We address the dynamic modeling and control of a heavy-lift quadrotor and its suspended payload operating in the presence of wind disturbances. A compact 3D model is developed in matrix form using a Lagrangian formulation. The payload is treated as a rigid cuboid; the drag forces and the gyroscopic effects of the propellers are considered. The wind disturbances are modeled as a combined mean shear and turbulent velocity field with discrete gusts. Both first order (conventional) sliding mode control (SMC) and higher order sliding mode control (HOSMC) techniques are employed to solve the trajectory tracking problem. In the case of first order SMC the coefficients of the sliding surfaces are computed via linearization and Hurwitz analysis, and the closed-loop stability is proven via Lyapunov’s Second Method. The resulting controllers require tuning of a small number of parameters. To reduce chattering, a boundary layer approach is used: the signum function is approximated with a continuous saturation function, leading to the ultimate boundedness of the errors. To improve the tracking capabilities, HOSMC based on the Modified Super-Twisting Algorithm is also proposed. In this case, the controllers ensure asymptotic tracking of the reference trajectory, while maintaining low computational demands. Finally, simulations are carried out to compare the performance of first order SMC and HOSMC with proportional integral derivative (PID) control, as the latter type of control is most often embedded in commercial aerial vehicles. Considering a simulated 72 kg cargo drone with a 25 kg payload suspended by an 18 m cable in presence of non-vanishing disturbances, the designed sliding mode controllers reduce the integral of time multiplied by absolute error by an average factor greater than 100 when compared with PID controllers.

Uncrewed ground vehicle (UGV) applications, such as warehouse operations, assembly-line production, infrastructure inspection, surveillance, precision farming, and search & rescue, can benefit from shared control, in which a human can semi-automatically control the UGV when needed and let it operate fully-automatically, when desired. Many algorithms have been developed to permit a UGV to semi-autonomously conduct tasks, either individually, or in a group. However, a complete semi-autonomous system that works wherever, and whenever, needed is far from being implemented. Here, we develop a human-robot shared controller for the supervisory control of one or more UGVs by a single person. The shared controller blends an automatic control input with a human control input. The automatic control input consists of a trajectory tracking controller and a control barrier function based input term for collision avoidance. A joystick is used to provide the human control input. Human intent is measured employing a Lyapunov-like storage function, which is used in a convex function based blending law that continuously varies the magnitude of the control inputs coming from the human and the machine. The approach permits us to theoretically prove the asymptotic stability of the closed-loop system. The shared controller is validated using both a physical robot in a cluttered indoor environment, and a hardware-in-the-loop simulated robot operating in virtual warehouse environment.

Precision agriculture offers the opportunity to automate routine or difficult tasks in orchards and vineyards, such as spraying or inspection, with Uncrewed Ground Vehicles (UGV). In this context, human operators should be kept in the closed-loop control of the robot for safety and reliability. This work is motivated by the challenges of effectively deploying human-robot shared control in the field. First, an asymptotically stable controller keeps the robot on the desired trajectory between rows of trees, whose distance is on the order of the robot’s width. Second, the robot must efficiently avoid static and moving obstacles on its path. Third, the control inputs must not exceed the actuator limits, which can degrade trajectory tracking performance, cause instability, or damage critical hardware. Finally, in real-life scenarios, user intervention is sometimes required to manage unpredictable situations. To overcome these challenges, we propose and deploy a shared controller that continuously and smoothly varies the ratio of human and automatic control inputs depending on the human’s intent, geometrically rescales trajectory inputs to maintain bounded control, and incorporates obstacle avoidance capabilities – all while preserving asymptotic stability of the closed-loop system. Additionally, we introduce a time re-scaling strategy that modifies trajectory evolution, ensuring target positions remain within a defined vicinity of the robot. The system performance was assessed in simulation and in 26 field trials inside an apple orchard using different obstacle configurations, weather, and terrain conditions, with a success rate of 100% and an average tracking error of 0.1 m.Note to Practitioners—The proposed shared control approach is developed for use with a differentially steered Uncrewed Ground Vehicle (UGV) with first order kinematic constraints and can be adapted to different indoor and outdoor scenarios. The environment in which the UGV is deployed should be mapped in advance to create a reference trajectory for the UGV to follow. If the location of obstacles is not known in advance, an obstacle detection and tracking system, as well as an online mapping system, must be developed. A simulated model of the UGV and the environment are useful for determining initial values of the shared control gains that can be further tuned when the physical platform is first deployed. In GPS-denied scenarios, a Simultaneous Localization and Mapping (SLAM) system must be implemented; a lidar-inertial based SLAM system is recommended. A force-reflexive joystick is ideal for sensitive human input. In addition, the communication between the UGV and the base station (where the human operator supervises the UGV and provides commands to it) should have minimal time delays, not exceeding typical human reaction time (ca. 0.25 s).

Quadrotor Uncrewed Aerial Vehicles (UAVs) find diverse applications in inspection, surveillance, and mapping. They are characterized by high maneuverability, low size and vertical take-off. In recent decades, model-based non-linear controllers have been designed to deal with the problem of trajectory tracking of these mobile robots. Among them, Sliding Mode Control (SMC) allows one to counterbalance unknown dynamics and external disturbances. However, classical SMC is affected by high-frequency oscillations, known as the chattering effect, which are induced by the discontinuous signum term included in the controller. Inspired by previous work, this paper proposes four sliding mode controllers for trajectory tracking of UAVs, with simple linearization of the model and use of Hurwitz stability to find the controllers gains. The benefit of these controllers is that they provide chattering-free SMC by approximating the signum function with a saturation function. The stability of the proposed closed-loop controllers is proven via Lyapunov analysis and their effectiveness is validated through simulations.

This paper addresses the problem of safety-critical control for a Coaxial Octorotor UAV (COUAV). A Multi-Input-Multi-Output (MIMO) super-twisting Sliding Mode Controller (SMC) is designed as the nominal control strategy for trajectory tracking of COUAV, taking into consideration coupling effects. Additionally, a High Order Control Barrier Function (HOCBF) is proposed to ensure the safety of the trajectory. To validate the proposed method, a heavy-lift COUAV is simulated considering static and moving obstacles.

We introduce a model-predictive trajectory planning algorithm for unmanned surface vehicles (USVs) operating in congested civilian traffic. The planner reasons about the availability of contingency maneuvers needed in case of any of the civilian vessels breaches the International Regulations for the Prevention of Collisions at Sea (COLREGs). Our exploratory study indicated that implementing the envisioned planner requires significant speed up of trajectory planning to cope with the dynamics of the scene, and evaluation of collision risk. We describe a new method for efficiently searching 5D state space for a dynamically feasible trajectory using adaptive control action primitives. The algorithm estimates the congestion of the state space regions to evaluate collision risk, and then dynamically scales action primitives used during the search while preserving their dynamical feasibility. Our simulation experiments demonstrate that this leads to a substantial increase in the search efficiency and a decrease in the number of collisions, especially in complex scenarios with a higher number of civilian vessels.

We present a model-predictive trajectory planning algorithm for following a target boat by an autonomous unmanned surface vehicle (USV) in an environment with static obstacle regions and civilian boats. The planner developed in this work is capable of making a balanced trade-off among the following, possibly conflicting criteria: the risk of losing the target boat, trajectory length, risk of collision with obstacles, violation of the Coast Guard Collision Regulations (COLREGs), also known as “rules of the road”, and execution of avoidance maneuvers against vessels that do not follow the rules. The planner addresses these criteria by combining a search for a dynamically feasible trajectory to a suitable pose behind the target boat in 4D state space, forming a time-extended lattice, and reactive planning that tracks this trajectory using control actions that respect the USV dynamics and are compliant with COLREGs. The reactive part of the planner represents a generalization of the velocity obstacles paradigm by computing obstacles in the control space using a system-identified, dynamic model of the USV as well as worst-case and probabilistic predictive motion models of other vessels. We present simulation and experimental results using an autonomous unmanned surface vehicle platform and a human-driven vessel to demonstrate that the planner is capable of fulfilling the above mentioned criteria.