Stair Climbing Control for 4-DOF Tracked Vehicle Based on Internal Sensors

Endo, Daisuke; Watanabe, Atsushi; Nagatani, Keiji

doi:10.1155/2017/3624589

Cited by 6 publications

(3 citation statements)

References 14 publications

(24 reference statements)

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“…Another approach relying on precise estimation of the staircase configuration was proposed in [12] where the robot could autonomously negotiate a staircase using proprioceptive state estimation. That approach showed prominent performance, however, the system could only negotiate a staircase known beforehand.…”

Section: Control Methods In Obstacle Negotiationmentioning

confidence: 99%

Reinforcement Learning Based, Staircase Negotiation Learning: Simulation and Transfer to Reality for Articulated Tracked Robots

Mitriakov

Papadakis

Kerdreux

et al. 2021

IEEE Robot. Automat. Mag.

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Autonomous control of reconfigurable robots is crucial for their deployment in diverse environments. The development of such skills is however hampered by the diversity in hardware and task constraints. We advocate the use of artificial intelligence-based approaches to improve scalability to different conditions and portability to platforms of comparable traversability skills. In particular, we succeed in tackling the problem of staircase traversal via a reinforcement learning-based control framework applicable to different articulated tracked robots, powerful enough to generalize to varying conditions learnt in simulation and to transfer to reality in a zero-shot setting. Our extensive experiments demonstrate the robustness of the framework in learning tasks with increased risk and difficulty induced by platform diversification and increased control dimensionality.

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Section: Control Methods In Obstacle Negotiationmentioning

confidence: 99%

Reinforcement Learning Based, Staircase Negotiation Learning: Simulation and Transfer to Reality for Articulated Tracked Robots

Mitriakov

Papadakis

Kerdreux

et al. 2021

IEEE Robot. Automat. Mag.

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“…An approach using dense laser sensory data and passive flippers was proposed in [11] where the robot orientates flippers tangentially to the traversing obstacle while being tele-operated. A planning and control method for 4-DOF tracked robots in autonomous ascent and descent of known staircase was proposed in [12] where the robot estimated its state through internal sensors. The main limitation of that work is that the robot can only negotiate staircases known beforehand.…”

Section: Related Workmentioning

confidence: 99%

Staircase Negotiation Learning for Articulated Tracked Robots with Varying Degrees of Freedom

Mitriakov

Papadakis

Nguyen

et al. 2020

2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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Tracked robots capable of negotiating 3D terrains require delicate control, most often tailored to a specific platform or setting. For staircase traversal in particular, autonomous robot behaviours are difficult to obtain due to the increased risk of accident and stochasticity. Based on a previously developed reinforcement learning based framework that allows learning staircase ascent for an articulated tracked robot, in this work we extend our work to allow also staircase descent and further investigate the role of a manipulating arm in the stability and smoothness of the traversal. By relying on a precise simulation of geometry and kinematics of a real robot, we demonstrate prototype policies for staircase ascent and descent, optionally under the influence of an integrated active arm and different penalty criteria. The obtained results are qualitatively and quantitatively compared and show that the robot can learn plausible behaviors effectively, when guided by appropriate reward and penalty criteria.

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“…For the maneuvering and mobility, it is a popular strategy to mount flippers on rescue robots to increase its ability to pass trough various terrain. The most commonly designs used in research consists of front (and back) subtracks mounted on the main body with rotary joints [3][2] [4] [5][6] [7]. There are also people working with reconfigurable robots [8], transformable robots [9], four tracked robots [10] and others.…”

Section: Introductionmentioning

confidence: 99%

Configuration-Space Flipper Planning for Rescue Robots

Yuan

Wang

Schwertfeger

2019

2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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For rescue robots, flipper endows the robot with additional ability to pass through various terrain. Autonomous motion becomes more important. In recent work autonomy is done by either planning with several special states or based on collected data. We are considering if it is possible to find a way to build continues states without collecting old trail data. In this paper, we first model the possible states as a global planning path with parameter configuration of the scene. Then, we follows the path to achieve the autonomous run. We plot the morphology of each path points to show the correctness of the path and implement a simple path following on real robot to demonstrate the performance of our algorithm.

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Stair Climbing Control for 4-DOF Tracked Vehicle Based on Internal Sensors

Cited by 6 publications

References 14 publications

Reinforcement Learning Based, Staircase Negotiation Learning: Simulation and Transfer to Reality for Articulated Tracked Robots

Reinforcement Learning Based, Staircase Negotiation Learning: Simulation and Transfer to Reality for Articulated Tracked Robots

Staircase Negotiation Learning for Articulated Tracked Robots with Varying Degrees of Freedom

Configuration-Space Flipper Planning for Rescue Robots

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