Although in recent years several studies aimed at the navigation of robots in cluttered environments, just a few have addressed the problem of robots navigating while moving a large or heavy object. This is especially useful when transporting loads with variable weights and shapes without having to change the robot hardware. On one hand, a major advantage of using a humanoid robot to move an object is that it has arms to firmly grasp it and control it. On the other hand, humanoid robots tend to have higher drift than their wheeled counterparts as well as having significant lateral swing while walking, which propagates to anything they carry. In this work, we present algorithms for a humanoid robot navigating in a cluttered environment while pushing a cart-like object. In addition, the algorithms make use of the hands and arms to articulate the cart when executing tight turns using whole body control scheme to reduce the lateral swing effect on the load and ensure a safe transport. Experiments conducted on a real Nao robot assessed the proposed approach and algorithms, they show that the payload of a humanoid robot can be significantly increased without changing the humanoid robot's hardware, and therefore enact the capacity of humanoid robots in reallife situations.

Abstract-In this work, we tackle the problem of making two humanoid robots navigate in a cluttered environment while transporting a very large object that simply can not be moved by a single robot. 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 those trajectories. We present experiments conducted on real Nao robots, equipped with RGB-D sensors mounted on their heads, moving an object around obstacles. Our experiments show that a significantly large object can be transported without changing the robot's main hardware, and therefore enacting the capacity of humanoid robots in real-life situations.

Between 1 June 1993 and 31 December 1998, 17 patients underwent temporary abdominal closure with 3L urological irrigation bags, because in most cases, there was massive sepsis leading to the conclusion that primary closure was not advisable. Indicative of the seriousness of these conditions, Apache score averaged 19 (range 10-30). The technique consisted of suturing a double thickness of irrigation bags to each side of the wound, and joining the two bags in the midline with running sutures. Abdominal lavage with large quantities of fluid was performed every other day. This type of closure was used for a mean duration of 15 days. Mean length of hospitalization was 60 days. There were only three deaths (17.6%). No incisional hernia occurred after the iterative laparotomies. Deleting patients with acute pancreatitis would have reduced the death rate to only 7%. A 3L urological irrigation bag costs pound 11.60 (24.40 dollars CAN) while a Marlex mesh costs pound 81.40 (171.00 dollars CAN). We conclude that the usage of 3L urological plastic bags is a simple, safe and efficient method for temporary closure of the abdomen.

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

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 object. Such a functionality is especially useful when transporting objects of different shapes and weights without having to modify the robot hardware. In this work, we tackle the problem of making two humanoid robots navigate in a cluttered environment while transporting a very large object that simply could not be moved by a single robot. 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 design of the control to execute those trajectories. We present experiments made on real NAO robots, equipped with RGB-D sensors mounted on their heads, moving an object around obstacles. Our experiments show that a significantly large object can be transported without modifying the robot main hardware, and therefore that our scheme enhances the humanoid robots capacities in real-life situations. Our contributions are: (1) a low-dimension multi-robot motion planning algorithm that finds an obstacle-free trajectory, by using the constructed map of the environment as an input, (2) a framework that produces continuous and consistent odometry data, by fusing the visual and the robot odometry information, (3) a synchronization system that uses the projection of the robots based on their hands positions coupled with the visual feedback error computed from a frontal camera, (4) an efficient real-time whole-body control scheme that controls the motions of the closed-loop robot–object–robot system.