This paper presents an adaptive output feedback-based visual servoing law for a quadrotor unmanned aerial vehicle equipped with a single downward facing camera. The objective is to regulate the ...relative position and yaw of the vehicle to a planar target consisting of multiple points using a minimal sensor set, i.e., an inertial measurement unit and a vision sensor. A set of first order image moment features, defined in the image plane of a virtual camera with zero roll and pitch motion, is used for visual servoing. It has been observed in previous work that various system uncertainties, such as aerodynamics constants and attitude estimation bias, result in steady-state errors if not being compensated. By treating those uncertainties as unknown system parameters, an adaptive backstepping controller is developed. As the given visual servoing law is an output feedback controller, the translational velocity measurement from the global position system is not required. The asymptotic stability of the error dynamics is proven. Experimental results are provided to demonstrate the controller performance.
Transition Optimization for a VTOL Tail-Sitter UAV Li, Boyang; Sun, Jingxuan; Zhou, Weifeng ...
IEEE/ASME transactions on mechatronics,
2020-Oct., 2020-10-00, 20201001, Volume:
25, Issue:
5
Journal Article
Peer reviewed
Open access
This article focuses on the transition process optimization for a vertical takeoff and landing (VTOL) tail-sitter unmanned aerial vehicle (UAV). For VTOL UAVs that can fly with either hover or cruise ...mode, transition refers to the intermediate phases between these two modes. This work develops a transition strategy with the trajectory optimization method. The strategy is a reference maneuver enabling the vehicle to perform transition efficiently by minimizing the cost of energy and maintaining a small change of altitude. The simplified three-degree-of-freedom longitudinal aerodynamic model is used as a dynamic constraint. The transition optimization problem is then modeled by nonlinear programming and solved by the collocation method to obtain the reference trajectory of the pitch angle and throttle offline. Simulations with the Gazebo simulator and outdoor flight experiments are carried out with the optimized forward (hover cruise) and backward (cruise hover) transition solutions. The simulation and experimental results show that the optimized transition strategy enables the vehicle to finish transition with less time and change of altitude compared with that by using traditional linear transition methods.
Increasing use of Unmanned Aerial Vehicle (UAV) in urban environments poses to an increased risk of fallen UAVs impacting people and vehicles on the ground, as well as colliding with manned aircraft ...in the vicinity of airports. Risk management of UAV flights for safe operations is essential. We proposed a comprehensive risk assessment model for UAV operation in urban environments. Three risk categories (people, vehicles, and manned aircraft) were considered and each risk cost was quantified using collision probability. We adjusted the risk costs in various magnitudes to a same scale and conducted a sensitivity analysis to determine the optimal coefficients of the three risk cost models. We then computed the total risk and generated a risk cost map for path planning. Modified path planning algorithms were used to produce a cost-effective path, and we compared their performances in terms of total risk cost and computational time. Lastly, we performed simulations to validate the feasibility and effectiveness of our proposed risk assessment model. The results show that the risk-cost-based path planning method can generate safer path for UAV operations than the traditional shortest-distance-based method. Our proposed model can be extended to complex urban environments by including more relevant parameters and data.
One of the causes of positioning inaccuracies in the Unmanned Aircraft System (UAS) is navigation error. In urban environment operations, multipaths could be the dominant contributor to navigation ...errors. This paper presents a study on how the operation environment affects the lateral (horizontal) navigation performance when a self-built UAS is going near different types of urban obstructions in real flight tests. Selected test sites are representative of urban environments, including open carparks, flight paths obstructed by buildings along one or both sides, changing sky access when flying towards corners formed by two buildings or dead ends, and buildings with reflective glass-clad surfaces. The data was analysed to obtain the horizontal position error between Global Positioning System (GPS) position and ground truth derived from Real Time Kinematics (RTK), with considerations for (1) horizontal position uncertainty estimate (EPH) reported by the GPS receiver, (2) no. of visible satellites, and (3) percentage of sky visible (or sky openness ratio, SOR) at various altitudes along the flight paths inside the aforementioned urban environments. The investigation showed that there is no direct correlation between the measured horizontal position error and the reported EPH; thus, the EPH could not be used for the purpose of monitoring navigation performance. The investigation further concluded that there is no universal correlation between the sky openness ratio (SOR) seen by the UAS and the resulting horizontal position error, and a more complex model would need to be considered to translate 3D urban models to expected horizontal navigation uncertainty for the UAS Traffic Management (UTM) airspace.
An improved design of a biomimetic underwater vehicle (RoMan-II) inspired by manta ray is presented in this paper. The design of the prototype and the swimming motion control are discussed. Instead ...of using rigid multiple degree-of-freedom linkages as fin rays in the first version, six flexible fin rays are adopted to drive two sided fins which generate thrust through flapping motions. Furthermore, in order to save the energy for a long distance cruising, a bio-inspired gliding motion is incorporated onto the motion control of the improved prototype. With a closed-loop buoyancy control system, the vehicle can perform gliding locomotion in water, which reduces the overall energy consumption. The vehicle can also perform pivot turning and backward locomotion without turning its body. It can achieve an average velocity of one body length per second. The vehicle is able to carry various sensors or communication equipments, as the payload capacity is about 4 kg. Initial testing shows that the operation time of the buoyancy body is estimated to about 6 hours for free swimming and 90 hours for a pure gliding. The flapping frequency, flapping amplitude, and the number of waves performed across the fin's chord and wave directions can be independently tuned through the proposed control scheme. In general, the present prototype provides a useful platform to study the ray-like swimming motion in a single or combination mode of flapping, undulation and gliding.
This paper presents the design and development of a starfish-like soft robot with flexible rays and the implementation of multi-gait locomotion using Shape Memory Alloy (SMA) actuators. The design ...principle was inspired by the starfish, which possesses a remarkable symmetrical structure and soft internal skeleton. A soft robot body was constructed by using 3D printing technology. A kinematic model of the SMA spring was built and developed for motion control according to displacement and force requirements. The locomotion inspired from starfish was applied to the implementation of the multi-ray robot through the flexible actuation induced multi-gait movements in various environments. By virtue of the proposed ray control patterns in gait transition, the soft robot was able to cross over an obstacle approximately twice of its body height. Results also showed that the speed of the soft robot was 6.5 times faster on sand than on a clammy rough terrain. These experiments demonstrated that the bionic soft robot with flexible rays actuated by SMAs and multi-gait locomotion in proposed patterns can perform successfully and smoothly in various terrains.
Air route crossing waypoint optimization is one of the effective ways to improve airspace utilization, capacity and resilience in dealing with air traffic congestion and delay. However, research is ...lacking on the optimization of multiple Crossing Waypoints (CWPs) in the fragmented airspace separated by Prohibited, Restricted and Dangerous areas (PRDs). To tackle this issue, this paper proposes an Artificial Potential Field (APF) model considering attractive forces produced by the optimal routes and repulsive forces generated by obstacles. An optimization framework based on the APF model is proposed to optimize the different airspace topologies varying the number of CWPs, air route segments and PRDs. Based on the framework, an adaptive method is developed to dynamically control the optimization process in minimizing the total air route cost. The proposed model is applied to a busy controlled airspace. And the obtained results show that after optimization the safety-related indicators: conflict number and controller workload reduced by 7.75% and 6.51% respectively. As for the cost-effectiveness indicators: total route length, total air route cost and non-linear coefficient, declined by 1.74%, 3.13% and 1.70% respectively. While the predictability indicator, total flight delay, saw a notable reduction by 7.96%. The proposed framework and methodology can also provide an insight in the understanding of the optimization to other network systems.
An experiment-based approach is proposed to improve the performance of biomimetic undulatory locomotion through on-line optimization. The approach is implemented through two steps: (1) the generation ...of coordinated swimming gaits by artificial Central Pattern Generators (CPGs); (2) an on-line searching of optimal parameter sets for the CPG model using Genetic Algorithm (GA). The effectiveness of the approach is demonstrated in the optimization of swimming speed and energy efficiency for a biomimetic fin propulsor. To evaluate how well the input energy is converted into the kinetic energy of the propulsor, an energy-efficiency index is presented and utilized as a feedback to regulate the on-line searching with a closed-loop swimming control. Experiments were conducted on propulsor prototypes with different fin segments and the optimal swimming patterns were found separately. Comparisons of results show that the optimal curvature of undulatory propulsor, which might have different shapes depending on the actual prototype design and control scheme. It is also found that the propulsor with six fin segments, is preferable because of higher speed and lower energy efficiency.
Unmanned aerial vehicles (UAVs) have gained much attention from academic and industrial areas due to the significant number of potential applications in urban airspace. A traffic management system ...for these UAVs is needed to manage this future traffic. Tactical conflict resolution for unmanned aerial systems (UASs) is an essential piece of the puzzle for the future UAS Traffic Management (UTM), especially in very low-level (VLL) urban airspace. Unlike conflict resolution in higher altitude airspace, the dense high-rise buildings are an essential source of potential conflict to be considered in VLL urban airspace. In this paper, we propose an attention-based deep reinforcement learning approach to solve the tactical conflict resolution problem. Specifically, we formulate this task as a sequential decision-making problem using Markov Decision Process (MDP). The double deep Q network (DDQN) framework is used as a learning framework for the host drone to learn to output conflict-free maneuvers at each time step. We use the attention mechanism to model the individual neighbor’s effect on the host drone, endowing the learned conflict resolution policy to be adapted to an arbitrary number of neighboring drones. Lastly, we build a simulation environment with various scenarios covering different types of encounters to evaluate the proposed approach. The simulation results demonstrate that our proposed algorithm provides a reliable solution to minimize secondary conflict counts compared to learning and non-learning-based approaches under different traffic density scenarios.
•A double deep Q network with attention mechanism (DDQN-attention) learning framework is proposed.•Introduce a quantitative metric, domino effect count (DEC), to measure the domino conflict count of action made by individual unmanned aerial vehicles (UAVs) at every time step.•The superiority of DDQN-attention on reducing domino conflict during tactical conflict resolution is demonstrated through simulation.•The DDQN-attention can enable the UAV to perform de-conflict with consideration of domino conflict reduction given the appropriate training environment.
This paper aims to address a challenging problem of a drone swarm for a specific mission by reaching a desired region, through an unknown environment. A bio-inspired flocking algorithm with adaptive ...goal-directed strategy (AGDS) is proposed and developed for the drones swarmed across unknown environments. Each drone employs a biological visual mechanism to sense obstacles in within local perceptible scopes. Task information of the destination is only given to a few specified drones (named as informed agents), rather than to all other individual drones (uninformed agents). With the proposed flocking swarm, the informed agents operate collectively with the remaining uninformed agents to achieve a common and overall mission. By virtue of numerical simulation, the AGDS and non-adaptive goal-directed strategy (non-AGDS) are both presented and evaluated. Experiments by flying six DJI Tello quadrotors indoor are conducted to validate the developed flocking algorithm. Additional validations within canyon-like complicated scenarios have also been carried out. Both simulation and experimental results demonstrate the efficiency of the proposed swarm flocking algorithm with AGDS.
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