An official website of the United States government
Safe quadrupedal navigation through unknown environments is a challenging problem. This paper proposes a hierarchical vision-based planning framework (GPF-BG) integrating our previous Global Path Follower (GPF) navigation system and a gap-based local planner using Bézier curves, so called B ézier Gap (BG). This BG-based trajectory synthesis can generate smooth trajectories and guarantee safety for point-mass robots. With a gap analysis extension based on non-point, rectangular geometry, safety is guaranteed for an idealized quadrupedal motion model and significantly improved for an actual quadrupedal robot model. Stabilized perception space improves performance under oscillatory internal body motions that impact sensing. Simulation-based and real experiments under different benchmarking configurations test safe navigation performance. GPF-BG has the best safety outcomes across all experiments.
more »
« less
AeriaLPiPS: A Local Planner for Aerial Vehicles with Geometric Collision Checking
Real-time navigation in non-trivial environments by micro aerial vehicles (MAVs) predominantly relies on modelling the MAV with idealized geometry, such as a sphere. Simplified, conservative representations increase the likelihood of a planner failing to identify valid paths. That likelihood increases the more a robot's geometry differs from the idealized version. Few current approaches consider these situations; we are unaware of any that do so using perception space representations. This work introduces the egocan, a perception space obstacle representation using line-of-sight free space estimates, and 3D Gap, a perception space approach to gap finding for identifying goal-directed, collision-free directions of travel through 3D space. Both are integrated, with real-time considerations in mind, to define a local planner module of a hierarchical navigation system. The result is Aerial Local Planning in Perception Space (AeriaLPiPS). AeriaLPiPS is shown to be capable of safely navigating a MAV with non-idealized geometry through various environments, including those impassable by traditional real-time approaches. The open source implementation of this work is available at github.com/ivaROS/AeriaLPiPS.
more »
« less
- Award ID(s):
- 1849333
- PAR ID:
- 10496678
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- International Conference on Robotics and Automation
- ISBN:
- 979-8-3503-2365-8
- Page Range / eLocation ID:
- 4092 to 4098
- Format(s):
- Medium: X
- Location:
- London, United Kingdom
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)The TEB hierarchical planner for real-time navigation through unknown environments is highly effective at balancing collision avoidance with goal directed motion. Designed over several years and publications, it implements a multi-trajectory optimization based synthesis method for identifying topologically distinct trajectory candidates through navigable space. Unfortunately, the underlying factor graph approach to the optimization problem induces a mismatch between grid-based representations and the optimization graph, which leads to several time and optimization inefficiencies. This paper explores the impact of using egocentric, perception space representations for the local planning map. Doing so alleviates many of the identified issues related to TEB and leads to a new method called egoTEB. Timing experiments and Monte Carlo evaluations in benchmark worlds quantify the benefits of egoTEB for navigation through uncertain environments.more » « less
-
null (Ed.)This paper addresses the problem of autonomously deploying an unmanned aerial vehicle in non-trivial settings, by leveraging a manipulator arm mounted on a ground robot, acting as a versatile mobile launch platform. As real-world deployment scenarios for micro aerial vehicles such as searchand- rescue operations often entail exploration and navigation of challenging environments including uneven terrain, cluttered spaces, or even constrained openings and passageways, an often arising problem is that of ensuring a safe take-off location, or safely fitting through narrow openings while in flight. By facilitating launching from the manipulator end-effector, a 6- DoF controllable take-off pose within the arm workspace can be achieved, which allows to properly position and orient the aerial vehicle to initialize the autonomous flight portion of a mission. To accomplish this, we propose a sampling-based planner that respects a) the kinematic constraints of the ground robot / manipulator / aerial robot combination, b) the geometry of the environment as autonomously mapped by the ground robots perception systems, and c) accounts for the aerial robot expected dynamic motion during takeoff. The goal of the proposed planner is to ensure autonomous collision-free initialization of an aerial robotic exploration mission, even within a cluttered constrained environment. At the same time, the ground robot with the mounted manipulator can be used to appropriately position the take-off workspace into areas of interest, effectively acting as a carrier launch platform. We experimentally demonstrate this novel robotic capability through a sequence of experiments that encompass a micro aerial vehicle platform carried and launched from a 6-DoF manipulator arm mounted on a four-wheel robot base.more » « less
-
A major challenge in deploying the smallest of Micro Aerial Vehicle (MAV) platforms (< 100 g) is their inability to carry sensors that provide high-resolution metric depth information (e.g., LiDAR or stereo cameras). Current systems rely on end-to-end learning or heuristic approaches that directly map images to control inputs, and struggle to fly fast in unknown environments. In this work, we ask the following question: using only a monocular camera, optical odometry, and offboard computation, can we create metrically accurate maps to leverage the powerful path planning and navigation approaches employed by larger state-of-the-art robotic systems to achieve robust autonomy in unknown environments? We present MonoNav: a fast 3D reconstruction and navigation stack for MAVs that leverages recent advances in depth prediction neural networks to enable metrically accurate 3D scene reconstruction from a stream of monocular images and poses. MonoNav uses off-the-shelf pre-trained monocular depth estimation and fusion techniques to construct a map, then searches over motion primitives to plan a collision-free trajectory to the goal. In extensive hardware experiments, we demonstrate how MonoNav enables the Crazyflie (a 37 g MAV) to navigate fast (0.5 m/s) in cluttered indoor environments. We evaluate MonoNav against a state-of-the-art end-to-end approach, and find that the collision rate in navigation is significantly reduced (by a factor of 4). This increased safety comes at the cost of conservatism in terms of a 22% reduction in goal completion.more » « less
-
This paper describes a hierarchical solution consisting of a multi-phase planner and a low-level safe controller to jointly solve the safe navigation problem in crowded, dynamic, and uncertain environments. The planner employs dynamic gap analysis and trajectory optimization to achieve collision avoidance with respect to the predicted trajectories of dynamic agents within the sensing and planning horizon and with robustness to agent uncertainty. To address uncertainty over the planning horizon and real-time safety, a fast reactive safe set algorithm (SSA) is adopted, which monitors and modifies the unsafe control during trajectory tracking. Compared to other existing methods, our approach offers theoretical guarantees of safety and achieves collision-free navigation with higher probability in uncertain environments, as demonstrated in scenarios with 20 and 50 dynamic agents.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA48891.2023.10160852
