Movement generalization of variable initial task state based on Euclidean transformation dynamical movement primitives

Robots need the ability to tackle problems of movement generalization in variable task state and complex environment. Dynamical movement primitives can effectively endow robots with humanoid characteristics. However, when the initial state of tasks changes, the generalized trajectories by dynamical movement primitives cannot retain shape features of demonstration, resulting in the loss of imitation quality. In this article, a modified dynamical movement primitives based on Euclidean transformation is proposed to solve this problem. It transforms the initial task state to a virtual situation similar to the demonstration and then utilizes the dynamical movement primitive method to realize movement generalization. Finally, it reverses the movement back to the real situation. Besides, the information of obstacles is added to Euclidean transformation based dynamical movement primitives framework to endow robots with the ability of obstacle avoidance. The normalized root-mean-square error is proposed as the criterion to evaluate the imitation similarity. The feasibility of this method is verified through writing letters, wiping whiteboard in two-dimensional task, and stirring mixture in three-dimensional task. The results show that the similarity of movement imitation in the proposed method is higher than dynamical movement primitives when the initial state changes. Meanwhile, Euclidean transformation based dynamical movement primitives can still greatly retain shape feature of demonstration while avoiding obstacles in an unstructured environment.

Modular Robotic Limbs for Astronaut Activities Assistance

In order to meet the assist requirements of extravehicular activity (EVA) for astronauts, such as moving outside the international space station (ISS) or performing on-orbit tasks by a single astronaut, this paper proposes an astronaut robotic limbs system (AstroLimbs) for extravehicular activities assistance. This system has two robotic limbs that can be fixed on the backpack of the astronaut. Each limb is composed of several basic module units with identical structure and function, which makes it modularized and reconfigurable. The robotic limbs can work as extra arms of the astronaut to assist them outside the space station cabin. In this paper, the robotic limbs are designed and developed. The reinforcement learning method is introduced to achieve autonomous motion planning capacity for the robot, which makes the robot intelligent enough to assist the astronaut in unstructured environment. In the meantime, the movement of the robot is also planned to make it move smoothly. The structure scene of the ISS for extravehicular activities is modeled in a simulation environment, which verified the effectiveness of the proposed method.

On-board cable attitude measurement and controller for outdoor aerial transportation

Deploying quadcopters for aerial transportation can be cost effective in impromptu material handling applications. However, such applications are limited mainly due to the requirement of onboard localization sensors and associated computation. The current work presents a human-controlled modality to successfully execute spontaneous outdoor flight of a quadcopter with a cable-suspended payload. Stable and smooth flights are achieved through an onboard integration of a custom-built sensor system and a controller to minimize payload oscillations. The feasibility of the proposed modality is demonstrated by conducting outdoor experiments and a case study in an unstructured environment.

Theoretical and Experimental Aspects Regarding the Forced Mounting of a Cylinder Containing the Electronics of a Mini Submarine

The underwater robot is part of a project with “terrestrial–maritime” collaborative robots, whose mission is recognition and rescue. From a structural point of view, some small changes were made in this study to the original robot. These changes consisted of making supports to hold the two plexiglass tubes, since the tube containing the battery system is larger. A larger tube was chosen because the aim was to increase the travel autonomy of the mini remotely operated vehicle (ROV). The mini submarine will move in an unstructured environment and will be able to reach a depth of 100 m. The purpose of the article is to present a point of view regarding the effect of the behavior of the mini ROV on tensions produced by the forced assembly of the sealing cover of the cylinder containing its command-and-control system. Both the gripping elements and the sealing lids are made using 3D printing technology, and the material used is polylactic acid (PLA). For the numerical analysis, the finite element method is used in both static and dynamic conditions. The results of this work refer to the field of tensions and displacements. The main conclusions emphasize the fact that the gripping performed for sealing is influenced by the usage of oiled mechanisms.

Investigating the failure to best respond in experimental games

We examine strategic sophistication using eight two-person 3 × 3 one-shot games. To facilitate strategic thinking, we design a ‘structured’ environment where subjects first assign subjective values to the payoff pairs and state their beliefs about their counterparts’ probable strategies, before selecting their own strategies in light of those deliberations. Our results show that a majority of strategy choices are inconsistent with the equilibrium prediction, and that only just over half of strategy choices constitute best responses to subjects’ stated beliefs. Allowing for other-regarding considerations increases best responding significantly, but the increase is rather small. We further compare patterns of strategies with those made in an ‘unstructured’ environment in which subjects are not specifically directed to think strategically. Our data suggest that structuring the pre-decision deliberation process does not affect strategic sophistication.

Robustness of Collective Scenting in the Presence of Physical Obstacles

Honey bees (Apis mellifera L.) aggregate around the queen by collectively organizing a communication network to propagate volatile pheromone signals. Our previous study shows that individual bees “scent” to emit pheromones and fan their wings to direct the signal flow, creating an efficient search and aggregation process. In this work, we introduce environmental stressors in the form of physical obstacles that partially block pheromone signals and prevent a wide open path to the queen. We employ machine learning methods to extract data from the experimental recordings, and show that in the presence of an obstacle that blocks most of the path to the queen, the bees need more time but can still effectively employ the collective scenting strategy to overcome the obstacle and aggregate around the queen. Further, we increase the complexity of the environment by presenting the bees with a maze to navigate to the queen. The bees require more time and exploration to form a more populated communication network. Overall, we show that given volatile pheromone signals and only local communication, the bees can collectively solve the swarming process in a complex unstructured environment with physical obstacles.