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

University Of Texas At Austin, Austin TX

Investigators

Abstract

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.