Control strategy and implementation for a humanoid robot pushing a heavy load on a rolling cart

Hawley, Louis; Suleiman, Wael

doi:10.1109/iros.2017.8206382

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…Moreover, the friction on the wheels and the transported mass have not been taken into account in the current study. An ongoing work focuses on the integration of two techniques [10,11] that we have recently developed into the framework in order to consider the friction and transported load, we expect that the new pattern generator will allow us to increase the transported load while ensuring the robot equilibrium.…”

Section: Discussionmentioning

confidence: 99%

Autonomous SLAM based humanoid navigation in a cluttered environment while transporting a heavy load

Rioux

Suleiman

2018

Robotics and Autonomous Systems

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Although in recent years there have been quite a few studies aimed at the navigation of robots in cluttered environments, few of these have addressed the problem of robots navigating while moving a large or heavy objects. This is especially useful when transporting loads with variable weights and shapes without having to change the robot hardware. Inspired by the wide use of makeshift carts by humans, we tackle, in this work, the problem of a humanoid robot navigating in a cluttered environment while displacing a heavy load that lies on a cart-like object. We present a complete navigation scheme, from the incremental construction of a map of the environment and the computation of collision-free trajectories to the control to execute these trajectories. Our contributions are as follows: (1) a whole-body control scheme that makes the humanoid use its hands and arms to control the motions of the cart-load system (e.g. tight turns) (2) a sensorless approach to automatically select the appropriate primitive set according to the load weight (3) a motion planning algorithm to find an obstacle-free trajectory using the appropriate primitive set and the constructed map of the environment as input (4) an efficient filtering technique to remove the cart from the field of view of the robot while improving the general performances of the SLAM algorithms and (5) a continuous and consistent odometry data formed by fusing the visual and the robot odometry information. We present experiments conducted on a real Nao robot, equipped with an RGB-D sensor mounted on its head, pushing a cart with different loads. Our experiments show that the payload can be significantly increased without changing the robot's main hardware, and therefore enacting the capacity of humanoid robots in real-life situations. 1

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Section: Discussionmentioning

confidence: 99%

Autonomous SLAM based humanoid navigation in a cluttered environment while transporting a heavy load

Rioux

Suleiman

2018

Robotics and Autonomous Systems

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“…Once the mass is found, the optimal feedback gains (or the matrices used in the QP formulation) are computed once and can then be used for the whole task. It is also possible to precompute a set of gains for different masses as shown in [15].…”

Section: Estimating the Massmentioning

confidence: 99%

“…1. Although simpler solutions such as using a wheeled-cart [15,29] can achieve great results, cooperative transportation has the advantage of not requiring any external tool, thus it is a promising option in precarious and unknown environments such as the scene of a natural disaster. However, cooperation between robots requires particular considerations at both motion planning and control levels.…”

Section: Introductionmentioning

confidence: 99%

Control framework for cooperative object transportation by two humanoid robots

Hawley

Suleiman

2019

Robotics and Autonomous Systems

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This paper aims at proposing a comprehensive control framework designed for cooperative transportation of a heavy load by two humanoid robots. First, a simplified dynamic model of the cooperative task is developed and the system stability is rigorously analyzed. Next, a centralized controller is formulated, this formulation provides an optimal control of the system by considering the robots dynamical stability while satisfying the robot-object-robot constraints. Finally, the controller is integrated with an arm controller and a local planner module forming a complete framework for cooperative transportation tasks. The approach is thoroughly analyzed and validated in simulation, and experiments are carried out on a team of two Nao humanoid robots transporting a range of objects placed on a small table. The experimental results pointed out the robustness of the approach as the robots successfully accomplished the transportation tasks in a stable way, moreover the transported objects masses were up to half the mass of one of the robot. Besides increasing the robot payload, some of the transported objects are relatively large and it is simply impossible for a single robot to transport them.

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“…Image processing, including image segmentation, noise removal, image modification, image restoration, and image representation, was applied to virtual objects to make the location more accurate, and the coordinates of plane coordinates and world transformation are constructed by connecting four visual windows. 18 Figure 1 shows the system framework diagram for the present study. Based on the HRP-2 platform of a humanoid robot, Osaka University of Japan considered the centroid com, thrust, and acceleration to achieve force control.…”

Section: Introductionmentioning

confidence: 99%

“…17 The influence of the vehicle and load on robot stability was analyzed and an effective solution to control the whole system was proposed. 18 Figure 1 shows the system framework diagram for the present study. The robot system control module is divided into head control, arm control, and attitude control.…”

Section: Introductionmentioning

confidence: 99%

Target recognition of indoor trolley for humanoid robot based on piecewise fitting method

Zhang

Liu

Luo

et al. 2019

Adaptive Control & Signal

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Although several studies have considered the problem of humanoid robots pushing carts, only a few have focused on the problem of robots moving heavy objects under monocular vision. This study proposes a target recognition and positioning method and a control method for a robot pushing a loaded trolley. A control system based on the humanoid robot NAO is developed and a monocular visual ranging method with segmented fitting is proposed to realize hardware control and target search and positioning for NAO. The ability of NAO to push a small cart with various weights using visual positioning is tested. The experimental results show that the average error of the monocular distance measurement method is 1.7 mm and that the target search and positioning is accurate. NAO can push a loaded cart that is 6.5 times its own weight.

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Control strategy and implementation for a humanoid robot pushing a heavy load on a rolling cart

Cited by 12 publications

References 10 publications

Autonomous SLAM based humanoid navigation in a cluttered environment while transporting a heavy load

Autonomous SLAM based humanoid navigation in a cluttered environment while transporting a heavy load

Control framework for cooperative object transportation by two humanoid robots

Target recognition of indoor trolley for humanoid robot based on piecewise fitting method

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