Scaling in a continuous time model for biological aging

In this paper we consider a generalization to the asexual version of the Penna model for biological aging, where we take a continuous time limit. The genotype associated to each individual is an interval of real numbers over which Dirac $\delta$--functions are defined, representing genetically programmed diseases to be switched on at defined ages of the individual life. We discuss two different continuous limits for the evolution equation and two different mutation protocols, to be implemented during reproduction. Exact stationary solutions are obtained and scaling properties are discussed.Comment: 10 pages, 6 figure

Vision-Depth Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments

This paper proposes a method for tight fusion of visual, depth and inertial data in order to extend robotic capabilities for navigation in GPS-denied, poorly illuminated, and texture-less environments. Visual and depth information are fused at the feature detection and descriptor extraction levels to augment one sensing modality with the other. These multimodal features are then further integrated with inertial sensor cues using an extended Kalman filter to estimate the robot pose, sensor bias terms, and landmark positions simultaneously as part of the filter state. As demonstrated through a set of hand-held and Micro Aerial Vehicle experiments, the proposed algorithm is shown to perform reliably in challenging visually-degraded environments using RGB-D information from a lightweight and low-cost sensor and data from an IMU.Comment: 11 pages, 6 figures, Published in International Symposium on Visual Computing (ISVC) 201

Role and task allocation framework for Multi-Robot Collaboration with latent knowledge estimation

In this work a novel framework for modeling role and task allocation in Cooperative Heterogeneous Multi-Robot Systems (CHMRSs) is presented. This framework encodes a CHMRS as a set of multidimensional relational structures (MDRSs). This set of structure defines collaborative tasks through both temporal and spatial relations between processes of heterogeneous robots. These relations are enriched with tensors which allow for geometrical reasoning about collaborative tasks. A learning schema is also proposed in order to derive the components of each MDRS. According to this schema, the components are learnt from data reporting the situated history of the processes executed by the team of robots. Data are organized as a multirobot collaboration treebank (MRCT) in order to support learning. Moreover, a generative approach, based on a probabilistic model, is combined together with nonnegative tensor decomposition (NTD) for both building the tensors and estimating latent knowledge. Preliminary evaluation of the performance of this framework is performed in simulation with three heterogeneous robots, namely, two Unmanned Ground Vehicles (UGVs) and one Unmanned Aerial Vehicle (UAV)

Information-driven search and source reconstruction using cooperative UAVs

This paper proposes a search and source reconstructing strategy in a decentralized manner using multiple mobile sensors. Each mobile sensor estimates the source location and the release rate using a sequential Monte Carlo method, and decides its own optimal control action based on information theory. Furthermore, all agents negotiate multiple times by exchanging local measurements and decisions to improve their decisions towards a quasi-group decision. This decision making process is called negotiated coordination or cooperation in this paper. Numerical simulations and experiments are conducted to verify the performance of the proposed method. ?? 2019, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved