This paper presents a backseat controller suitable for Autonomous Underwater Vehicles (AUVs) which promotes quick development turn-around of novel control methods, especially for missions which require onboard re-plan...
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ISBN:
(纸本)9781538648148
This paper presents a backseat controller suitable for Autonomous Underwater Vehicles (AUVs) which promotes quick development turn-around of novel control methods, especially for missions which require onboard re-planning. This backseat controller communicates low-level control instructions to a vehicle-specific frontseat controller. Small, power efficient components comprise the backseat controller hardware, especially a Raspberry Pi computer. The backseat controller runs Robot Operating System (ROS), a middleware that facilitates implementation of novel control algorithms and use of OpenCV, an extensive collection of computer vision libraries. ROS enables the underwater community to leverage development efforts in the ground and aerial domains to accelerate progress. The backseat controller has been mated to General Dynamics Bluefin SandShark AUV. The platform has been validated in a controlled environment and is now being deployed in Lake Superior, MI, USA.
The problem of robotic task definition and execution was pioneered by Mason, who defined setpoint constraints where the position, velocity, and/or forces are expressed in one particular task frame for a 6-DOF robot. L...
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The problem of robotic task definition and execution was pioneered by Mason, who defined setpoint constraints where the position, velocity, and/or forces are expressed in one particular task frame for a 6-DOF robot. Later extensions generalized this approach to constraints in 1) multiple frames;2) redundant robots;3) other sensor spaces such as cameras;and 4) trajectory tracking. Our work extends tasks definition to 1) expressions of constraints, with a focus on expressions between geometric entities ( distances and angles), in place of explicit set-point constraints;2) a systematic composition of constraints;3) runtime monitoring of all constraints ( that allows for runtime sequencing of constraint sets via, for example, a Finite State Machine);and 4) formal task descriptions, that can be used by symbolic reasoners to plan and analyse tasks. This means that tasks are seen as ordered groups of constraints to be achieved by the robot's motion controller, possibly with different set of geometric expressions to measure outputs, which are not controlled, but are relevant to assess the task evolution. Those monitored expressionsmay result in events that trigger switching to another ordered group of constraints to execute and monitor. For these task specifications, formal language definitions are introduced in the JSON-schema modeling language.
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