-A*, by combining the ant colony optimization (ACO) with the A* search. Once a mission with a set of arbitrary targets is assigned, ACO is responsible to determine the traveling order of targets. But, prior to ACO, a cost graph indicating the necessary traveling costs among targets must be quickly established to facilitate traveling order evaluation. For this purpose, a coarse-grained modeling with a representativebased estimation (RBE) strategy is proposed. Following the order obtained by ACO, targets will be traversed one by one and the pairwise path planning to reach each target can be performed during vehicle driving. To deal with the dense obstacles, A* is adopted to plan paths based on a fine-grained modeling and an admissible heuristic function is designed for A* to guarantee its optimality. Experiments on both synthetic and realistic scenarios have been designed to validate the efficiency of the proposed ACO-A*, as well as the effectiveness of RBE and the necessity of A*.

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“…A "multi-objective model predictive control (MO-MPC)" has been proposed on the Saab Sea Eye Falcon open-frame ROV/AUV and validated as an effective PPC by Shen et al [24] In order to improve the performance of AUVs deployed in different applications such as oceanographic survey, search and detection of mines in military missions, it is necessary to develop an appropriate path planning controller which should provide precise and fast control of the propeller system of an AUV. Different PPC employed for formation control of multiple AUVs to follow a specified path while retaining a desired spatial pattern are reviewed by Das et al [25] Guerrero et al [26] introduced a second-order SMC named "generalized super-twisting algorithm (GSTA)" for automatic gain adjustment to cope with external disturbances along with uncertain dynamic errors. Yan et al [27] and Li et al [28] respectively proposed a "real-time reaction obstacle avoidance algorithm (RRA)" and a "predictive guidance obstacle avoidance algorithm (PGOA)" to deal with complicated terrain structure in the unpredictable oceanic environment based on information provided by "forward looking sonar (FLS)".…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Panda

Subudhi

Pati

2020

Int. J. Autom. Comput.

148

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The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments. The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles (AUVs) because of its hostile and dynamic nature. The major constraints for path planning are limited data transmission capability, power and sensing technology available for underwater operations. The sea environment is subjected to a large set of challenging factors classified as atmospheric, coastal and gravitational. Based on whether the impact of these factors can be approximated or not, the underwater environment can be characterized as predictable and unpredictable respectively. The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner. But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted. Path planning is necessary for many applications involving AUVs. These are based upon planning safety routes with minimum energy cost and computation overheads. This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable. The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.

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A Real-Time Reaction Obstacle Avoidance Algorithm for Autonomous Underwater Vehicles in Unknown Environments

Cited by 29 publications

References 24 publications

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

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

ACO-A: Ant Colony Optimization Plus A for 3-D Traveling in Environments With Dense Obstacles

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

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A Real-Time Reaction Obstacle Avoidance Algorithm for Autonomous Underwater Vehicles in Unknown Environments

Cited by 29 publications

References 24 publications

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

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

ACO-A*: Ant Colony Optimization Plus A* for 3-D Traveling in Environments With Dense Obstacles

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

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ACO-A: Ant Colony Optimization Plus A for 3-D Traveling in Environments With Dense Obstacles