A Novel Low-Cost, Recyclable, Easy-to-Build Robot Blimp For Transporting Supplies in Hard-to-Reach Locations

Rural communities in remote areas often encounter significant challenges when it comes to accessing emergency healthcare services and essential supplies due to a lack of adequate transportation infrastructure. The situation is further exacerbated by poorly maintained, damaged, or flooded roads, making it arduous for rural residents to obtain the necessary aid in critical situations. Limited budgets and technological constraints pose additional obstacles, hindering the prompt response of local rescue teams during emergencies. The transportation of crucial resources, such as medical supplies and food, plays a vital role in saving lives in these situations. In light of these obstacles, our objective is to improve accessibility and alleviate the suffering of vulnerable populations by automating transportation tasks using low-cost robotic systems. We propose a low-cost, easy-to-build blimp robot (UAVs), that can significantly enhance the efficiency and effectiveness of local emergency responses.

SBlimp: Design, Model, and Translational Motion Control for a Swing-Blimp

We present an aerial vehicle composed of a custom quadrotor with tilted rotors and a helium balloon, called SBlimp. We propose a novel control strategy that takes advantage of the natural stable attitude of the blimp to control translational motion. Different from cascade controllers in the literature that controls attitude to achieve desired translational motion, our approach directly controls the linear velocity regardless of the heading orientation of the vehicle. As a result, the vehicle swings during the translational motion. We provide a planar analysis of the dynamic model, demonstrating stability for our controller. Our design is evaluated in numerical simulations with different physical factors and validated with experiments using a real-world prototype, showing that the SBlimp is able to achieve stable translation regardless of its orientation.

Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach

Aerial operation in turbulent environments is a challenging problem due to the chaotic behavior of the flow. This problem is made even more complex when a team of aerial robots is trying to achieve coordinated motion in turbulent wind conditions. In this paper, we present a novel multi-robot controller to navigate in turbulent flows, decoupling the trajectory-tracking control from the turbulence compensation via a nested control architecture. Unlike previous works, our method does not learn to compensate for the air-flow at a specific time and space. Instead, our method learns to compensate for the flow based on its effect on the team. This is made possible via a deep reinforcement learning approach, implemented via a Graph Convolutional Neural Network (GCNN)-based architecture, which enables robots to achieve better wind compensation by processing the spatial-temporal correlation of wind flows across the team. Our approach scales well to large robot teams -- as each robot only uses information from its nearest neighbors -- , and generalizes well to robot teams larger than seen in training. Simulated experiments demonstrate how information sharing improves turbulence compensation in a team of aerial robots and demonstrate the flexibility of our method over different team configurations.

Toward Fine Contact Interactions: Learning to Control Normal Contact Force with Limited Information

Dexterous manipulation of objects through fine control of physical contacts is essential for many important tasks of daily living. A fundamental ability underlying fine contact control is compliant control, \textit{i.e.}, controlling the contact forces while moving. For robots, the most widely explored approaches heavily depend on models of manipulated objects and expensive sensors to gather contact location and force information needed for real-time control. The models are difficult to obtain, and the sensors are costly, hindering personal robots' adoption in our homes and businesses. This study performs model-free reinforcement learning of a normal contact force controller on a robotic manipulation system built with a low-cost, information-poor tactile sensor. Despite the limited sensing capability, our force controller can be combined with a motion controller to enable fine contact interactions during object manipulation. Promising results are demonstrated in non-prehensile, dexterous manipulation experiments.

Finding Optimal Modular Robots for Aerial Tasks

Traditional aerial vehicles have limitations in their capabilities due to actuator constraints, such as motor saturation. The hardware components and their arrangement are designed to satisfy specific requirements and are difficult to modify during operation. To address this problem, we introduce a versatile modular multi-rotor vehicle that can change its capabilities by reconfiguration. Our modular robot consists of homogeneous cuboid modules, propelled by quadrotors with tilted rotors. Depending on the number of modules and their configuration, the robot can expand its actuation capabilities. In this paper, we build a mathematical model for the actuation capability of a modular multi-rotor vehicle and develop methods to determine if a vehicle is capable of satisfying a task requirement. Based on this result, we find the optimal configurations for a given task. Our approach is validated in realistic 3D simulations, showing that our modular system can adapt to tasks with varying requirements.

Dynamic Crowd Vetting: Collaborative Detection of Malicious Robots in Dynamic Communication Networks

Coordination in a large number of networked robots is a challenging task, especially when robots are constantly moving around the environment and there are malicious attacks within the network. Various approaches in the literature exist for detecting malicious robots, such as message sampling or suspicious behavior analysis. However, these approaches require every robot to sample every other robot in the network, leading to a slow detection process that degrades team performance. This paper introduces a method that significantly decreases the detection time for legitimate robots to identify malicious robots in a scenario where legitimate robots are randomly moving around the environment. Our method leverages the concept of ``Dynamic Crowd Vetting" by utilizing observations from random encounters and trusted neighboring robots' opinions to quickly improve the accuracy of detecting malicious robots. The key intuition is that as long as each legitimate robot accurately estimates the legitimacy of at least some fixed subset of the team, the second-hand information they receive from trusted neighbors is enough to correct any misclassifications and provide accurate trust estimations of the rest of the team. We show that the size of this fixed subset can be characterized as a function of fundamental graph and random walk properties. Furthermore, we formally show that as the number of robots in the team increases the detection time remains constant. We develop a closed form expression for the critical number of time-steps required for our algorithm to successfully identify the true legitimacy of each robot to within a specified failure probability. Our theoretical results are validated through simulations demonstrating significant reductions in detection time when compared to previous works that do not leverage trusted neighbor information.

Forming and Controlling Hitches in Midair Using Aerial Robots

The use of cables for aerial manipulation has shown to be a lightweight and versatile way to interact with objects. However, fastening objects using cables is still a challenge and human is required. In this work, we propose a novel way to secure objects using hitches. The hitch can be formed and morphed in midair using a team of aerial robots with cables. The hitch's shape is modeled as a convex polygon, making it versatile and adaptable to a wide variety of objects. We propose an algorithm to form the hitch systematically. The steps can run in parallel, allowing hitches with a large number of robots to be formed in constant time. We develop a set of actions that include different actions to change the shape of the hitch. We demonstrate our methods using a team of aerial robots via simulation and actual experiments.

Folding Knots Using a Team of Aerial Robots

From ancient times, humans have been using cables and ropes to tie, carry, and manipulate objects by folding knots. However, automating knot folding is challenging because it requires dexterity to move a cable over and under itself. In this paper, we propose a method to fold knots in midair using a team of aerial vehicles. We take advantage of the fact that vehicles are able to fly in between cable segments without any re-grasping. So the team grasps the cable from the floor, and releases it once the knot is folded. Based on a composition of catenary curves, we simplify the complexity of dealing with an infinite-dimensional configuration space of the cable, and formally propose a new knot representation. Such representation allows us to design a trajectory that can be used to fold knots using a leader-follower approach. We show that our method works for different types of knots in simulations. Additionally, we show that our solution is also computationally efficient and can be executed in real-time.

PogoDrone: Design, Model, and Control of a Jumping Quadrotor

We present a design, model, and control for a novel jumping-flying robot that is called PogoDrone. The robot is composed of a quadrotor with a passive mechanism for jumping. The robot can continuously jump in place or fly like a normal quadrotor. Jumping in place allows the robot to quickly move and operate very close to the ground. For instance, in agricultural applications, the jumping mechanism allows the robot to take samples of soil. We propose a hybrid controller that switches from attitude to position control to allow the robot to fall horizontally and recover to the original position. We compare the jumping mode with the hovering mode to analyze the energy consumption. In simulations, we evaluate the effect of different factors on energy consumption. In real experiments, we show that our robot can repeatedly impact the ground, jump, and fly in a physical environment.

Modular Multi-Rotors: From Quadrotors to Fully-Actuated Aerial Vehicles

Traditional aerial vehicles have specific characteristics to perform specific tasks. For instance, in aerial transportation, the vehicles are limited with a maximum payload that cannot be extended to transport heavier objects. We propose a versatile modular robotic system that can increase its payload and controllable degrees of freedom by reconfiguring heterogeneous modules; we call it H-ModQuad. The system consists of cuboid modules, propelled by quadrotors with tilted propellers that can generate forces in different directions. We present two module designs with different actuation properties that enhance the capabilities of the assembled robot. By assembling different types of modules, H-ModQuad can increase its controllable degrees of freedom from 4 to 5 and 6 depending on its configuration. We model the modular vehicle and propose a general control strategy for all possible numbers of controllable degrees of freedom. We extend the concept of the actuation ellipsoid to find the best reference orientation that can maximize the performance of the structure. Our approach is validated with experiments using actual robots, showing that the structure can perform independent actuation for rotation and translation.