权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

NRI: FND: Efficient algorithms for safety guiding mobile robots through spaces populated by humans and mobile intelligent machines and robots

NRI：FND：用于安全引导移动机器人穿过人类和移动智能机器和机器人居住的空间的高效算法

基本信息

批准号：
1924790
负责人：
Efstathios Bakolas
金额：
$ 25万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924790&HistoricalAwards=false
关键词：
NRI FND Efficient algorithms safety

项目摘要

The goal of this research effort is to contribute to recent efforts aimed at promoting the safe and smooth integration of intelligent (autonomous or semi-autonomous) machines and robots in different aspects of our everyday life. Humans and different types of robots will have to co-exist and work together in shared spaces including dense urban places (e.g., busy crossroads) and the factory floors of big industrial facilities. For the harmonious symbiosis of robots and humans, it is necessary that they can both effectively avoid conflicts and physical collisions that may cause serious damage, significant economic losses, injuries, or even loss of life. One of the key challenges of this research effort has to do with the fact that robots will have to make decisions in real-time under uncertainty when they are navigating through busy spaces populated by other robots and humans. In particular, the ability of mobile robots to safely navigate in densely populated spaces hinges upon their knowledge of not only the whereabouts of the other mobile robots or humans in their vicinity but also the intentions of the latter (i.e., which directions they plan to move) which may be difficult to predict. The proposed research will create new algorithmic methods that will allow robots to simultaneously 1) infer the most likely future motion patterns of nearby humans and robots in real-time and 2) safely guide them to their destination while avoiding collisions with nearby robots and humans.This research effort is expected to lead to the creation of scalable algorithms for decentralized intention-aware local motion planning for autonomous robots in multi-agent environments. The proposed approach explicitly accounts for the mobility characteristics and the shape of the agents involved in a conflict event as well as the effects of uncertainty on their decision making mechanisms due to (1) sensing / perception limitations of the agents, and (2) lack of knowledge by the agent of interest (ego-agent) of the intentions of its nearby agents regarding their future motion. One of the backbones of our approach is an intention identification algorithm that seeks to compute an approximation of the density function of the probability distribution associated with the projected goal destination of each agent involved in a conflict situation. The proposed algorithm relies on a class of non-parametric statistical methods known as kernel density estimation algorithms whose computational footprint and complexity are significantly smaller than those of other approaches that rely on the solution of partially observable Markov decision processed (POMDPs), which can be a very complex task. Subsequently, we construct ellipsoidal tubes that contain with a certain probability the anticipated trajectories that will transfer all the agents near the ego-agent to their projected goal destinations. Next, we reduce the local motion planning problem to a low-dimensional convex optimization problem whose solution will be updated only when significant changes in the predictions of the agents future trajectories have taken place. The proposed approach judiciously characterizes the areas of high risk for collisions, thus allowing the agents to plan collision-free trajectories even in densely crowded spaces. This is in contrast with reachability-based approaches which often give false negative answers to the question of feasibility in a collision avoidance problem, thus declaring the latter problem to be infeasible despite the existence of feasible collision-free trajectories. An array of interweaved research and educational activities that will promote the participation of undergraduate and underrepresented students in real world problems of robotics are also proposed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究工作的目标是促进智能（自主或半自主）机器和机器人在我们日常生活的不同方面的安全和顺利集成。人类和不同类型的机器人将不得不在包括人口密集的城市场所（如繁忙的十字路口）和大型工业设施的工厂车间在内的共享空间中共存和共同工作。为了使机器人与人类和谐共生，它们必须能够有效地避免可能造成严重损害、重大经济损失、伤害甚至生命损失的冲突和物理碰撞。这项研究工作的关键挑战之一是，当机器人在由其他机器人和人类组成的繁忙空间中导航时，它们必须在不确定的情况下实时做出决定。特别是，移动机器人在人口密集的空间中安全导航的能力不仅取决于它们对附近其他移动机器人或人类的下落的了解，还取决于后者的意图（即，它们计划移动的方向），这可能很难预测。拟议的研究将创造新的算法方法，使机器人能够同时1)实时推断附近人类和机器人未来最有可能的运动模式，2)安全地引导它们到达目的地，同时避免与附近的机器人和人类发生碰撞。这项研究工作有望为多智能体环境中自主机器人的分散意图感知局部运动规划创建可扩展算法。所提出的方法明确地解释了冲突事件中涉及的智能体的移动性特征和形状，以及由于(1)智能体的感知/感知限制以及(2)利益智能体（自我智能体）对其附近智能体关于其未来运动的意图缺乏知识而导致的不确定性对其决策机制的影响。我们方法的主干之一是意图识别算法，该算法试图计算与冲突情况中涉及的每个代理的预计目标目的地相关的概率分布的密度函数的近似值。所提出的算法依赖于一类被称为核密度估计算法的非参数统计方法，其计算足迹和复杂性明显小于其他依赖于部分可观察马尔可夫决策处理（pomdp）的解的方法，这可能是一个非常复杂的任务。随后，我们构建了椭球管，其中包含一定概率的预期轨迹，这些轨迹将把自我智能体附近的所有智能体转移到它们预计的目标目的地。接下来，我们将局部运动规划问题简化为一个低维凸优化问题，只有当智能体未来轨迹的预测发生重大变化时，该问题的解才会更新。提出的方法明智地描述了碰撞高风险区域的特征，从而允许智能体即使在密集拥挤的空间中也能规划无碰撞的轨迹。这与基于可达性的方法形成对比，后者经常对避碰问题的可行性问题给出假否定的答案，从而宣布后者的问题是不可行的，尽管存在可行的无碰撞轨迹。提出了一系列相互交织的研究和教育活动，以促进本科生和代表性不足的学生参与现实世界的机器人问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。