Miniature Autonomous Blimp Research Articles

Distributed field mapping for mobile sensor teams using a derivative‐free optimisation algorithm

AbstractThe authors propose a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field using a Gaussian Process (GP). The authors’ strategy arises by balancing exploration objectives between areas of high error and high variance. As computing high error regions is impossible since the scalar field is unknown, a bio‐inspired approach known as Speeding‐Up and Slowing‐Down is leveraged to track the gradient of the GP error. This approach achieves global field‐learning convergence and is shown to be resistant to poor hyperparameter tuning of the GP. This approach is validated in simulations and experiments using 2D wheeled robots and 2D flying miniature autonomous blimps.

A Particle Fusion Approach for Distributed Filtering and Smoothing

Distributed state estimation is an important tool for coordinated team decision-making and typically involves sharing information between robots in order to outperform individual state estimation. The shared information typically takes the form of relative measurements which allow team members to act as virtual sensors for other robots where the virtual sensor uncertainty is corrupted also by the team member’s state uncertainty. However, incorporating relative measurements commonly depends on a pointwise product operation in most distributed estimation techniques which is well defined for continuously-valued distributions but ill-defined for particle-based distributions. We propose a drop-in replacement for the pointwise product based on using the generalized Hölder’s inequality to upper-bound the product over a series of grid cell sets that discretize the state space. This upper-bound is well defined for particle-based distributions and allows for tighter approximations by decreasing the volume of the sets. We leverage the approach to realize two distributed estimation strategies that use a pointwise product, the Kullback–Leibler Average and Belief Propagation and use these methods in simulations and experiments with a pair of miniature autonomous blimps. We found that after distributed smoothing, we were able to achieve an average improvement of ∼25.1% and ∼35.2% in the position tracking error, indicating our distributed smoothing strategy is able to improve the tracking performance of our initial filtering estimates.

Human pointing motion during interaction with an autonomous blimp

We investigate the interaction between a human and a miniature autonomous blimp using a wand as pointing device. The wand movement generated by the human is followed by the blimp through a tracking controller. The Vector Integration to Endpoint (VITE) model, previously applied to human–computer interface (HCI), has been applied to model the human generated wand movement when interacting with the blimp. We show that the closed-loop human–blimp dynamics are exponentially stable. Similar to HCI using computer mouse, overshoot motion of the blimp has been observed. The VITE model can be viewed as a special reset controller used by the human to generate wand movements that effectively reduce the overshoot of blimp motion. Moreover, we have observed undershoot motion of the blimp due to its inertia. The asymptotic stability of the human–blimp dynamics is beneficial towards tolerating the undershoot motion of the blimp.

Monocular Vision-Based Localization and Pose Estimation with a Nudged Particle Filter and Ellipsoidal Confidence Tubes

This paper proposes a nudged particle filter for estimating the pose of a camera mounted on flying robots collecting a video sequence. The nudged particle filter leverages two image-to-pose and pose-to-image neural networks trained in an auto-encoder fashion with a dataset of pose-labeled images. Given an image, the retrieved camera pose using the image-to-pose network serves as a special particle to nudge the set of particles generated from the particle filter while the pose-to-image network serves to compute the likelihoods of each particle. We demonstrate that such a nudging scheme effectively mitigates low likelihood samplings during the particle propagation step. Ellipsoidal confidence tubes are constructed from the set of particles to provide a computationally efficient bound on localization error. When an ellipsoidal tube self-intersects, the probability volume of the intersection can be significantly shrunken using a novel Dempster–Shafer probability mass assignment algorithm. Starting from the intersection, a loop closure procedure is developed to move backward in time to shrink the volumes of the entire ellipsoidal tube. Experimental results using the Georgia Tech Miniature Autonomous Blimp platform are provided to demonstrate the feasibility and effectiveness of the proposed algorithms in providing localization and pose estimation based on monocular vision.

Autopilot design of a class of miniature autonomous blimps enabled by switched controllers

Autopilot design of a class of miniature autonomous blimps enabled by switched controllers

Viscoelastic Fluid-Inspired Swarm Behavior to Reduce Susceptibility to Local Minima: The Chain Siphon Algorithm

We present a novel distributed robotic swarm algorithm inspired by the open channel siphon phenomenon displayed in certain viscoelastic fluids. Self-siphoning viscoelastic fluids are often able to mitigate the trapping effects of local minima in the environment. Using a similar strategy, our algorithm enables a robot swarm to mitigate the trapping effects of local minima in potential fields. Once a robot senses the goal, local communication between robots is used to propagate path-to-goal gradient information through the swarm's communication graph. This information is used to augment each agent's local potential field, reducing the local minima traps and often eliminating them. We perform hardware experiments using the Georgia Tech Miniature Autonomous Blimp (GT-MAB) aerial robotic platforms as well as Monte Carlo simulations conducted in the Simulating Collaborative Robots in Massive Multi-Agent Game Execution (SCRIMMAGE) simulator. We compare the new method to other potential field based swarm behaviors that both do and do not incorporate local minima fixes. The distributed algorithm generates self-siphoning behavior within the robotic swarm, and this reduces its susceptibility to local minima.

Swing-Reducing Flight Control System for an Underactuated Indoor Miniature Autonomous Blimp

This article presents the first swing-stabilization control for indoor miniature autonomous blimps (MABs). Indoor MABs are safe to operate in close proximities to humans and can fly for multiple hours, but swing oscillation is commonly observed due to their underactuated design and unique aerodynamic shape. In this article, we analyze the flight characteristics of indoor MABs, and describe the design of the swing-reducing flight control system in detail. Key mechatronic designs for swing-stabilization control are also presented. Experimental results show that the proposed controller can keep the blimp travel at the desired velocity while effectively stabilizing the swing oscillation. The swing-reducing velocity controller is then expanded for station keeping and waypoint navigation in a 3-D space.

Learning and detecting abnormal speed of marine robots

This article presents anomaly detection algorithms for marine robots based on their trajectories under the influence of unknown ocean flow. A learning algorithm identifies the flow field and estimates the through-water speed of a marine robot. By comparing the through-water speed with a nominal speed range, the algorithm is able to detect anomalies causing unusual speed changes. The identified ocean flow field is used to eliminate false alarms, where an abnormal trajectory may be caused by unexpected flow. The convergence of the algorithms is justified through the theory of adaptive control. The proposed strategy is robust to speed constraints and inaccurate flow modeling. Experimental results are collected on an indoor testbed formed by the Georgia Tech Miniature Autonomous Blimp and Georgia Tech Wind Measuring Robot, while simulation study is performed for ocean flow field. Data collected in both studies confirm the effectiveness of the algorithms in identifying the through-water speed and the detection of speed anomalies while avoiding false alarms.

Modeling and Control of Swing Oscillation of Underactuated Indoor Miniature Autonomous Blimps

Swing oscillation is widely observed among indoor miniature autonomous blimps (MABs) due to their underactuated design and unique aerodynamic shape. This paper presents the modeling, identification and control system design that reduce the swing oscillation of an MAB during hovering flight. We establish a dynamic model to describe the swing motion of the MAB. The model parameters are identified from both physical measurements, computer modeling and experimental data captured during flight. A control system is designed to stabilize the swing motion with features including low latency and center-of-mass (CM) position estimation. The modeling and control methods are verified with the Georgia-Tech Miniature Autonomous Blimp (GT-MAB) during hovering flight. The experimental results show that the proposed methods can effectively reduce the swing oscillation of GT-MAB.

Autonomous flying blimp interaction with human in an indoor space

We present the Georgia Tech Miniature Autonomous Blimp (GT-MAB), which is designed to support human-robot interaction experiments in an indoor space for up to two hours. GT-MAB is safe while flying in close proximity to humans. It is able to detect the face of a human subject, follow the human, and recognize hand gestures. GT-MAB employs a deep neural network based on the single shot multibox detector to jointly detect a human user’s face and hands in a real-time video stream collected by the onboard camera. A human-robot interaction procedure is designed and tested with various human users. The learning algorithms recognize two hand waving gestures. The human user does not need to wear any additional tracking device when interacting with the flying blimp. Vision-based feedback controllers are designed to control the blimp to follow the human and fly in one of two distinguishable patterns in response to each of the two hand gestures. The blimp communicates its intentions to the human user by displaying visual symbols. The collected experimental data show that the visual feedback from the blimp in reaction to the human user significantly improves the interactive experience between blimp and human. The demonstrated success of this procedure indicates that GT-MAB could serve as a flying robot that is able to collect human data safely in an indoor environment.