The problem of autonomous navigation for UAV inspection remains challenging as it requires effectively navigating in close proximity to obstacles, while accounting for dynamic risk factors such as weather conditions, communication reliability, and battery autonomy. This paper introduces the MOAR path planner which addresses the complexities of evolving risks during missions. It offers real-time trajectory adaptation while concurrently optimizing safety, time, and energy. The planner employs a risk-aware cost function that integrates pre-computed cost maps, the new concepts of damage and insertion costs, and an adaptive speed planning framework. With that, the optimal path is searched in a graph using a discrete representation of the state and action spaces. The method is evaluated through simulations and real-world flight tests. The results show the capability to generate real-time trajectories spanning a broad range of evaluation metrics—around 90% of the range occupied by popular algorithms. The proposed framework contributes by enabling UAVs to navigate more autonomously and reliably in critical missions.

Collaborative robots are more and more used in applications requiring robots and humans to work in proximity or direct contact. However, conventional collaborative robots powered by servo-geared actuators are intrinsically dangerous due to their high reflected inertia. Recent studies have shown that low inertia and high bandwidth (> 30 Hz) magnetorheological (MR) actuators have the potential to improve the safety of collaborative robots without reducing their force and speed capabilities. The main contribution of this paper is to provide a quantitative assessment of how MR actuators can contribute to reducing the impact forces with humans, and thus increase the safety of collaborative robots. Dynamic models, validated with simplified 1 DOF experiments, show that the safety level of collaborative robots can be increased by a factor up to 3 only by changing the conventional servo-geared actuator architectures for MR actuators with no other changes. The paper also presents a simple, reliable, and fast collision detection method based on joint angular velocity band-pass filtering, a method exploiting the unique low inertia and clean dynamics properties of MR actuators. Finally, an experimental comparison of representative collaborative robots demonstrates an impact force reduction of 10 times using MR actuators, fast collision detection, and passive foam padding.

Many path planning algorithms have been introduced so far, but most are costly, in path cost and in processing time, in large-scale uncluttered 3D environments such as underground mining stopes explored by an unmanned aerial vehicle (UAV). Rapidly-exploring Random Tree (RRT) algorithms are popular because of their probabilistic completeness and rapidity in finding a feasible path in single-query problems. Many of the algorithms (e.g. Informed RRT*, RRT#) developed to improve RRT need considerable time to converge in large environments. Shortcutting an RRT is an old idea that has been proven to outperform RRT variants. This paper introduces a new method, RRT-Rope, that aims at finding a near-optimal solution in a drastically shorter amount of time. The proposed approach benefits from fast computation of a feasible path with an altered version of RRT-connect, and post-processes it quickly with a deterministic shortcutting technique, taking advantage of intermediate nodes added to each branch of the tree. This paper presents simulations and statistics carried out to show the efficiency of RRT-Rope, which gives better results in terms of path cost and computation time than other popular RRT variations and shortening techniques in all our simulation environments, and is up to 70% faster than the next best algorithm in a representative stope.

Rotary-Wing Air Vehicles (RW-UAVs), also referred to as drones, have gained in popularity over the last few years. Intrusions over secured areas have become common and authorities are actively looking for solutions to detect and localize undesired drones. The sound generated by the propellers of the RW-UAVs is powerful enough to be perceived by a human observer nearby. In this paper, we examine the use of particle filtering to detect and localize in 3D the position of a RW-UAV based on sound source localization (SSL) over distributed microphone arrays (MAs). Results show that the proposed method is able to detect and track a drone with precision, as long as the noise emitted by the RW-UAVs dominates the background noise.

We implement a 2D computational model to investigate the unsteady aerodynamic effects not captured by classical quasi-steady models. We compare numerical simulation results, experimental measurements and quasi-steady predictions to demonstrate the strength of the numerical tool in identifying unsteady fluid mechanisms and improving propulsive efficiency of flapping wing robots. In particular, this study quantifies the effect of the relative phase between wing degrees of freedom δ on lift and drag production. The computational model also identifies unsteady effects such as wake capture and downwash that are not accounted for in classical quasi-steady models. To examine the accuracy of our computational model, we fabricate millimeter-scale wings through the SCM fabrication processes and measure flapping kinematics and dynamics. The experiments show 2D computational model is 44% more accurate than the quasi-steady model and can be further used to improve wing morphology for better aerodynamic performance.

Articulation based on flexure hinges is increasingly popular in microrobotics because of the absence of Coulomb friction, ease of manufacturability, fluid motion, durability, and large angular ranges. However, the inherent flexibility of these hinges makes modeling very complex and specific to the particular engineering applications for which they were developed. In this paper we describe the development and testing of a simplified, versatile method for modeling the stress on a flexure hinge under multi-axis loads in order to maximize hinge lifespan. We also discuss other stress concentration reducing features and design rules that can be applied to more general flexure hinge designs to further extend hinge lifespan.

A dynamic model of a jump glider is presented and correlated with the results obtained with a prototype glider. The glider uses a carbon fiber spring and a main wing that pivots approximately parallel to the airflow during ascent and latches into place for a gliding descent. The robot demonstrates longer traveled distance than an equivalent drag-free ballistic mass. A detailed numerical and a simplified algebraic model are also introduced, which are useful for exploring design tradeoffs and performance. These models suggest ways to improve the traveled distance and indicate that with modest variations in the wing angle of attack during ascent, one can choose from a variety of launch angles to accommodate variations in ground friction without greatly compromising range.

This paper presents a wing characterization method for insect-scale flapping-wing robots. A quasi-steady model is developed to predict passive wing pitching at mid-stroke. Millimeter scale wings and passive hinges are manufactured using the SCM fabrication processes. Flapping experiments at various frequencies and driving voltages are performed to extract kinematics for comparison with the quasi-steady predictions. These experiments examine the validity of the quasi-steady model and demonstrate the robustness of the wing characterization method. In addition, because time-averaged lift and drag are strongly correlated with flapping kinematics, quasi-steady prediction of wing kinematics directly leads to predictions of lift and drag generation. Given a flapping frequency and a driving voltage, the model computes the hinge stiffness that leads to optimal flapping kinematics. This reduces the number of flapping experiments required for wing characterization by a factor of four.

Dynamic perching maneuvers for fixed-wing aircraft are becoming increasingly plausible due to recent progress in perching using `micro-spines' mounted on tuned suspensions and, separately, on feedback motion planning techniques for post-stall maneuvering. In this paper, we bring these complementary techniques together by efficiently estimating the mechanical stability of the plane when it makes contact with a vertical surface; the resulting landing funnel can then be used in a feedback motion planning algorithm for the flight controller. We consider a simplified model of the perching dynamics and report an extension of the region of attraction techniques, using sums-of-squares optimization, which combines polynomial approximations of barrier constraints with the traditional Lyapunov methods to achieve tight estimation of the true region of attraction for the model. We demonstrate the new method on a variety of design parameters for the perching system, suggesting a potential use as a mechanical system or controller design tool.

We present an approach that builds upon previous developments in unmanned air vehicles and climbing robots and seeks to emulate the capabilities of bats, insects and certain birds that combine powered flight with the ability to land and perch on sloped and vertical surfaces. As it approaches a wall, the plane executes an intentional pitch-up maneuver to shed speed and present its feet for landing. On contact, a nonlinear suspension dissipates the remaining kinetic energy and directs interaction forces toward the feet to engage small asperities on surfaces such as brick or concrete. The focus of the work in this paper is on the controller used for sensing a wall and executing vertical landing and take-off procedures and on the mechanisms developed for spine engagement and disengagement.

We present an approach that builds upon previous developments in unmanned air vehicles and climbing robots and seeks to emulate the capabilities of bats, insects and certain birds that combine powered flight with the ability to land and perch on sloped and vertical surfaces. As it approaches a wall, the plane executes an intentional pitch-up maneuver to shed speed and present its feet for landing. On contact, a nonlinear suspension dissipates the remaining kinetic energy and directs interaction forces toward the feet, where microspines can engage small asperities on surfaces such as brick or concrete. The plane can then take off by disengaging the spines and lifting off, pointing its nose up and away from the wall to slowly build forward speed until it can resume normal flight.

Without a properly designed power management unit, power failures are to be expected on systems raining on batteries. Unfortunately, there is no low cost fully configurable unit that is available on the market for small mobile robots. This paper presents the design of a power management unit for a six legged pneumatic robot that runs at a top speed of 1.11 m/s. The system consists of a main unit that acts as a fast charger, a power supply and a monitor of the energy left available to the robot. This unit communicates with small circuit embedded on each NiMH battery, which gives their level and stores the battery's parameters so that the charger can adapt its charging algorithm to batteries of different capacity or chemistry. Furthermore, this power management unfit takes advantage of the controller area network (CAN) and the PIC18F brain to act as a monitoring, fully configurable, remotely controllable and autonomous node.