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Maintaining orientation in an environment with non-Earth gravity (1 g) is critical for an astronaut's operational performance. Such environments present a number of complexities for balance and motion. For example, when an astronaut tilts due to ascending or descending an inclined plane on the moon, the gravity vector will be tilted correctly, but the magnitude will be different from on earth. If this results in a mis-perceived tilt, then that may lead to postural and perceptual errors, such as mis-perceiving the orientation of oneself or the ground plane and corresponding errors in task judgment.
This work discusses how to use OSM for robotic applications and aims at starting a discussion between the OSM and the robotics community. OSM contains much topological and semantic information that can be directly used in robotics and offers various advantages: 1) Standardized format with existing tooling. 2) The graph structure allows to compose the OSM models with domain-specific semantics by adding custom nodes, relations, and key-value pairs. 3) Information about many places is already available and can be used by robots since it is driven by a community effort.
This paper introduces a random number generator (RNG) based on the avalanche noise of two diodes. A true random number generator (TRNG) generates true random numbers with the use of the electronic noise produced by two avalanche diodes. The amplified outputs of the diodes are sampled and digitized. The difference between the two concurrently sampled and digitized outputs is calculated and used to select a seed and to drive a pseudo-random number generator (PRNG). The PRNG is an xorshift generator that generates 1024 bits in each cycle. Every sequence of 1024 bits is moderately modified and output. The TRNG delivers the next seed and the next cycle begins. The statistical behavior of the generator is analyzed and presented.
In presence of conflicting or ambiguous visual cues in complex scenes, performing 3D selection and manipulation tasks can be challenging. To improve motor planning and coordination, we explore audio-tactile cues to inform the user about the presence of objects in hand proximity, e.g., to avoid unwanted object penetrations. We do so through a novel glove-based tactile interface, enhanced by audio cues. Through two user studies, we illustrate that proximity guidance cues improve spatial awareness, hand motions, and collision avoidance behaviors, and show how proximity cues in combination with collision and friction cues can significantly improve performance.
We present a novel forearm-and-glove tactile interface that can enhance 3D interaction by guiding hand motor planning and coordination. In particular, we aim to improve hand motion and pose actions related to selection and manipulation tasks. Through our user studies, we illustrate how tactile patterns can guide the user, by triggering hand pose and motion changes, for example to grasp (select) and manipulate (move) an object. We discuss the potential and limitations of the interface, and outline future work.
Entering the work envelope of an industrial robot can lead to severe injury from collisions with moving parts of the system. Conventional safety mechanisms therefore mostly restrict access to the robot using physical barriers such as walls and fences or non-contact protective devices including light curtains and laser scanners. As none of these mechanisms applies to human-robot-collaboration (HRC), a concept in which human and machine complement one another by working hand in hand, there is a rising need for safe and reliable detection of human body parts amidst background clutter. For this application camera-based systems are typically well suited. Still, safety concerns remain, owing to possible detection failures caused by environmental occlusion, extraneous light or other adverse imaging conditions. While ultrasonic proximity sensing can provide physical diversity to the system, it does not yet allow to reliably distinguish relevant objects from background objects.This work investigates a new approach to detecting relevant objects and human body parts based on acoustic holography. The approach is experimentally validated using a low-cost application-specific ultrasonic sensor system created from micro-electromechanical systems (MEMS). The presented results show that this system far outperforms conventional proximity sensors in terms of lateral imaging resolution and thus allows for more intelligent muting processes without compromising the safety of people working close to the robot. Based upon this work, a next step could be the development of a multimodal sensor systems to safeguard workers who collaborate with robots using the described ultrasonic sensor system.