NSF-BSF: RI: Small: Resource-Constrained Multi-hypothesis-aware Perception

自动驾驶汽车、服务机器人和家用机器人等移动机器人将在日常生活中为人们提供帮助。它们在受控的工厂环境之外运行，需要使用车载传感器（如摄像头）来感知周围的世界，进行自我定位、创建地图和执行任务。目前大多数感知系统估计世界最可能的状态，但在处理模糊性方面很差，因此很容易失败。在模棱两可的情况下，根据当前可用的传感器测量结果选择最可能的解决方案，即使这可能与现实不符。未来的测量可以消除这种情况的歧义，但如果之前已经丢弃了正确的解决方案，则无法恢复，机器人将无法完成任务。这个项目的重点是开发新的算法，可以处理歧义，例如，通过跟踪多个可能的解决方案。关键的挑战是可能的解决方案的数量可能会迅速增长，并且需要有效的解决方案，这些解决方案可以在移动机器人上有限的可用计算资源中实现。本研究中研究的新方法将通过逼近全套潜在假设来扩展当前最先进的鲁棒感知和信念空间规划技术，同时减少计算需求并提供性能的概率界限。我们将研究两种不同的近似方法：(1)通过将相似的假设分组到可以作为一个组有效地进行近似评估的集合中；(2)通过寻求一个概率接近原始未约简假设集的简化假设集。在被动情况下开发这些方法后，它们将扩展到主动感知情况，在这种情况下，将研究固有的权衡，以便在普遍存在感知混叠和模糊的环境中，在模拟和现实世界的实验中获得在线性能。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。

Mobile robots such as self-driving cars, service and household robots, will help people in their daily lives. They operate outside controlled factory environments where they need to perceive the world around them using onboard sensors such as cameras to self-localize, create maps, and perform tasks. Most current perception systems estimate the most likely state of the world but are poor at handling ambiguity and can therefore easily fail. In ambiguous situations, the most likely solution based on the currently available sensor measurements is selected, even though that might not be the one that corresponds to reality. Future measurements can disambiguate the situation, but if the correct solution has previously been discarded, it cannot be recovered, and the robot will fail in its task. This project focuses on developing novel algorithms that can deal with ambiguity for example by keeping track of multiple possible solutions. The key challenge is that the number of possible solutions can grow rapidly, and efficient solutions are needed that can be implemented with the restricted computational resources available onboard mobile robots.The novel methods to be investigated in this research will extend current state-of-the-art robust perception and belief space planning techniques by approximating the full set of potential hypotheses while simultaneously decreasing computational demands and providing probabilistic bounds on performance. Two different approximations will be investigated: (1) by grouping similar hypotheses into sets that can be approximately evaluated efficiently as a group and (2) by seeking to find a simplified set of hypotheses that is probabilistically close to the original unreduced set of hypotheses. After developing these methods in the passive case, they will be extended to the active perception situation where inherent tradeoffs will be investigated to attain online performance during both simulated and real-world experiments in environments where perceptual aliasing and ambiguity are prevalent.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.