A Multi-Sensor Stochastic Energy-Based Vibro-Localization Technique with Byzantine Sensor Elimination.

This paper presents an occupant localization technique that determines the location of individuals in indoor environments by analyzing the structural vibrations of the floor caused by their footsteps. Structural vibration waves are difficult to measure as they are influenced by various factors, including the complex nature of wave propagation in heterogeneous and dispersive media (such as the floor) as well as the inherent noise characteristics of sensors observing the vibration wavefronts. The proposed vibration-based occupant localization technique minimizes the errors that occur during the signal acquisition time. In this process, the likelihood function of each sensor-representing where the occupant likely resides in the environment-is fused to obtain a consensual localization result in a collective manner. In this work, it becomes evident that the above sources of uncertainties can render certain sensors deceptive, commonly referred to as "Byzantines." Because the ratio of Byzantines among the set sensors defines the success of the collective localization results, this paper introduces a Byzantine sensor elimination (BSE) algorithm to prevent the unreliable information of Byzantine sensors from affecting the location estimations. This algorithm identifies and eliminates sensors that generate erroneous estimates, preventing the influence of these sensors on the overall consensus. To validate and benchmark the proposed technique, a set of previously conducted controlled experiments was employed. The empirical results demonstrate the proposed technique's significant improvement (3~0%) over the baseline approach in terms of both accuracy and precision.

Design and printing of proprioceptive three-dimensional architected robotic metamaterials.

Advances in additive manufacturing techniques have enabled the creation of stimuli-responsive materials with designed three-dimensional (3D) architectures. Unlike biological systems in which functions such as sensing, actuation, and control are closely integrated, few architected materials have comparable system complexity. We report a design and manufacturing route to create a class of robotic metamaterials capable of motion with multiple degrees of freedom, amplification of strain in a prescribed direction in response to an electric field (and vice versa), and thus, programmed motions with self-sensing and feedback control. These robotic metamaterials consist of networks of piezoelectric, conductive, and structural elements interwoven into a designed 3D lattice. The resulting architected materials function as proprioceptive microrobots that actively sense and move.