Topological Mapping and Navigation in Real-World Environments

We introduce the Hierarchical Hybrid Spatial Semantic Hierarchy (H2SSH), a hybrid topological-metric map representation. The H2SSH provides a more scalable representation of both small and large structures in the world than existing topological map representations, providing natural descriptions of a hallway lined with offices as well as a cluster of buildings on a college campus. By considering the affordances in the environment, we identify a division of space into three distinct classes: path segments afford travel between places at their ends, decision points present a choice amongst incident path segments, and destinations typically exist at the start and end of routes. Constructing an H2SSH map of the environment requires understanding both its local and global structure. We present a place detection and classification algorithm to create a semantic map representation that parses the free space in the local environment into a set of discrete areas representing features like corridors, intersections, and offices. Using these areas, we introduce a new probabilistic topological simultaneous localization and mapping algorithm based on lazy evaluation to estimate a probability distribution over possible topological maps of the global environment. After construction, an H2SSH map provides the necessary representations for navigation through large-scale environments. The local semantic map provides a high-fidelity metric map suitable for motion planning in dynamic environments, while the global topological map is a graph-like map that allows for route planning using simple graph search algorithms. For navigation, we have integrated the H2SSH with Model Predictive Equilibrium Point Control (MPEPC) to provide safe and efficient motion planning for our robotic wheelchair, Vulcan. However, navigation in human environments entails more than safety and efficiency, as human behavior is further influenced by complex cultural and social norms. We show how social norms for moving along corridors and through intersections can be learned by observing how pedestrians around the robot behave. We then integrate these learned norms with MPEPC to create a socially-aware navigation algorithm, SA-MPEPC. Through real-world experiments, we show how SA-MPEPC improves not only Vulcan’s adherence to social norms, but the adherence of pedestrians interacting with Vulcan as well.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144014/1/collinej_1.pd

A Survey on Human-aware Robot Navigation

Intelligent systems are increasingly part of our everyday lives and have been integrated seamlessly to the point where it is difficult to imagine a world without them. Physical manifestations of those systems on the other hand, in the form of embodied agents or robots, have so far been used only for specific applications and are often limited to functional roles (e.g. in the industry, entertainment and military fields). Given the current growth and innovation in the research communities concerned with the topics of robot navigation, human-robot-interaction and human activity recognition, it seems like this might soon change. Robots are increasingly easy to obtain and use and the acceptance of them in general is growing. However, the design of a socially compliant robot that can function as a companion needs to take various areas of research into account. This paper is concerned with the navigation aspect of a socially-compliant robot and provides a survey of existing solutions for the relevant areas of research as well as an outlook on possible future directions.Comment: Robotics and Autonomous Systems, 202

Multi-Policy Decision Making for Reliable Navigation in Dynamic Uncertain Environments

Navigating everyday social environments, in the presence of pedestrians and other dynamic obstacles remains one of the key challenges preventing mobile robots from leaving carefully designed spaces and entering our daily lives. The complex and tightly-coupled interactions between these agents make the environment dynamic and unpredictable, posing a formidable problem for robot motion planning. Trajectory planning methods, supported by models of typical human behavior and personal space, often produce reasonable behavior. However, they do not account for the future closed-loop interactions of other agents with the trajectory being constructed. As a consequence, the trajectories are unable to anticipate cooperative interactions (such as a human yielding), or adverse interactions (such as the robot blocking the way). Ideally, the robot must account for coupled agent-agent interactions while reasoning about possible future outcomes, and then take actions to advance towards its navigational goal without inconveniencing nearby pedestrians. Multi-Policy Decision Making (MPDM) is a novel framework for autonomous navigation in dynamic, uncertain environments where the robot's trajectory is not explicitly planned, but instead, the robot dynamically switches between a set of candidate closed-loop policies, allowing it to adapt to different situations encountered in such environments. The candidate policies are evaluated based on short-term (five-second) forward simulations of samples drawn from the estimated distribution of the agents' current states. These forward simulations and thereby the cost function, capture agent-agent interactions as well as agent-robot interactions which depend on the ego-policy being evaluated. In this thesis, we propose MPDM as a new method for navigation amongst pedestrians by dynamically switching from amongst a library of closed-loop policies. Due to real-time constraints, the robot's emergent behavior is directly affected by the quality of policy evaluation. Approximating how good a policy is based on only a few forward roll-outs is difficult, especially with the large space of possible pedestrian configurations and the sensitivity of the forward simulation to the sampled configurations. Traditional methods based on Monte-Carlo sampling often missed likely, high-cost outcomes, resulting in an over-optimistic evaluation of a policy and unreliable emergent behavior. By re-formulating policy evaluation as an optimization problem and enabling the quick discovery of potentially dangerous outcomes, we make MPDM more reliable and risk-aware. Even with the increased reliability, a major limitation is that MPDM requires the system designer to provide a set of carefully hand-crafted policies as it can evaluate only a few policies reliably in real-time. We radically enhance the expressivity of MPDM by allowing policies to have continuous-valued parameters, while simultaneously satisfying real-time constraints by quickly discovering promising policy parameters through a novel iterative gradient-based algorithm. Overall, we reformulate the traditional motion planning problem and paint it in a very different light --- as a bilevel optimization problem where the robot repeatedly discovers likely high-cost outcomes and adapts its policy parameters avoid these outcomes. We demonstrate significant performance benefits through extensive experiments in simulation as well as on a physical robot platform operating in a semi-crowded environment.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150017/1/dhanvinm_1.pd

Integrating Perception, Prediction and Control for Adaptive Mobile Navigation

Mobile robots capable of navigating seamlessly and safely in pedestrian rich environments promise to bring robotic assistance closer to our daily lives. A key limitation of existing navigation policies is the difficulty to predict and reason about the environment including static obstacles and pedestrians. In this thesis, I explore three properties of navigation including prediction of occupied spaces, prediction of pedestrians and measurements of uncertainty to improve crowd-based navigation. The hypothesis is that improving prediction and uncertainty estimation will increase robot navigation performance resulting in fewer collisions, faster speeds and lead to more socially-compliant motion in crowds. Specifically, this thesis focuses on techniques that allow mobile robots to predict occupied spaces that extend beyond the line of sight of the sensor. This is accomplished through the development of novel generative neural network architectures that enable map prediction that exceed the limitations of the sensor. Further, I extend the neural network architectures to predict multiple hypotheses and use the variance of the hypotheses as a measure of uncertainty to formulate an information-theoretic map exploration strategy. Finally, control algorithms that leverage the predicted occupancy map were developed to demonstrate more robust, high-speed navigation on a physical small form factor autonomous car. I further extend the prediction and uncertainty approaches to include modeling pedestrian motion for dynamic crowd navigation. This includes developing novel techniques that model human intent to predict future motion of pedestrians. I show this approach improves state-of-the-art results in pedestrian prediction. I then show errors in prediction can be used as a measure of uncertainty to adapt the risk sensitivity of the robot controller in real time. Finally, I show that the crowd navigation algorithm extends to socially compliant behavior in groups of pedestrians. This research demonstrates that combining obstacle and pedestrian prediction with uncertainty estimation achieves more robust navigation policies. This approach results in improved map exploration efficiency, faster robot motion, fewer number of collisions and more socially compliant robot motion within crowds