Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an obstacle's space: a polyhedron, such as a cuboid, or a nonlinear differentiable surface, such as an ellipsoid. The former approach relies on disjunctive programming, which has a relatively high computational cost that grows exponentially with the number of obstacles. The latter approach needs to be linearized locally to find a tractable evaluation of the chance constraints, which dramatically reduces the remaining free space and leads to over-conservative trajectories or even unfeasibility. In this work, we present a hybrid approach that eludes the pitfalls of both strategies while maintaining the original safety guarantees. The key idea consists in obtaining a safe differentiable approximation for the disjunctive chance constraints bounding the obstacles. The resulting nonlinear optimization problem is free of chance constraint linearization and disjunctive programming, and therefore, it can be efficiently solved to meet fast real-time requirements with multiple obstacles. We validate our approach through mathematical proof, simulation and real experiments with an aerial robot using nonlinear model predictive control to avoid pedestrians.
Autonomous navigation in unknown environments populated by humans and other robots is one of the main challenges when working with mobile robots. In this paper, we present a new approach to dynamic collision avoidance for multi-rotor unmanned aerial vehicles (UAVs). A new nonlinear model predictive control (NMPC) approach is proposed to safely navigate in a workspace populated by static and/or moving obstacles. The uniqueness of our approach lies in its ability to anticipate the dynamics of multiple obstacles, avoiding them in real-time. Exploiting active set algorithms, only the obstacles that affect to the UAV during the prediction horizon are considered at each sample time. We also improve the fluency of avoidance maneuvers by reformulating the obstacles as orientable ellipsoids, being less prone to local minima and allowing the definition of a preferred avoidance direction. Finally, we present two real-time implementations based on simulation. The former demonstrates that our approach outperforms its analog static formulation in terms of safety and efficiency. The latter shows its capability to avoid multiple dynamic obstacles.
In this work, we present a semantic situation awareness system for multirotor aerial robots equipped with a 2D LIDAR sensor, focusing on the understanding of the environment, provided to have a drift-free precise localization of the robot (e.g. given by GNSS/INS or motion capture system). Our algorithm generates in real-time a semantic map of the objects of the environment as a list of ellipses represented by their radii, and their pose and velocity, both in world coordinates. Two different Convolutional Neural Network (CNN) architectures are proposed and trained using an artificially generated dataset and a custom loss function, to detect ellipses in a segmented (i.e. with one single object) LIDAR measurement. In cascade, a specifically designed indirect-EKF estimates the ellipses based semantic map in world coordinates, as well as their velocity. We have quantitative and qualitatively evaluated the performance of our proposed situation awareness system. Two sets of Software-In-The-Loop simulations using CoppeliaSim with one and multiple static and moving cylindrical objects are used to evaluate the accuracy and performance of our algorithm. In addition, we have demonstrated the robustness of our proposed algorithm when handling real environments thanks to real laboratory experiments with non-cylindrical static (i.e. a barrel) objects and moving persons.
In this work, we present an optimizationbased trajectory tracking solution for multirotor aerial robots given a geometrically feasible path.A trajectory planner generates a minimum-time kinematically and dynamically feasible trajectory that includes not only standard restrictions such as continuity and limits on the trajectory, constraints in the waypoints, and maximum distance between the planned trajectory and the given path, but also restrictions in the actuators of the aerial robot based on its dynamic model, guaranteeing that the planned trajectory is achievable. Our novel compact multi-phase trajectory definition, as a set of two different kinds of polynomials, provides a higher semantic encoding of the trajectory, which allows calculating an optimal solution but following a predefined simple profile.
Abstract. Flying autonomously in a workspace populated by obstacles is one of the main goals when working with Unmanned Aerial Vehicles (UAV). To address this challenge, this paper presents a model predictive flight controller that drives the UAV through collision-free trajectories to reach a given pose or follow a waypoint path. The major advantage of this approach lies on the inclusion of threedimensional obstacle avoidance in the control layer by adding ellipsoidal constraints to the optimal control problem. The obstacles can be added, moved and resized online, providing a way to perform waypoint navigation without the need of motion planning. In addition, the delays of the system are cosidered in the prediction by an experimental first order with delay model of the system. Successful experiments in 3D path tracking and obstacle avoidance validates its effectiveness for sense-and-avoid and surveillance applications presenting the proper structure to extent its autonomy and applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.