Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behavior Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, reactiveness, scalability and domain-independence.In this paper, we propose a model-free AP framework using Genetic Programming (GP) to derive an optimal BT for an autonomous agent to achieve a given goal in unknown (but fully observable) environments. We illustrate the proposed framework using experiments conducted with an open source benchmark Mario AI for automated generation of BTs that can play the game character Mario to complete a certain level at various levels of difficulty to include enemies and obstacles.

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People with hand amputations experience strenuous daily life challenges, often leading to lifelong use of a prosthetic hand(s) and services. Modern advanced prosthetic hands must be able to provide human hand-like sensory perceptions to receive external stimuli during daily activities while simultaneously replicating a realistic appearance and physical properties to naturally integrate in social contexts; however, the practical realization of these issues are impeded by a lack of effective methodologies. Herein, we present an optimal set of materials, design layouts, and fabrication schemes to construct an easy-to-wear seamless electronic glove (e-glove) suitable for arbitrary hand shapes that provides all of the desired human hand-like features. The system configuration involves a connection to a control wristwatch unit for real-time display of sensory data measured and remote transmission to the user. The experimental and computational studies provide details regarding the underlying principles of the materials selection, mechanics design, and operational management of the entire system. The demonstration of the e-glove system in interactions with human subjects illustrates the utility, comfort, and convenience of this device.

Understanding the radio signal transmission characteristics in the environment where the telerobotic application is sought is a key part of achieving a reliable wireless communication link between a telerobot and a control station. In this paper, wireless communication requirements and a case study of a typical telerobotic application in an underground facility at CERN are presented. Then, the theoretical and experimental characteristics of radio propagation are investigated with respect to time, distance, location and surrounding objects. Based on analysis of the experimental findings, we show how a commercial wireless system, such as Wi-Fi, can be made suitable for a case study application at CERN.

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-Maximizing energy autonomy is a consistent challenge when deploying mobile robots in ionizing radiation or other hazardous environments. Having a reliable robot system is essential for successful execution of missions and to avoid manual recovery of the robots in environments that are harmful to human beings. For deployment of robots missions at short notice, the ability to know beforehand the energy required for performing the task is essential. This paper presents a on-line method for predicting energy requirements based on the pre-determined power models for a mobile robot. A small mobile robot, Khepera III is used for the experimental study and the results are promising with high prediction accuracy. The applications of the energy prediction models in energy optimization and simulations are also discussed along with examples of significant energy savings.

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Abstract-Mobile robots, be it autonomous or teleoperated, require stable communication with the base station to exchange valuable information. Given the stochastic elements in radio signal propagation, such as shadowing and fading, and the possibilities of unpredictable events or hardware failures, communication loss often presents a significant mission risk, both in terms of probability and impact, especially in Urban Search and Rescue (USAR) operations. Depending on the circumstances, disconnected robots are either abandoned, or attempt to autonomously back-trace their way to the base station. Although recent results in Communication-Aware Motion Planning can be used to effectively manage connectivity with robots, there are no results focusing on autonomously re-establishing the wireless connectivity of a mobile robot without back-tracing or using detailed a priori information of the network.In this paper, we present a robust and online radio signal mapping method using Gaussian Random Fields, and propose a Resilient Communication-Aware Motion Planner (RCAMP) that integrates the above signal mapping framework with a motion planner. RCAMP considers both the environment and the physical constraints of the robot, based on the available sensory information. We also propose a self-repair strategy using RCMAP, that takes both connectivity and the goal position into account when driving to a connection-safe position in the event of a communication loss. We demonstrate the proposed planner in a set of realistic simulations of an exploration task in single or multi-channel communication scenarios.