Identification and Avoidance of Static and Dynamic Obstacles on Point
Cloud for UAVs Navigation
- URL: http://arxiv.org/abs/2105.06622v1
- Date: Fri, 14 May 2021 02:44:18 GMT
- Title: Identification and Avoidance of Static and Dynamic Obstacles on Point
Cloud for UAVs Navigation
- Authors: Han Chen and Peng Lu
- Abstract summary: We introduce a technique to distinguish dynamic obstacles from static ones with only point cloud input.
A computationally efficient obstacle avoidance motion planning approach is proposed and it is in line with an improved relative velocity method.
The approach is able to avoid both static obstacles and dynamic ones in the same framework.
- Score: 7.14505983271756
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Avoiding hybrid obstacles in unknown scenarios with an efficient flight
strategy is a key challenge for unmanned aerial vehicle applications. In this
paper, we introduce a technique to distinguish dynamic obstacles from static
ones with only point cloud input. Then, a computationally efficient obstacle
avoidance motion planning approach is proposed and it is in line with an
improved relative velocity method. The approach is able to avoid both static
obstacles and dynamic ones in the same framework. For static and dynamic
obstacles, the collision check and motion constraints are different, and they
are integrated into one framework efficiently. In addition, we present several
techniques to improve the algorithm performance and deal with the time gap
between different submodules. The proposed approach is implemented to run
onboard in real-time and validated extensively in simulation and hardware
tests. Our average single step calculating time is less than 20 ms.
Related papers
- A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with
Dynamic Obstacle Trajectory Prediction and Its Application with LLMs [6.747468447244154]
This paper proposes a vision-based planning system that combines tracking and trajectory prediction of dynamic obstacles to achieve efficient and reliable autonomous flight.
We conduct experiments in both simulation and real-world environments, and the results indicate that our approach can successfully detect and avoid obstacles in dynamic environments in real-time.
arXiv Detail & Related papers (2023-11-21T08:09:00Z) - Exploring and Exploiting Decision Boundary Dynamics for Adversarial
Robustness [59.948529997062586]
It is unclear whether existing robust training methods effectively increase the margin for each vulnerable point during training.
We propose a continuous-time framework for quantifying the relative speed of the decision boundary with respect to each individual point.
We propose Dynamics-aware Robust Training (DyART), which encourages the decision boundary to engage in movement that prioritizes increasing smaller margins.
arXiv Detail & Related papers (2023-02-06T18:54:58Z) - Obstacle Identification and Ellipsoidal Decomposition for Fast Motion
Planning in Unknown Dynamic Environments [0.0]
Collision avoidance in unknown environments is one of the most critical challenges for unmanned systems.
We present a method that identifies obstacles in terms of ellipsoids to estimate linear and angular obstacle velocities.
arXiv Detail & Related papers (2022-09-28T17:00:10Z) - Vision-aided UAV navigation and dynamic obstacle avoidance using
gradient-based B-spline trajectory optimization [7.874708385247353]
This paper proposes a gradient-based B-spline trajectory optimization algorithm utilizing the robot's onboard vision.
The proposed optimization first adopts the circle-based guide-point algorithm to approximate the costs and gradients for avoiding static obstacles.
With the vision-detected moving objects, our receding-horizon distance field is simultaneously used to prevent dynamic collisions.
arXiv Detail & Related papers (2022-09-15T02:12:30Z) - Motion Planning and Control for Multi Vehicle Autonomous Racing at High
Speeds [100.61456258283245]
This paper presents a multi-layer motion planning and control architecture for autonomous racing.
The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
arXiv Detail & Related papers (2022-07-22T15:16:54Z) - StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception.
We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy.
Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
arXiv Detail & Related papers (2022-07-21T12:03:02Z) - Simultaneous Contact-Rich Grasping and Locomotion via Distributed
Optimization Enabling Free-Climbing for Multi-Limbed Robots [60.06216976204385]
We present an efficient motion planning framework for simultaneously solving locomotion, grasping, and contact problems.
We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45deg with a much shorter planning time.
arXiv Detail & Related papers (2022-07-04T13:52:10Z) - Learning Salient Boundary Feature for Anchor-free Temporal Action
Localization [81.55295042558409]
Temporal action localization is an important yet challenging task in video understanding.
We propose the first purely anchor-free temporal localization method.
Our model includes (i) an end-to-end trainable basic predictor, (ii) a saliency-based refinement module, and (iii) several consistency constraints.
arXiv Detail & Related papers (2021-03-24T12:28:32Z) - Multi-Agent Path Planning based on MPC and DDPG [14.793341914236166]
We propose a new algorithm combining Model Predictive Control (MPC) with Deep Deterministic Policy Gradient (DDPG)
The DDPG with continuous action space is designed to provide learning and autonomous decision-making capability for robots.
We employ Unity 3D to perform simulation experiments in highly uncertain environment such as aircraft carrier decks and squares.
arXiv Detail & Related papers (2021-02-26T02:57:13Z) - A Feasibility-Driven Approach to Control-Limited DDP [22.92789455838942]
We show that BOX-FDDP regulates the dynamic feasibility during the numerical optimization and ensures control limits.
We demonstrate the benefits of our approach by generating complex and athletic motions for quadruped and humanoid robots.
arXiv Detail & Related papers (2020-10-01T13:56:14Z) - Reinforcement Learning with Fast Stabilization in Linear Dynamical
Systems [91.43582419264763]
We study model-based reinforcement learning (RL) in unknown stabilizable linear dynamical systems.
We propose an algorithm that certifies fast stabilization of the underlying system by effectively exploring the environment.
We show that the proposed algorithm attains $tildemathcalO(sqrtT)$ regret after $T$ time steps of agent-environment interaction.
arXiv Detail & Related papers (2020-07-23T23:06:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.