We propose a new multi-instance dynamic RGB-D SLAM system using an object-level octree-based volumetric representation. It can provide robust camera tracking in dynamic environments and at the same time, continuously estimate geometric, semantic, and motion properties for arbitrary objects in the scene. For each incoming frame, we perform instance segmentation to detect objects and refine mask boundaries using geometric and motion information. Meanwhile, we estimate the pose of each existing moving object using an object-oriented tracking method and robustly track the camera pose against the static scene. Based on the estimated camera pose and object poses, we associate segmented masks with existing models and incrementally fuse corresponding colour, depth, semantic, and foreground object probabilities into each object model. In contrast to existing approaches, our system is the first system to generate an object-level dynamic volumetric map from a single RGB-D camera, which can be used directly for robotic tasks. Our method can run at 2-3 Hz on a CPU, excluding the instance segmentation part. We demonstrate its effectiveness by quantitatively and qualitatively testing it on both synthetic and real-world sequences.
Exploration and collision-free navigation through an unknown environment is a fundamental task for autonomous robots. In this paper, a novel exploration strategy for Micro Aerial Vehicles (MAVs) is presented. The goal of the exploration strategy is the reduction of map entropy regarding occupancy probabilities, which is reflected in a utility function to be maximised. We achieve fast and efficient exploration performance with tight integration between our octree-based occupancy mapping approach, frontier extraction, and motion planningas a hybrid between frontier-based and sampling-based exploration methods. The computationally expensive frontier clustering employed in classic frontier-based exploration is avoided by exploiting the implicit grouping of frontier voxels in the underlying octree map representation. Candidate next-views are sampled from the map frontiers and are evaluated using a utility function combining map entropy and travel time, where the former is computed efficiently using sparse raycasting. These optimisations along with the targeted exploration of frontierbased methods result in a fast and computationally efficient exploration planner. The proposed method is evaluated using both simulated and real-world experiments, demonstrating clear advantages over state-of-the-art approaches.
Additive manufacturing methods 1-4 using static and mobile robots are being developed for both on-site construction 5-8 and off-site prefabrication 9, 10 . Here we introduce a new method of additive manufacturing, referred to as Aerial Additive Manufacturing (Aerial-AM), that utilizes a team of aerial robots inspired by natural builders 11 such as wasps who use collective building methods 12, 13 . We present a scalable multi-robot 3D printing and path planning framework that enables robot tasks and population size to be adapted to variations in print geometry throughout a building mission. The multi-robot manufacturing framework allows for autonomous 3D printing under human supervision, real-time assessment of printed geometry and robot behavioural adaptation. To validate autonomous Aerial-AM based on the framework, we develop BuilDrones for depositing materials during flight and ScanDrones for measuring print quality, and integrate a generic real-time model-predictive-control scheme with the Aerial-AM robots. In addition, we integrate a dynamically self-aligning delta manipulator with the BuilDrone to further improve manufacturing accuracy to 5mm for printing geometry with precise trajectory requirements, and develop four cementitious-polymeric composite mixtures suitable for continuous material deposition. We demonstrate proof-of-concept prints including a cylinder of 2.05m with a rapid curing insulation foam material and a cylinder of 0.18m with structural pseudoplastic cementitious material, a light-trail virtual print of a dome-like geometry, and multi-robot simulations.
One of the main challenges for autonomous aerial robots is to land safely on a target position on varied surface structures in real‐world applications. Most of current aerial robots (especially multirotors) use only rigid landing gears, which limit the adaptability to environments and can cause damage to the sensitive cameras and other electronics onboard. This paper presents a bioinpsired landing system for autonomous aerial robots, built on the inspire–abstract–implement design paradigm and an additive manufacturing process for soft thermoplastic materials. This novel landing system consists of 3D printable Sarrus shock absorbers and soft landing pads which are integrated with an one‐degree‐of‐freedom actuation mechanism. Both designs of the Sarrus shock absorber and the soft landing pad are analyzed via finite element analysis, and are characterized with dynamic mechanical measurements. The landing system with 3D printed soft components is characterized by completing landing tests on flat, convex, and concave steel structures and grassy field in a total of 60 times at different speeds between 1 and 2 m/s. The adaptability and shock absorption capacity of the proposed landing system is then evaluated and benchmarked against rigid legs. It reveals that the system is able to adapt to varied surface structures and reduce impact force by 540N at maximum. The bioinspired landing strategy presented in this paper opens a promising avenue in Aerial Biorobotics, where a cross‐disciplinary approach in vehicle control and navigation is combined with soft technologies, enabled with adaptive morphology.
The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) held in spring 2017 was a very successful competition well attended by teams from all over the world. One of the challenges (Challenge 1) required an aerial robot to detect, follow, and land on a moving target in a fully autonomous fashion. In this paper, we present the hardware components of the micro air vehicle (MAV) we built with off the self components alongside the designed algorithms that were developed for the purposes of the competition. We tackle the challenge of landing on a moving target by adopting a generic approach, rather than following one that is tailored to the MBZIRC Challenge 1 setup, enabling easy adaptation to a wider range of applications and targets, even indoors, since we do not rely on availability of global positioning system. We evaluate our system in an uncontrolled outdoor environment where our MAV successfully and consistently lands on a target moving at a speed of up to 5.0 m/s.
Aerial manipulation aims at combining the maneuverability of aerial vehicles with the manipulation capabilities of robotic arms. This, however, comes at the cost of the additional control complexity due to the coupling of the dynamics of the two systems. In this paper we present a Nonlinear Model Predictive Control (NMPC) specifically designed for Micro Aerial Vehicles (MAVs) equipped with a robotic arm. We formulate a hybrid control model for the combined MAV-arm system which incorporates interaction forces acting on the end effector. We explain the practical implementation of our algorithm and show extensive experimental results of our custom built system performing multiple 'aerial-writing' tasks on a whiteboard, revealing accuracy in the order of millimetres.
Safe and precise reference tracking is a crucial characteristic of Micro Aerial Vehicles (MAVs) that have to operate under the influence of external disturbances in cluttered environments. In this paper, we present a Nonlinear Model Predictive Control (NMPC) that exploits the fully physics based non-linear dynamics of the system. We furthermore show how the moment and thrust control inputs can be transformed into feasible actuator commands. In order to guarantee safe operation despite potential loss of a motor under which we show our system keeps operating safely, we developed an Extended Kalman Filter (EKF) based motor failure identification algorithm. We verify the effectiveness of the developed pipeline in flight experiments with and without motor failures.
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