Path planning and obstacle avoidance for AUV: A review

Cheng, Chunxi; Sha, Qixin; He, Bo; Li, Guangliang

doi:10.1016/j.oceaneng.2021.109355

Cited by 154 publications

(58 citation statements)

References 83 publications

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“…This on-line planner creates the initial mission similar to its offline counterpart (with fixed mission parameters and goals), but is fed selected sensor data which enables it to react to mission-relevant information and alter or re-plan the mission. A very basic example would be the case of obstacle-avoidance [118], where a sensor (e.g. forward-looking sonar) detects an obstacle in the vehicle's path and reports this to the on-line planning system.…”

Section: Perception and Navigationmentioning

confidence: 99%

Recent Advances in AI for Navigation and Control of Underwater Robots

et al. 2022

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Purpose of Review The goal of this paper is to review current developments in the area of underwater robotics regarding the use of AI, especially in model learning, robot control, perception and navigation as well as manipulation. Recent Findings AI technologies and advanced control techniques are finding their way into robotics systems to deal with complex and challenging conditions and to equip them with higher levels of autonomy. Summary Although AI techniques and concepts are already a focus area in research on autonomous underwater systems, broad adoption to commercial systems is still in its infancy. Nonetheless, major advances have been done in recent years, especially on integrating different capabilities (perception, navigation, advanced control) in a single system and with first approaches on interaction and autonomous manipulation.

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Section: Perception and Navigationmentioning

confidence: 99%

Recent Advances in AI for Navigation and Control of Underwater Robots

et al. 2022

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“…The x init is the starting node and x rand is the target node. By collision detection of random sampling points in the state space, the nearest node x near to the target node can be found, expanding the node x new into the open undetected area [33].…”

Section: Rapid-exploring Random Treesmentioning

confidence: 99%

“…The G best is called the global extremum, that is, the optimal solution in the whole swarm of particles. The whole process of the particle swarm optimization algorithm is to use the particles' velocity, V, current position X, P best and G best information iterate until an optimal solution is found [33]. Particle swarm algorithm is widely used in UAV PP problem.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

An overview of path planning technologies for unmanned aerial vehicles

Bal

2022

THERM SCI

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Unmanned aerial vehicles, due to their superior maneuverability and reduced costs can easily perform tasks that are too difficult and complex to be performed with manned aircraft, under all conditions. In order to cope with various obstacles and operate in complex and unstable environmental conditions, the unmanned aerial vehicles must first plan its path. One of the most important problems to investigated in order to find an optimal path between the starting point and the target point of the unmanned aerial vehicles is path planning and choosing the appropriate algorithm. These algorithms find the optimal and shortest path, and also provide a collision-free environment for unmanned aerial vehicles. It is important to have path planning algorithms to calculate a safe path to the final destination in the shortest possible time. However, algorithms are not guaranteed to provide full performance in each path planning situation. Also, each algorithm has some specifications, these specifications make it possible to make them suitable in complex situations. Although there are many studies in path planning literature, this subject is still an active research area considering the high maneuverability of unmanned aerial vehicles. In this study, the most used methods of graph search, sampling-based algorithms and computational intelligence-based algorithms, which have become one of the important technologies for unmanned aerial vehicles and have been the subject of extensive research, are examined and their pros and cons are emphasized. In addition, studies conducted in the field of unmanned aerial vehicles with these algorithms are also briefly mentioned.

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“…Global planning depends heavily on the information of the current navigation plane that the agent currently navigating. This information is used for precalculation for path planning which is unsuitable for the dynamic environment in evacuation scenarios, where obstacles and agents' positions are constantly on move [3], [7], [10]- [19]. Global planning is only viable as long as the information of navigation space is available for the navigating agents.…”

Section: Introductionmentioning

confidence: 99%

Crowd evacuation navigation for evasive maneuver of brownian based dynamic obstacles using reciprocal velocity obstacles

et al. 2022

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This paper presents an approach for evasive maneuver against dynamic obstacles in multi-agent navigation in a crowd evacuation scenario. Our proposed approach is based on reciprocal velocity obstacles (RVO) with a different manner to treat the obstacles. We treat all possible hindrances in velocity space reciprocally thus all collision cones generated by other agents and obstacles are treated in the same RVO manner with the key difference in the effort of avoidance. Our approach assumes that dynamic obstacles bear no awareness of navigation space unlike agents thus the avoidance effort lies on behalf of the mobile agents, creating unmutual effort in an evasive maneuver. We display our approach in an evacuation scenario where a crowd of agents must navigate through an evacuation area trespassing zone filled with dynamic obstacles. These dynamic obstacles consist of random motion built based on Brownian motion thus posses an immense challenge for the mobile agent in order to overcome this hindrance and safely navigate to their evacuation area. Our experimentation shows that 51.1% fewer collisions occurred which is denote safer navigation for agents in approaching their evacuation point.

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Path planning and obstacle avoidance for AUV: A review

Cited by 154 publications

References 83 publications

Recent Advances in AI for Navigation and Control of Underwater Robots

Recent Advances in AI for Navigation and Control of Underwater Robots

An overview of path planning technologies for unmanned aerial vehicles

Crowd evacuation navigation for evasive maneuver of brownian based dynamic obstacles using reciprocal velocity obstacles

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