A Bilateral Teleoperation System for Planetary Rovers

Yu, Chang-Qing; Ju, Hehua; Gao, Yang; Cui, Pingyuan

doi:10.1109/cise.2009.5363354

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…In existing systems that demonstrated the teleoperation of mobile robots, for example (Yu et al, 2009) and (Wright et al, 2006), this robot model is assumed to be provided to the simulator with sufficient accuracy. In most cases, the parameters of this model are hard to quantify, because values from manufacturer's specifications or manual measurements are either non-existent, prone to errors, or can vary from one robot to the next.…”

Section: Kinematic Robot Simulation Modelmentioning

confidence: 99%

“…With exception of (Wright et al, 2006) and (Yu et al, 2009), many teleprogramming systems focus on the operation of robot arms, while systems tailored towards mobile robot tele-operation have gained less attention. This might be due to the fact that the physical simulation of mobile robots dynamics and environment interaction is non-trivial.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Control Paradigm for Decoupled Operation of Mobile Robots in Remote Environments

Pillat¹,

Nagendran²,

Hughes³

2012

Proceedings of the International Conference on Computer Graphics Theory and Applications

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Remote operation of robots in distant environments presents a significant challenge for direct control due to inherent issues with latency and available bandwidth. Purely autonomous behaviors in such environments come with the associated risks of failure. It may be advantageous to acquire information about the environment from the robot's onboard sensors, to recreate a simulation and plan a mission beforehand. The planned mission can then be uploaded to the robot for execution, allowing the physical robot to handle the autonomy required for completing its task. In this paper, the foundations of a control paradigm are presented that uses a simulated virtual environment to decouple the operator from the physical robot, thus circumventing latency problems. To achieve this, it is first required to accurately model the robot's kinematic parameters for use in the simulation. Kalman filtering techniques that demonstrate this modeling for a differential drive robot are presented in this paper. The estimated parameters are then used as inputs to a modular planning and execution module. Based on simulation results, a safe and non-redundant path can then be uploaded and executed on the real robot.

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Section: Kinematic Robot Simulation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%