Control of two-wheeled humanoid robots poses several challenges due to the unstable dynamics of their mobile base and the coupling between the upper and lower body dynamics. The problem is often faced in the literature with methods based on the linearized or simplified models that fail in exploiting the whole body dynamic capabilities of the platform in use. In this letter, we tackle the problem of whole-body dynamic control for a mobile wheeled unstable humanoid robot. Compared to the existing approaches based on online optimization to guarantee respect of the constraints, we propose a control method that takes into account the nominal constrained dynamics of the robot in the quasi-velocities through an internal model, thus reducing the computational burden. A computed-torque control law in the quasi-velocities is used to stabilize the robot around the upper position. We report on preliminary experimental results and on the method effectiveness in rejecting unknown external disturbances.

Self driving vehicles promise to bring one of the greatest technological and social revolutions of the next decade for their potential to drastically change human mobility and goods transportation, in particular regarding efficiency and safety. Autonomous racing provides very similar technological issues while allowing for more extreme conditions in a safe human environment. While the software stack driving the racing car consists of several modules, in this paper we focus on the localization problem, which provides as output the estimated pose of the vehicle needed by the planning and control modules. When driving near the friction limits, localization accuracy is critical as small errors can induce large errors in control due to the nonlinearities of the vehicle’s dynamic model. In this paper, we present a localization architecture for a racing car that does not rely on Global Navigation Satellite Systems (GNSS). It consists of two multi-rate Extended Kalman Filters and an extension of a state-of-the-art laser-based Monte Carlo localization approach that exploits some a priori knowledge of the environment and context. We first compare the proposed method with a solution based on a widely employed state-of-the-art implementation, outlining its strengths and limitations within our experimental scenario. The architecture is then tested both in simulation and experimentally on a full-scale autonomous electric racing car during an event of Roborace Season Alpha. The results show its robustness in avoiding the robot kidnapping problem typical of particle filters localization methods, while providing a smooth and high rate pose estimate. The pose error distribution depends on the car velocity, and spans on average from 0.1 m (at 60 km/h) to 1.48 m (at 200 km/h) laterally and from 1.9 m (at 100 km/h) to 4.92 m (at 200 km/h) longitudinally.

Today, human intervention is the only effective course of action after a natural or artificial disaster. This is true both for relief operations, where search and rescue of survivors is the priority, and for subsequent activities, such as those devoted to building assessment. In these contexts, the use of robotic systems would be beneficial to drastically reduce operators? risk exposure. However, the readiness level of robots still prevents their effective exploitation in relief operations, which are highly critical and characterized by severe time constraints. On the contrary, current robotic technologies can be profitably applied in procedures like building assessment after an earthquake. To date, these operations are carried out by engineers and architects who inspect numerous buildings over a large territory, with a high cost in terms of time and resources, and with a high risk due to aftershocks. The main idea is to have the robot acting as an alter ego of the human operator, who, thanks to a virtual-reality device and a body-tracking system based on inertial sensors, teleoperates the robot.