In this article, we propose a reactive constrained navigation scheme, with embedded obstacles avoidance for an unmanned aerial vehicle (UAV), for enabling navigation in obstacle-dense environments. The proposed navigation architecture is based on a nonlinear model predictive controller (NMPC) and utilizes an onboard 2-D LiDAR to detect obstacles and translate online the key geometric information of the environment into parametric constraints for the NMPC that constrain the available position space for the UAV. This article focuses also on the real-world implementation and experimental validation of the proposed reactive navigation scheme, and it is applied in multiple challenging laboratory experiments, where we also conduct comparisons with relevant methods of reactive obstacle avoidance. The solver utilized in the proposed approach is the optimization engine (OpEn) and the proximal averaged Newton for optimal control (PANOC) algorithm, where a penalty method is applied to properly consider obstacles and input constraints during the navigation task. The proposed novel scheme allows for fast solutions while using limited onboard computational power, which is a required feature for the overall closed-loop performance of a UAV and is applied in multiple real-time scenarios. The combination of built-in obstacle avoidance and real-time applicability makes the proposed reactive constrained navigation scheme an elegant framework for UAVs that is able to perform fast nonlinear control, local path planning, and obstacle avoidance, all embedded in the control layer.

In this letter, a floating robotic emulation platform is presented with an autonomous maneuverability for a virtual demonstration of a satellite motion. Such a robotic platform design is characterized by its friction-less, levitating, yet planar motion over a hyper-smooth surface. The design of the robotic platform, integrated with the sensor and actuator units, is briefly described, including the related component specification along with the mathematical model, describing its dynamic motion. Additionally, the article establishes a nonlinear optimal control architecture consisting of a unified model predictive approach for the overall manoeuvre tracking. The efficacy of the proposed modeling and control scheme is demonstrated in multiple experimental studies, where it is depicted that the proposed controller has the potential to address a precise point-to-point manoeuvre with terminal objectives, as well as an excellent path following capability. The proposed design is validated with extensive experimental studies, and it is supported with related results.

In this article, we address the task of developing a unified solution that incorporates quadruped and aerial robots for planetary exploration missions. The designing process takes recommendations provided by Boston Dynamics for building custom payloads for the Spot robot, as well as its kinematic constraints. The unification task itself encompasses design of a passive drone landing platform as a hardware link between the Spot robot and the drone, which has active locking and unlocking capabilities required to securely keep the drone on the Spot independently whether it is standing or moving. Thus, in the designed unification solution, the landing platform does not impact the overall robot mobility and has no interference with the robot’s legs. The initial solution design was extensively evaluated in a series of tests at the laboratory, which demonstrated its viability.