Browsing by Subject "Motion planning"

In surgical robotics, fully-automated tumor removal is an important topic and it includes three main tasks: tissue classification for cancer diagnosis, pathological mapping for tumor localization and tissue resection by using a laser scalpel. Generating a three-dimensional (3D) pathological tissue model with fully non-contact sensors can provide invaluable information to assist surgeons in decision-making and enable the use of surgical robots for efficient tissue manipulation. To collect the comprehensive information of a biological tissue target, robotic laser systems with complementary sensors (e.g., Optical coherence tomography (OCT) sensor, and stereovision) can play important roles in providing non-contact laser scalpels (i.e., cutting laser scalpel) for tissue removal, applying photonics-based sensors for pathological tissue classification (i.e., laser-based endogenous fluorescence), and aligning multi-sensing information to generate a 3D pathological map. However, there are three main challenges with integrating multiple laser-based sensors into the robotic laser system, which includes: 1) Modelling the laser beam transmission in 3D free-space to achieve accurate laser-tissue manipulation under geometric constraints, 2) Studying the complex physics of laser-tissue interaction for tissue differentiation and 3D shape modelling to ensure safe tissue removal, and 3) Integrating information from multiple sensing devices under sensor noise and uncertainties from system calibration.

Targeting these three research problems separately, a computational framework is proposed to provide kinematics and calibration algorithms to control and direct the 3D laser beam through a system with multiple rotary mirrors (to transmit laser beam in free-space) and laser-based sensor inputs. This framework can serve as a base platform for optics-based robotic system designs and solving the motion planning problems related to laser-based robot systems. Simulation experiments have verified the feasibility of the proposed framework and actual experiments have been conducted with an existing robotic laser system on phantom and ex-vivo biological tissues.

To study the complex physics of laser-tissue interaction, a 3D data-driven method is developed to model the geometric relation between the laser energy distribution, laser incident angles, and the tissue deformation resulting from photoablation. The results of the phantom studies have demonstrated the feasibility of applying the trained model for laser crater shape predictions during the surgical planning.

Finally, a research platform, referred as ``TumorMapping", is developed to collect multimodal sensing information from complementary sensors to build a 3D pathological map of a mice tumor surface. This robot system includes a sensor module attached to a 6-DOF robot arm end-effector, based on the laser-induced fluorescence spectroscopy for tissue classification and a fiber couple cutting laser for tissue resection. A benchtop sensor platform is built with an OCT sensor and a stereovision system with two lens camera to collect the tissue information with a non-contact pattern. The robot-sensor and the complementary sensor sub-systems are integrated in a unified platform for the 3D pathological map reconstruction.

In summary, the research contributions include important advancements in laser-based sensor fusion for surgical decision-making which is enabling new capabilities for the use of 3D pathological mapping combined with intelligent robot planning and control algorithms for robotic surgery.

This thesis develops a novel solution method for the problem of collision-free, optimal control of a robotic vehicle in an obstacle populated environment. The technique presented combines the well established approximate cell decomposition methodology with disjunctive programming in order to address both geometric and kinematic trajectory concerns. In this work, an algorithm for determining the shortest distance, collision-free path of a robot with unicycle kinematics is developed. In addition, the research defines a technique to discretize nonholonomic vehicle kinematics into a set of mixed integer linear constraints. Results obtained using the Tomlab/CPLEX mixed integer quadratic programming software exhibit that the method developed provides a powerful initial step in reconciling geometric path planning methods with optimal control techniques.

Robotics has the potential to dramatically change society over the next decade. Technology has matured such that modern robots can execute complex motions with sub-millimeter precision. Advances in sensing technology have driven down the price of depth cameras and increased their performance. However, the planning algorithms used in currently-deployed systems are too slow to react to changing environments; this has restricted the use of high degree-of-freedom (DOF) robots to tightly-controlled environments where planning in real time is not necessary.

Our work focuses on overcoming this challenge through careful hardware/software co-design. We leverage aggressive precomputation and parallelism to design accelerators for several components of the motion planning problem. We present architectures for accelerating collision detection as well as path search. We show how we can maintain flexibility even with custom hardware, and describe microarchitectures that we have implemented at the register-transfer level. We also show how to generate effective planning roadmaps for use with our designs.

Our accelerators bring the total planning latency to less than 3 microseconds, several orders of magnitude faster than the state of the art. This capability makes it possible to deploy systems that plan under uncertainty, use complex decision making algorithms, or plan for multiple robots in a workspace. We hope this technology will push robotics into domains and applications that were previously infeasible.

Sensor path planning and control refer to the problems of determining the trajectory and feedback control law that best support sensing objectives, such as monitoring, detection, classification, and tracking. Many autonomous systems developed, for example, to conduct environmental monitoring, search-and-rescue operations, demining, or surveillance, consist of a mobile vehicle instrumented with a suite of proprioceptive and exteroceptive sensors characterized by a bounded field-of-view (FOV) and a performance that is highly dependent on target and environmental conditions and, thus, on the vehicle position and orientation relative to the target and the environment. As a result, the sensor performance can be significantly improved by planning the vehicle motion and attitude in concert with the measurement sequence. This dissertation develops a general and systematic approach for deriving information-driven path planning and control methods that maximize the expected utility of the sensor measurements subject to the vehicle kinodynamic constraints.

The approach is used to develop three path planning and control methods: the information potential method (IP) for integrated path planning and control, the optimized coverage planning based on the Dirichlet process-Gaussian process (DP-GP) expected Kullback-Leibler (KL) divergence, and the optimized visibility planning for simultaneous target tracking and localization. The IP method is demonstrated on a benchmark problem, referred to as treasure hunt, in which an active vision sensor is mounted on a mobile unicycle platform and is deployed to classify stationary targets characterized by discrete random variables, in an obstacle-populated environment. In the IP method, an artificial potential function is generated from the expected conditional mutual information of the targets and is used to design a closed-loop switched controller. The information potential is also used to construct an information roadmap for escaping local minima. Theoretical analysis shows that the closed-loop robotic system is asymptotically stable and that an escaping path can be found when the robotic sensor is trapped in a local minimum. Numerical simulation results show that this method outperforms rapidly-exploring random trees and classical potential methods. The optimized coverage planning method maximizes the DP-GP expected KL divergence approximated by Monte Carlo integration in order to optimize the information value of a vision sensor deployed to track and model multiple moving targets. The variance of the KL approximation error is proven to decrease linearly with the inverse of the number of samples. This approach is demonstrated through a camera-intruder problem, in which the camera pan, tilt, and zoom variables are controlled to model multiple moving targets with unknown kinematics by nonparametric DP-GP mixture models. Numerical simulations as well as physical experiments show that the optimized coverage planning approach outperforms other applicable algorithms, such as methods based on mutual information, rule-based systems, and randomized planning. The third approach developed in this dissertation, referred to as optimized visibility motion planning, uses the output of an extended Kalman filter (EKF) algorithm to optimize the simultaneous tracking and localization performance of a robot equipped with proprioceptive and exteroceptive sensors, that is deployed to track a moving target in a global positioning system (GPS) denied environment.

Because active sensors with multiple modes can be modeled as a switched hierarchical system, the sensor path planning problem can be viewed as a hybrid optimal control problem involving both discrete and continuous state and control variables. For example, several authors have shown that a sensor with multiple modalities is a switched hybrid system that can be modeled by a hierarchical control architecture with components of mission planning, trajectory planning, and robot control. Then, the sensor performance can be represented by two Lagrangian functions, one function of the discrete state and control variables, and one function of the continuous state and control variables. Because information value functions are typically nonlinear, this dissertation also presents an adaptive dynamic programming approach for the model-free control of nonlinear switched systems (hybrid ADP), which is capable of learning the optimal continuous and discrete controllers online. The hybrid ADP approach is based on new recursive relationships derived in this dissertation and is proven to converge to the solution of the hybrid optimal control problem. Simulation results show that the hybrid ADP approach is capable of converging to the optimal controllers by minimizing the cost-to-go online based on a fully observable state vector.

Humanoid robots are created to look like humans, behave like humans, and ultimately reason in the same level as humans. Carrying on the hopes for emulating human beings' capabilities of performing dexterously and reliably in real world scenarios, humanoids are expected to undertake a wide variety of tasks including taking care of the elderly, doing house cleaning and disinfecting (especially important during COVID-19), and conducting operations in dangerous situations such as search and rescue during fire or earthquakes. The realization of these expectations of humanoid platforms needs continuous coordination of their upper and lower limbs in challenging environments and legged robots' inherent terrain adaptability makes them competent to provide assistance in these hazardous conditions. Though being considered as attractive candidates, humanoid robots suffer from a great risk of falling resulting from a relatively high center of mass position and a limited area of region of support. This prone-to-falling characteristic of humanoids’ bipedal walking makes them much harder to control, and falls can cause costly failures. As a result, the ability to regain balance from falling is a prerequisite before humanoids can be confidently applied to execute significant tasks. Despite the rise of relevant research on humanoid fall recovery in recent decades, humanoid's self-balancing in response to unexpected disturbances in arbitrary environment remains to be a difficult problem due to humanoid's high degrees of freedom, complicated nonlinear system dynamics, and a ``real-time" computational requirement owing to falling.

This dissertation focuses humanoid fall recovery with optimization-based motion planning approach. To advance state-of-the-art recovery strategies which mainly focus on open environment, I introduce motion planning algorithms which generalize fall recovery to both open and cluttered environments. I demonstrate two main contributions in this dissertation:1. The development and implementation of an efficient motion planner which enables humanoid to recover from falling by making hand contact with walls or other surfaces in the cluttered environment. This approach extends humanoid's balancing capability to cluttered environment with making hand contact and this ability to make use of environmental object for fall prevention improves humanoids' efficiency and reliability. 2. The proposal and development of a multi-contact motion planner which generalizes humanoid fall recovery in both open and cluttered environment. This algorithm unifies existing recovery strategies, such as inertial shaping, protective stepping, and hand contact, and automatically plans one strategy or a combination of strategies to regain robot's balance based on its disturbed state and nearby environment features. By enabling humanoid to reason how to regain balance on its own, this algorithm makes a significant contribution to the improvement of humanoid's sustainability in arbitrary environment.

Overall, these contributions advance state-of-the-art humanoid technologies with the ability to 1). use hand contact for fall prevention in cluttered environment and 2). reason how to regain balance in both open and cluttered environments. By further enhancing legged machines' capability of self-balancing, methods discussed in this dissertation have the potential to realize a more effective and more reliable humanoid performance in real world.