High-Dimensional Motion Planning and Learning Under Uncertain Conditions

Many existing path planning methods do not adequately account for uncertainty. Without uncertainty these existing techniques work well, but in real world environments they struggle due to inaccurate sensor models, arbitrarily moving obstacles, and uncertain action consequences. For example, picking up and storing childrens toys is a simple task for humans. Yet, for a robotic household robot the task can be daunting. The room must be modeled with sensors, which may or may not detect all the strewn toys. The robot must be able to detect and avoid the child who may be moving the very toys that the robot is tasked with cleaning. Finally, if the robot missteps and places a foot on a toy, it must be able to compensate for the unexpected consequences of its actions. This example demonstrates that even simple human tasks are fraught with uncertainties that must be accounted for in robotic path planning algorithms. This work presents the first steps towards migrating sampling-based path planning methods to real world environments by addressing three different types of uncertainty: (1) model uncertainty, (2) spatio-temporal obstacle uncertainty (moving obstacles) and (3) action consequence uncertainty. Uncertainty is encoded directly into path planning through a data structure in order to successfully and efficiently identify safe robot paths in sensed environments with noise. This encoding produces comparable clearance paths to other planning methods which are a known for high clearance, but at an order of magnitude less computational cost. It also shows that formal control theory methods combined with path planning provides a technique that has a 95% collision-free navigation rate with 300 moving obstacles. Finally, it demonstrates that reinforcement learning can be combined with planning data structures to autonomously learn motion controls of a seven degree of freedom robot despite a low computational cost despite the number of dimensions

PATH SMOOTHING STRATEGY BASED ON METAHEURISTIC ALGORITHMS FOR PROBABILISTIC FOAM

The probabilistic Foam method (PFM) is a sampling-basedpath planning algorithm that ensures a feasible path boundedby a safe region. This method is ideal for assistive roboticsapplications, which demands a high level of safety, such asperforming a motion by an active exoskeleton. However,PFM generates non-smoothed paths, which results in nonanthropomorphicmovements. Thus, this paper presentssome optimization strategies based on metaheuristics to smooththe paths generated by PFM. Simulated experiments wereperformed using the Harmony Search Algorithm, and GeneticAlgorithm and they were applied to an exoskeleton toovercome an obstacle. Results show that our proposed approachis capable of smoothing paths for this application,which resulted in more anthropomorphic motions

Generating Plausible Individual Agent Movements From Spatio-Temporal Occupancy Data

We introduce the Spatio-Temporal Agent Motion Model, a datadriven representation of the behavior and motion of individuals within a space over the course of a day. We explore different representations for this model, incorporating different modes of individual behavior, and describe how crowd simulations can use this model as source material for dynamic and realistic behaviors

Real-Time Long Range Trajectory Replanning for MAVs in the Presence of Dynamic Obstacles

Real-time long-range local planning is a challenging task, especially in the presence of dynamics obstacles. We propose a complete system which is capable of performing the local replanning in real-time. Desired trajectory is needed in the system initialization phase; system starts initializing sub-components of the system including point cloud processor, trajectory estimator and planner. Afterwards, the multi-rotary aerial vehicle starts moving on the given trajectory. When it detects obstacles, it replans the trajectory from the current pose to pre-defined distance incorporating the desired trajectory. Point cloud processor is employed to identify the closest obstacles around the vehicle. For replanning, Rapidly-exploring Random Trees (RRT*) is used with two modifications which allow planning the trajectory in milliseconds scales. Once we replanned the desired path, velocity components(x,y and z) and yaw rate are calculated. Those values are sent to the controller at a constant frequency to maneuver the vehicle autonomously. Finally, we have evaluated each of the components separately and tested the complete system in the simulated and real environments

Molecular Docking With Haptic Guidance and Path Planning

Molecular docking drives many important biological processes including immune system recognition and cellular signalling. Molecular docking occurs when molecules interact and form complexes. Predicting how specific molecules dock with each other using computational methods has several applications including understanding diseases and virtual drug design. The goal of molecular docking prediction is to find the lowest energy ligand states. The lower the energy state, the more probable the state is docked and biologically feasible. Existing automated computational methods can be time intensive, especially when using direct molecular dynamic simulation. One way to reduce this computational cost is to use more coarse-grained models that approximate molecular docking. Coarse-grained molecular docking prediction is generally performed first by sampling ligand states using a rigid body model or a partial flexibility model to reduce computation, then by screening the states. The ligand states are screened using a scoring function, usually a potential energy function for interactions between the atoms in each molecule. Ligand state search algorithms still have a significant computational cost if a large portion of the state space is to be explored. Instead of an automated ligand state search method, a human operator can explore the state space instead. Haptic force feedback devices providing guidance based off the energy function can aid the human operator. Haptic-guidance has been used for immersive semi-automatic and manual molecular docking on a single operator scale. A large amount of ligand state space can be explored with many human operators in a crowdsourced effort. Players in an interactive crowdsourced protein folding puzzle game have aided in finding protein folding prediction solutions, but without haptic feedback. Interactive crowdsourced methods for molecular docking prediction is not well-explored, although non-interactive crowdsourced systems such as Folding@home can be adapted for molecular docking. This thesis presents a molecular docking game that produces low potential energy ligand states and motion paths with crowdsource scale potential. In an exploratory user study, participants were assigned four different types of devices with varying levels of haptic guidance to search for a potentially docked ligand state. The results demonstrate some effect on the type of device and haptic guidance seen in the study. However, differences are minimal thus potentially enabling the use of commonly available input devices in a crowdsourced setting