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This paper addresses the problem of the coordinated deployment of a swarm of mobile robots in an area with geographical constraints. The objective of our research is to investigate how to build an ad hoc network of robotic sensors in a real-world environment. For attaining scalability and robustness, it is desirable that each robot can configure themselves into an area through local interactions with adjacent robots, using only locally available information. Therefore, a decentralized deployment scheme is presented based on geometric ideas allowing robots to converge to the uniform spatial distribution by forming regular triangle lattices. The convergence of the deployment scheme is mathematically examined, and verified through extensive simulations and experiments. In addition, a new evaluation index is proposed, to estimate a relative convergence degree with respect to a desired geometric configuration. Our results indicate that the deployment scheme can be applied to the problem regarding the coverage of an area of interest by a swarm of mobile robotic sensors.

This paper presents an approach for estimating the operational range for mobile robot exploration on a single battery discharge. Deploying robots in the wild usually requires uninterrupted energy sources to maintain the robot's mobility throughout the entire mission. However, for most endeavors into the unknown environments, recharging is usually not an option, due to the lack of pre-installed recharging stations or other mission constraints. In these cases, the ability to model the on-board energy consumption and estimate the operational range is crucial to prevent running out of battery in the wild. To this end, this work describes our recent findings that quantitatively break down the robot's on-board energy consumption and predict the operational range to guarantee safe mission completion on a single battery discharge cycle. Two range estimators with different levels of generality and model fidelity are presented, whose performances were validated on physical robot platforms in both indoor and outdoor environments. Model performance metrics are also presented as benchmarks., Comment: Accepted by RSS 2019 Workshop Robots in the Wild: Challenges in Deploying Robust Autonomy for Robotic Exploration

We consider the problem of synthesizing controllers automatically for distributed robots that are loosely coupled using a formal synthesis approach. Formal synthesis entails construction of game strategies for a discrete transition system such that the system under the strategy satisfies a specification, given for instance in linear temporal logic (LTL). The general problem of automated synthesis for distributed discrete transition systems suffers from state-space explosion because the combined state-space has size exponential in the number of subsystems. Motivated by multi-robot motion planning problems, we focus on distributed systems whose interaction is nearly decoupled, allowing the overall specification to be decomposed into specifications for individual subsystems and a specification about the joint system. We treat specifically reactive synthesis for the GR(1) fragment of LTL. Each robot is subject to a GR(1) formula, and a safety formula describes constraints on their interaction. We propose an approach wherein we synthesize strategies independently for each subsystem; then we patch the separate controllers around interaction regions such that the specification about the joint system is satisfied.

The Human or Cognitive Centered Design (HCD) of intelligent transport systems requires digital Models of Human Behavior and Cognition (MHBC) enabling Ambient Intelligence e.g. in a smart car. Currently MBHC are developed and used as driver models in traffic scenario simulations, in proving safety assertions and in supporting risk-based design. Furthermore, it is tempting to prototype assistance systems (AS) on the basis of a human driver model cloning an expert driver. To that end we propose the Bayesian estimation of MHBCs from human behavior traces generated in new kind of learning experiments: Bayesian model learning under driver control. The models learnt are called Bayesian Autonomous Driver (BAD) models. For the purpose of smart assistance in simulated or real world scenarios the obtained BAD models can be used as Bayesian Assistance Systems (BAS). The critical question is, whether the driving competence of the BAD model is the same as the driving competence of the human driver when generating the training data for the BAD model. We believe that our approach is superior to the proposal to model the strategic and tactical skills of an AS with a Markov Decision Process (MDP). The usage of the BAD model or BAS as a prototype for a smart Partial Autonomous Driving Assistant System (PADAS) is demonstrated within a racing game simulation.