Cooperative Prediction and Planning Under Uncertainty for Autonomous Robots
| dc.contributor.author | Nayak, Anshul Abhijit | en |
| dc.contributor.committeechair | Eskandarian, Azim | en |
| dc.contributor.committeemember | Doan, Thinh Thanh | en |
| dc.contributor.committeemember | Acar, Pinar | en |
| dc.contributor.committeemember | Losey, Dylan Patrick | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2024-10-12T08:00:20Z | en |
| dc.date.available | 2024-10-12T08:00:20Z | en |
| dc.date.issued | 2024-10-11 | en |
| dc.description.abstract | Autonomous robots are set to become ubiquitous in the future, with applications ranging from autonomous cars to assistive household robots. These systems must operate in close proximity of dynamic and static objects, including humans and other non-autonomous systems, adding complexity to their decision-making processes. The behaviour of such objects is often stochastic and hard to predict. Making robust decisions under such uncertain scenarios can be challenging for these autonomous robots. In the past, researchers have used deterministic approach to predict the motion of surrounding objects. However, these approaches can be over-confident and do not capture the stochastic behaviour of surrounding objects necessary for safe decision-making. In this dissertation, we show the importance of probabilistic prediction of surrounding dynamic objects and their incorporation into planning for safety-critical decision making. We utilise Bayesian inference models such as Monte Carlo dropout and deep ensemble to probabilistically predict the motion of surrounding objects. Our probabilistic trajectory forecasting model showed improvement over standard deterministic approaches and could handle adverse scenarios such as sensor noise and occlusion during prediction. The uncertainty-inclusive prediction of surrounding objects has been incorporated into planning. The inclusion of predicted states of surrounding objects with associated uncertainty enables the robot make proactive decisions while avoiding collisions. | en |
| dc.description.abstractgeneral | In future, humans will greatly rely on the assistance of autonomous robots in helping them with everyday tasks. Drones to deliver packages, cars for driving to places autonomously and household robots helping with day-to-day activities. In all such scenarios, the robot might have to interact with their surrounding, in particular humans. Robots working in close proximity to humans must be intelligent enough to make safe decisions not affecting or intruding the human. Humans, in particular make abrupt decisions and their motion can be unpredictable. It is necessary for the robot to understand the intention of human for navigating safely without affecting the human. Therefore, the robot must capture the uncertain human behaviour and predict its future motion so that it can make proactive decisions. We propose to capture the stochastic behaviour of humans using deep learning based prediction models by learning motion patterns from real human trajectories. Our method not only predicts future trajectory of humans but also captures the associated uncertainty during prediction. In this thesis, we also propose how to predict human motion under adverse scenarios like bad weather leading to noisy sensing as well as under occlusion. Further, we integrate the predicted stochastic behaviour of surrounding humans into the planning of the robot for safe navigation among humans. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:41652 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/121330 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution-NonCommercial 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | en |
| dc.subject | Trajectory Prediction | en |
| dc.subject | Planning | en |
| dc.subject | Uncertainty Quantification | en |
| dc.subject | Cooperative Perception | en |
| dc.title | Cooperative Prediction and Planning Under Uncertainty for Autonomous Robots | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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