Fraichard, "Safe motion planning in dynamic en- vironments," Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2005.Petti, S. & Fraichard, T. (2005). Safe motion planning in dynamic environments. IEEE IROS....
Planning on a (Risk) Budget: Safe Non-Conservative Planning in Probabilistic Dynamic Environments,Hung-Jui Huang1, Kai-Chi Huang1, Michal ˇC ́ap1, Yibiao Zhao1, Ying Nian Wu2, Chris L. Baker1。和 A Non-Conservatively Defensive Strategy for Urban Autonomous Driving一样,来自isee.ai,思路也...
This paper presents an autonomous motion planning algorithm for unmanned surface vehicles (USVs) to navigate safely in dynamic, cluttered environments. The algorithm not only addresses hazard avoidance (HA) for stationary and moving hazards, but also applies the International Regulations for Preventing ...
In this paper we present the first safe system for full control of self-driving vehicles trained from human demonstrations and deployed in challenging, real-world, urban environments. 端到端的方法不能提供安全保障。 To combat this, our approach uses a simple yet effective rule-based fallback laye...
Repository associated with paper titled "AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environments", presented at IEEE ICRA 2023. - utiasDSL/AMSwarm
1. Environments Supported 1.1. Safe Single Agent RL benchmarks AI Safety Gridworlds Safety-Gym Safety-Gymnasium 1.2. Safe Multi-Agent RL benchmarks Safe Multi-Agent Mujoco Safe Multi-Agent Isaac Gym Safe Multi-Agent Robosuite 2. Safe RL Baselines ...
2019 Structural Integrity Evaluation of Medical Devices Exposed to a Dynamic Environment Collier, Michael Email Request 2019 Implementation of Dynamic Flight Simulation Centrifuge Training in the Republic of Singapore Air Force Soh, MAJ Feng Wei Email Request 2019 Designing and Implementing Intuitive VR...
Deep reinforcement learning (RL) has shown promising results in the motion planning of manipulators. However, no method guarantees the safety of highly dynamic obstacles, such as humans, in RL-based manipulator control. This lack of formal safety assurances prevents the application of RL for manipul...
Robotic Motion Planning in Dynamic, Cluttered, Uncertain Environments Joint Multi-Policy Behavior Estimation and Receding-Horizon Trajectory Planning for Auto Urban Driv 不多写公式了,就只记录不确定性部分。 这里处理不确定性的方式是用一个椭圆包裹障碍物,随着障碍物的预测时间的增长,这个椭球将被越拉越长。
可以看到这里面有三部分组成,第一个是smooth项(比如我们选择minimum snap),第二部分是与障碍物距离,第三部分是dynamic constriants. 第一部分:在这里,作者采用了ref[9]的方案(论文推土机:Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments)将优化目标从多项式曲线的参数转变成...