4,579 research outputs found
Search-based 3D Planning and Trajectory Optimization for Safe Micro Aerial Vehicle Flight Under Sensor Visibility Constraints
Safe navigation of Micro Aerial Vehicles (MAVs) requires not only
obstacle-free flight paths according to a static environment map, but also the
perception of and reaction to previously unknown and dynamic objects. This
implies that the onboard sensors cover the current flight direction. Due to the
limited payload of MAVs, full sensor coverage of the environment has to be
traded off with flight time. Thus, often only a part of the environment is
covered.
We present a combined allocentric complete planning and trajectory
optimization approach taking these sensor visibility constraints into account.
The optimized trajectories yield flight paths within the apex angle of a
Velodyne Puck Lite 3D laser scanner enabling low-level collision avoidance to
perceive obstacles in the flight direction. Furthermore, the optimized
trajectories take the flight dynamics into account and contain the velocities
and accelerations along the path.
We evaluate our approach with a DJI Matrice 600 MAV and in simulation
employing hardware-in-the-loop.Comment: In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Montreal, Canada, May 201
Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups
A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper
PAMPC: Perception-Aware Model Predictive Control for Quadrotors
We present the first perception-aware model predictive control framework for
quadrotors that unifies control and planning with respect to action and
perception objectives. Our framework leverages numerical optimization to
compute trajectories that satisfy the system dynamics and require control
inputs within the limits of the platform. Simultaneously, it optimizes
perception objectives for robust and reliable sens- ing by maximizing the
visibility of a point of interest and minimizing its velocity in the image
plane. Considering both perception and action objectives for motion planning
and control is challenging due to the possible conflicts arising from their
respective requirements. For example, for a quadrotor to track a reference
trajectory, it needs to rotate to align its thrust with the direction of the
desired acceleration. However, the perception objective might require to
minimize such rotation to maximize the visibility of a point of interest. A
model-based optimization framework, able to consider both perception and action
objectives and couple them through the system dynamics, is therefore necessary.
Our perception-aware model predictive control framework works in a
receding-horizon fashion by iteratively solving a non-linear optimization
problem. It is capable of running in real-time, fully onboard our lightweight,
small-scale quadrotor using a low-power ARM computer, to- gether with a
visual-inertial odometry pipeline. We validate our approach in experiments
demonstrating (I) the contradiction between perception and action objectives,
and (II) improved behavior in extremely challenging lighting conditions
Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios
Perching on the moving platforms is a promising solution to enhance the
endurance and operational range of quadrotors, which could benefit the
efficiency of a variety of air-ground cooperative tasks. To ensure robust
perching, tracking with a steady relative state and reliable perception is a
prerequisite. This paper presents an adaptive dynamic tracking and perching
scheme for autonomous quadrotors to achieve tight integration with moving
platforms. For reliable perception of dynamic targets, we introduce elastic
visibility-aware planning to actively avoid occlusion and target loss.
Additionally, we propose a flexible terminal adjustment method that adapts the
changes in flight duration and the coupled terminal states, ensuring full-state
synchronization with the time-varying perching surface at various angles. A
relaxation strategy is developed by optimizing the tangential relative speed to
address the dynamics and safety violations brought by hard boundary conditions.
Moreover, we take SE(3) motion planning into account to ensure no collision
between the quadrotor and the platform until the contact moment. Furthermore,
we propose an efficient spatiotemporal trajectory optimization framework
considering full state dynamics for tracking and perching. The proposed method
is extensively tested through benchmark comparisons and ablation studies. To
facilitate the application of academic research to industry and to validate the
efficiency of our scheme under strictly limited computational resources, we
deploy our system on a commercial drone (DJI-MAVIC3) with a full-size
sport-utility vehicle (SUV). We conduct extensive real-world experiments, where
the drone successfully tracks and perches at 30~km/h (8.3~m/s) on the top of
the SUV, and at 3.5~m/s with 60{\deg} inclined into the trunk of the SUV
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
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