Vision-based localization of an underwater robot in a structured environment

Carreras, Marc; Ridao, Pere; García, Rafael; Nicosevici, Tudor

doi:10.1109/robot.2003.1241718

Cited by 64 publications

(31 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One can find algorithms suited for embedded systems with real-time performance, but their limitations restrict their utilization in real-world applications (e.g. the system in [30], which is based on detecting color segments, and the approach in [31], which uses a pattern of four tennis balls, suffers in varying lighting conditions).…”

Section: B Systems Of Relative Localization Of Autonomous Robotsmentioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

View full text Add to dashboard Cite

Abstract-A complex system for control of swarms of Micro Aerial Vehicles (MAV), in literature also called as Unmanned Aerial Vehicles (UAV) or Unmanned Aerial Systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visualbased stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

show abstract

Section: B Systems Of Relative Localization Of Autonomous Robotsmentioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Carreras proposed a vision-based localization technique [5], using a coded pattern placed on the bottom of a water tank and an onboard downward looking camera. This approach, although working well in the experimental setup, is difficult to be translated into real world missions, for the difficulty to reproduce a coded pattern on the seabed and for the problems in using cameras in the underwater environment, as described above.…”

Section: B Related Workmentioning

confidence: 99%

A particle filter approach for AUV localization

et al. 2008

View full text Add to dashboard Cite

Abstract-This paper presents a sonar-based localization approach for an autonomous underwater vehicle, in structured and unstructured environments. The system is based on a particle filter approach to represent the vehicle state and it uses a mechanically scanned profiling sonar, acquiring range profiles. A modification to the standard particle filter algorithm is proposed, in order to explore the state space in a more effective way and to reduce computational complexity. The proposed system was validated both in simulation and in trials involving a real vehicle, showing a high robustness and real-time capabilities.

show abstract

“…For the repulsion behavior, a preprogrammed state-action mapping was used, instead of using a SONQL algorithm, to avoid any physical damage to the vehicle during the learning period. For this test, the repulsion behavior used the data of a vision-based positioning system to determine the distance to the walls of the water tank [40]. A new behavior called teleoperation was defined to command the vehicle from an external human machine interface (HMI) module.…”

Section: B Urismentioning

confidence: 99%

A Behavior-Based Scheme Using Reinforcement Learning for Autonomous Underwater Vehicles

Carreras

Yuh

Batlle

et al. 2005

IEEE J. Oceanic Eng.

Self Cite

View full text Add to dashboard Cite

Abstract-This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs.Index Terms-Autonomous underwater vehicle (AUV), behavior-based control, neural networks, reinforcement learning.

show abstract

Vision-based localization of an underwater robot in a structured environment

Cited by 64 publications

References 4 publications

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

A particle filter approach for AUV localization

A Behavior-Based Scheme Using Reinforcement Learning for Autonomous Underwater Vehicles

Contact Info

Product

Resources

About