Human-aware robotic navigation remains an open challenge, with no universal solution yet available. Recent advances in the field have identified key metrics and scenarios that such navigation systems should address. In this paper, we present a novel social robotic navigation solution based on a heuristic approach that yields good performance in various environments (open areas or corridors) and different conditions (absence of human to crowded). Simulations first show the lack of generalization of existing methods and that our approach compete with baselines in crowded scenarios and outperforms them across daily scenarios (corridors), using a well-established set of metrics in the field. Preliminary real-world experiment further demonstrate the feasibility of deploying this approach in dynamic environments involving humans.

Acquiring vast and reliable data of physicochemical parameters is critical to environment monitoring. In the context of water quality analysis, data collection solutions have to overcome challenges related to the scale of environments to be explored. Sites to monitor can be large or remote. These challenges can be approached by the use of Unmanned Vehicles (UVs). Robots provide both flexibility on intervention plans and technological methods for real-time data acquisition. Being autonomous, UVs can explore areas difficult to access or far from the shore. This paper presents a nonlinear Model Predictive Control (MPC) for UV-based exploration. The strategy aims to improve the data collection of physicochemical parameters with the use of an Unmanned Surface Vehicle (USV) targeting water quality analysis. We have performed simulations based on real field experiments with a SPYBOAT® on the Heron Lake in Villeneuve d’Ascq, France. Numerical results suggest that the proposed strategy outperforms the schedule of mission planning and exploration for large areas.

Communicating Sequential Process (CSP) is nowadays a popular concurrency model in which threads/processes communicate by exchanging data through channels. Channels help in orchestrating concurrent processes but do not solve per-se data races. To prevent data races in the channel model, many programming languages rely on type systems to express ownership and behavioural restrictions such as immutability. However, dynamically-typed languages require run-time mechanisms because of the lack of type information at compile-time.

Testing and debugging a Virtual Machine is a laborious task without the proper tooling. This is particularly true for VMs with JIT compilation and dynamic code patching for techniques such as inline caching. In addition, this situation is getting worse when the VM builds and runs on multiple target architectures.

Performance benchmarking has become an important topic within robotics. It is indeed, a critical way to compare different solutions under different conditions. In this paper, we focus on performance benchmarking of multi-robot systems which explore and map unknown terrains. We present a collection of metrics to objectively compare different algorithms that can be applied to collaborative multi-robot exploration. We also identify parameters that impact robotic fleet performances. By varying the parameters, we can identify strengths and limits of an algorithm. This work is also a first concrete step to address the general problem of objectively comparing different multi-robot coordination algorithms. We illustrate these contributions with realistic simulations of the frontier-based exploration strategy. The simulations were implemented in ROS, which enables to uncouple the control software from the drivers of the robot body. We can therefore use the same code on both simulation and real robots.

Mobile autonomous robots are progressively entering the mass market. Thus, manufacturers have to perform quality assurance tests on series of robots. Therefore, tests should be repeatable and as much automated as possible. Tests are also performed for purpose of repairing robots. This calls for reusing tests already defined for quality assurance. In this paper we introduce a methodology to support the definition of repeatable, reusable, semi-automated tests. Our methodology describes the process of conducting tests in a way that maximizes safety for human operators, while avoiding to damage tested robots.

In the context of Component-based Programming, which addresses the implementation stage of a component-based software engineering development process, this paper describes a specification and an operational integration of an inheritance system into a self-contained new component-based programming language named Compo. Our proposal completes and extends related works by making it possible to apply inheritance to the full description of components, i.e. both to structural (description of provisions and requirements, of component architecture) and behavioral (full implementations of services) parts in component descriptions. Inheritance in Compo is designed to be used in conjunction with composition to maximize reuse capabilities and expressive power. Compo implementation proposes a clear operational solution for inheritance and for achieving and testing substitutions.