Automatic docking and recharging system for autonomous security robot

Luo, Ren C.; Liao, Chao; Su, Kuo-Lan; Lin, K.C.

doi:10.1109/iros.2005.1545197

Cited by 36 publications

(13 citation statements)

References 10 publications

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“…The formulation is complemented with a constraint on performance, here using the cycle time t f as a figure of merit; see (I) in (21). For each drive train, there are other four constraints.…”

Section: Design Examplementioning

confidence: 99%

“…For each drive train, there are other four constraints. The first three originate from the motors' continuous torque, peak torque, and speed requirements [see (II-IV) in (21)]. The maximum speed of the motors is 420 rad/s (IV).…”

Section: Design Examplementioning

confidence: 99%

“…In this example the lower limit is 20 million cycles. In VI in (21), the design variables are stated. There are six design variables for each axis and two axes involved in the optimization, which results in 12 optimization variables.…”

Section: Design Examplementioning

confidence: 99%

“…Every point on the graph is an optimal solution from solving the problem stated in (21). In order to generate the tradeoffs, this has been done several times for different performance requirements, i.e., the cycle time is slightly reduced, and a new optimization is done for each reduction.…”

Section: Design Examplementioning

confidence: 99%

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Drive Train Optimization for Industrial Robots

Pettersson

Ölvander

2009

IEEE Trans. Robot.

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1419Our future work includes integrating the proposed connection mechanism with an exact yet wide-ranging relative pose system for detection and localization of the robots. The overall objective is to develop an autonomous multirover robot, in which a team of autonomous mobile robots can assemble into a single serial-chain modular robot.Abstract-This paper presents an optimization strategy for finding the trade-offs between cost, lifetime, and performance when designing the drive train, i.e., gearboxes and electric motors, for new robot concepts. The method is illustrated with an example in which the drive trains of two principal axes on a six-axis serial manipulator are designed. Drive train design for industrial robots is a complex task that requires a concurrent design approach. For instance, the mass properties of one motor affect the torque requirements for another, and the method needs to consider several drive trains simultaneously. Since the trajectory has a large impact on the load on the actuators when running a robot, the method also includes the trajectory generation itself in the design loop. It is shown how the design problem can be formalized as an optimization problem. A non-gradientbased optimization algorithm that can handle mixed variable problems is used to solve the highly nonlinear problem. The outcome from an industrial point of view is minimization of cost and the simulataneous balancing of the trade-off between lifetime and performance.Index Terms-Design automation, drive train optimization, industrial robots.

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Section: Design Examplementioning

confidence: 99%

Section: Design Examplementioning

confidence: 99%

Section: Design Examplementioning

confidence: 99%

Section: Design Examplementioning

confidence: 99%

See 2 more Smart Citations

Drive Train Optimization for Industrial Robots

Pettersson

Ölvander

2009

IEEE Trans. Robot.

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“…This problem has been historically addressed in robotics [22], and several approaches were proposed based on a wide range of techniques, such as range lights [23] or vision and artificial landmarks [24]. It is really close to the problem of docking autonomous underwater vehicles (AUVs) for charging purposes [25].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Docking Based on Infrared System for Electric Vehicle Charging in Urban Areas

Pérez

Nashashibi

Lefaudeux

et al. 2013

Sensors

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Electric vehicles are progressively introduced in urban areas, because of their ability to reduce air pollution, fuel consumption and noise nuisance. Nowadays, some big cities are launching the first electric car-sharing projects to clear traffic jams and enhance urban mobility, as an alternative to the classic public transportation systems. However, there are still some problems to be solved related to energy storage, electric charging and autonomy. In this paper, we present an autonomous docking system for electric vehicles recharging based on an embarked infrared camera performing infrared beacons detection installed in the infrastructure. A visual servoing system coupled with an automatic controller allows the vehicle to dock accurately to the recharging booth in a street parking area. The results show good behavior of the implemented system, which is currently deployed as a real prototype system in the city of Paris.

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