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

这项研究工作的目标是促进最近的努力，旨在促进智能（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共享空间中共存并一起工作，包括密集的城市场所（例如，忙碌的十字路口）和大型工业设施的工厂楼层。为了实现机器人和人类的和谐共生，必须使两者都能有效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。这项研究工作的一个关键挑战是，当机器人在由其他机器人和人类居住的忙碌空间中导航时，它们必须在不确定的情况下实时做出决策。特别地，移动的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它们附近的其他移动的机器人或人类的下落的了解，而且还取决于后者的意图（即，他们计划移动的方向），这可能难以预测。这项研究将创建新的算法方法，使机器人能够同时1）实时推断附近人类和机器人最有可能的未来运动模式，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。这项研究工作预计将导致创建可扩展的算法，用于分散意图-多智能体环境中自主机器人的感知局部运动规划。所提出的方法明确占的流动性特征和形状的代理参与冲突事件，以及不确定性对他们的决策机制的影响，由于（1）传感/感知的代理限制，和（2）缺乏知识的代理感兴趣（自我代理）的意图，其附近的代理关于他们未来的运动。我们的方法的骨干之一是一个意图识别算法，旨在计算的概率分布的密度函数的近似值与参与冲突的情况下，每个代理的预计目标的目的地。所提出的算法依赖于一类非参数统计方法，称为核密度估计算法，其计算足迹和复杂性显着小于依赖于部分可观测马尔可夫决策处理（POMDPs）的解决方案的其他方法，这可能是一个非常复杂的任务。随后，我们构建椭圆管，包含一定的概率预期的轨迹，将转移所有代理附近的自我代理到他们的预计目标目的地。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，只有当代理未来轨迹的预测发生重大变化时，该问题的解才会更新。所提出的方法明智的特点，高风险的碰撞区域，从而使代理计划碰撞的轨迹，即使在密集拥挤的空间。这与基于可达性的方法相反，基于可达性的方法经常对避碰问题中的可行性问题给出假否定的答案，从而宣布后一个问题是不可行的，尽管存在可行的无碰撞轨迹。一系列相互交织的研究和教育活动，将促进本科生和代表性不足的学生在机器人技术的真实的世界问题的参与也proposed.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.