This article describes a Voronoi-based path generation (VPG) algorithm for an energy-constrained mobile robot, such as an unmanned aerial vehicle (UAV). The algorithm solves a variation of the coverage path-planning problem where complete coverage of an area is not possible due to path-length limits caused by energy constraints on the robot. The algorithm works by modeling the path as a connected network of mass-spring-damper systems. The approach further leverages the properties of Voronoi diagrams to generate a potential field to move path waypoints to near-optimal configurations while maintaining path-length constraints. Simulation and physical experiments on an aerial vehicle are described. Simulated runtimes show linear-time complexity with respect to the number of path waypoints. Tests in variously shaped areas demonstrate that the method can generate paths in both convex and nonconvex areas. Comparison tests with other path generation methods demonstrate that the VPG algorithm strikes a good balance between runtime and optimality, with significantly better runtime than direct optimization, lower cost coverage paths than a lawnmower-style coverage path, and moderately better performance in both metrics than the most conceptually similar method. Physical experiments demonstrate the applicability of the VPG method to a physical UAV, and comparisons between real-world results and simulations show that the costs of the generated paths are within a few percent of each other, implying that analysis performed in simulation will hold for real-world application, assuming that the robot is capable of closely following the path and a good energy model is available.Note to Practitioners—For autonomous mobile-robotics-based applications where a robot equipped with a tool or sensor is required to survey an area for inspection, monitoring, cleaning, and so on, effectively covering the area is desirable. However, for energy-constrained systems such as aerial vehicles with limited flight time, complete coverage is not possible. Presented here is a new Voronoi-based path generation algorithm that takes energy constraints into account to generate waypoints for the robot to follow in a near-optimal configuration while maintaining path-length constraints. The approach is applied in simulation and experiments for an application in environmental monitoring using unmanned aerial vehicles.

A new nonparametric Bayesian-based motion planning algorithm for autonomous plume source term estimation (STE) and source seeking (SS) is presented in this paper. The algorithm is designed for mobile robots equipped with gas concentration sensors. Specifically, robots coordinate and utilize a Gaussian-plume likelihood model in a Bayesian-based STE process, then they simultaneously search for and navigate toward the source through model based, bioinspired SS methods such as biased-random-walk and surge-casting. Compared with the state-of-the-art Bayesian- and sensor-based STE/SS motion planners, the strategy described takes advantage of coordination between multiple robots and the estimated plume model for faster and more robust SS, rather than rely on direct or filtered sensor measurements. A set of Monte Carlo simulation studies are conducted to compare the performance between the uncoordinated and coordinated algorithms for different robot team sizes and starting conditions. Additionally, the algorithms are validated experimentally through a laboratory-safe, realistic humid-air plume that behaves similar to a gas plume, to test STE and SS using mobile ground robots equipped with humidity sensors. Simulation and experimental results show consistently that the algorithm involving coordination outperforms traditional bioinspired SS algorithms and it is approximately twice as fast as the uncoordinated case. Finally, the plume source is distorted to study the algorithm's limitations and impact on STE and SS, where results show that even for distorted plumes, useful source localization information can be obtained.

This paper presents the design, modeling, analysis, and experimental validation of an inductive resonant wireless power transfer (WPT) system to power a micro aerial vehicle (MAV). Using WPT, in general, enables longer flight times, virtually eliminates the need for batteries, and minimizes down time for recharging or replacing batteries. The proposed WPT system consists of a transmit coil, which can either be fixed to ground or placed on a mobile platform, and a receive coil carried by the MAV. The details of the WPT circuit design are presented. A power-transfer model is developed for the two-coil system, where the model is used to select suitable coil geometries to maximize the power received by the MAV for hovering. Analysis, simulation, and experimental results are presented to demonstrate the effectiveness of the WPT circuitry. Finally, a wirelessly powered MAV that hovers above the transmit coil is demonstrated in a laboratory setting.

This paper presents the design, fabrication, modeling, and performance characterization of a new 3D-printed ionic polymer-metal composite (IPMC) soft crawling robot. First, a precursor to an ionomeric polymer material (Nafion) is used to 3D print modular leg and body sections to create a caterpillar-like robot. Then, the printed components are activated, plated with electrodes, assembled together to create the robot from smart electroactive polymer material. The 3D-printed robot exploits the unique capabilities of IPMC materials; specifically, the actuation of the hydrated 3D-printed leg and body sections can be controlled by applying a voltage signal. In particular, the IPMC legs can grip and the body sections can expand and contract, allowing the robot to propel itself forward and backward. The newly-developed 3D-printing process and the robot design and modeling process are described. Experimental results are presented that show the prototype robot moving along a tube like a caterpillar or inchworm.

This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitable for both omnidirectional and perspective cameras, is proposed. The proposed approach avoids the need for accurate calibration of the extrinsic parameters of the omnidirectional camera as well as the intrinsic and extrinsic parameters of perspective camera. Additionally, the coefficients of the plane where the feature point moves relative to the camera frame can be uncertain. These uncertain constant parameters are estimated using an adaptive estimator. Uniform Semi-global Practical Asymptotic Stability (USPAS) of the system is shown using the Lyapunov approach. Experimental results are presented to demonstrate the effectiveness of the proposed control scheme.

This paper describes the development of a flying and walking robot, called the dynamic underactuated flying-walking (DUCK) robot. The DUCK robot combines a high-mobility flying platform, such as a quadcopter (quadrotor helicopter), with passive-dynamic legs to create a versatile system that can fly and walk. One of the advantages of passive-dynamic legs for walking is that additional actuators are not needed for terrestrial locomotion. Herein, a mathematical model is presented and simulations are used to help design a prototype robot. Experimental results demonstrate the feasibility of combining an aerial platform with passive-dynamic legs to create an effective flying and walking robot. In particular, two modes of walking are demonstrated: (1) passive walking down inclined surfaces for low-energy terrestrial locomotion, and (2) active (powered) walking by leveraging the capabilities of the flying platform, where thrust from the quadcopter's rotors enables the DUCK robot to take steps and walk on flat surfaces or up inclined surfaces.

This paper proposes a new adaptive repetitive visual-servo control system for a moving high-flying vehicle (HFV) with an uncalibrated camera to monitor, track, and precisely control the movements of a low-flying vehicle (LFV) or mobile ground robot. When deployed, a remote operator of the HFV defines the desired trajectory for the LFV in the HFV's camera frame (image frame). Due to the circulatory motion of the HFV, the resulting motion trajectory of the LFV in the image frame is periodic in time, thus an adaptive repetitive control system is exploited to improve the tracking precision from one operating period to the next. Not only is the adaptive control law able to deal with uncertainties in the camera's intrinsic and extrinsic parameters, but it can also tolerate uncertainties in the localization of the LFV. The design and stability analysis of the closed-loop control system is presented, where the Lyapunov approach is used to show stability. Simulations and experimental results are presented to demonstrate the effectiveness of the method for controlling the movement of a low-flying quadcopter vehicle. Results show good tracking performance for three simulated test cases, where the average maximum tracking error is reduced by approximately 75% compared to the performance of a standard adaptive visual-servo controller.

This paper presents a stochastic approach for automatic collision avoidance for tele-operated unmanned aerial vehicles (UAVs). Collision detection and mitigation in the presence of uncertainty is an important problem to address because on-board sensing and state estimation uncertainties are inherent in real-world systems. A feedforward-based algorithm is described that continually extrapolates the future trajectory of the vehicle given the current operator control input for collision avoidance. If the predicted probability of a collision is greater than a user-defined confidence bound, the algorithm overrides the operator control input with the nearest, safe command signal to steer the robot away from obstacles, while maintaining user intent. The algorithm is implemented on a simulated quadrotor helicopter (quadcopter) with varying amounts of artificial uncertainty. Simulation results show that for a given confidence bound, the aerial robot is able to avoid collisions, even in a situation where the operator is deliberately attempting to crash the vehicle.

This paper presents the design, fabrication, modeling, and demonstration of a micro spherical rolling and flying robot, with a total mass and payload of 35 g and 10 g, respectively. The micro aerial terrestrial robot (ATR) has the ability to fly through the air or roll on the ground, for applications that include search and rescue, mapping, and surveillance. Its unique size makes is easily portable and enables the robot to enter and maneuver around in tight spaces such as air ducts. The design centers around a micro-quadcopter encased in a lightweight spherical exoskeleton that can rotate about the quadcopter. The spherical exoskeleton offers agile ground locomotion while maintaining characteristics of a basic aerial robot in flying mode. Details of the system modeling, design and fabrication are discussed, including the robot's turning capabilities over ground and the lightweight spring-steel exoskeleton. The prototype ATR is experimentally validated in aerial and terrestrial mode, and results show that the ATR traveling over the same distance in rolling mode is 260 percent more efficient than a traditional flying-only robot and in flying mode the system is only 39 percent less efficient. Experimental results also demonstrate transition between modes of locomotion and curved, rolling trajectories.

Advanced metal matrix composite (MMC) materials combine a metal and at least another part, such as a ceramic, to form a material with enhanced mechanical properties compared to traditional materials. An aluminium silicon carbide metal matrix material is investigated for compliant flexure design for nanopositioning systems. The material is up to 60% stiffer than traditional aluminum alloy with little to no increase in density. It is shown that the dynamic response of flexure-guided stages can be more easily tailored using the MMC. A flexure-based nanopositioner is designed to exhibit low cross-coupling behavior at high frequencies using the MMC. Finite element analysis (FEA) is used to guide the design process, and a prototype stage is created and evaluated. The measured dynamic response agrees with the FEA modeling; particularly, by using the MMC the actuation mode of the stage can be designed to occur before the out-of-plane modes. The improvement in performance of using the MMC outweighs the disadvantages that include increase cost of the material and lower manufacturability compared to traditional aluminum alloys. The stage is characterized to demonstrate the advantages of the material.