1,703 research outputs found
An application of lyapunov stability analysis to improve the performance of NARMAX models
Previously we presented a novel approach to program a robot controller based on system identification and robot training techniques. The proposed method works in two stages: first, the programmer demonstrates the desired behaviour to the robot by driving it manually in the target environment. During this run, the sensory perception and the desired velocity commands of the robot are logged. Having thus obtained training data we model the relationship between sensory readings and the motor commands of the robot using ARMAX/NARMAX models and system identification techniques. These produce linear or non-linear polynomials which can be formally analysed, as well as used in place of โtraditional robotโ control code.
In this paper we focus our attention on how the mathematical analysis of NARMAX models can be used to understand the robotโs control actions, to formulate hypotheses and to improve the robotโs behaviour. One main objective behind this approach is to avoid trial-and-error refinement of robot code. Instead, we seek to obtain a reliable design process, where program design decisions are
based on the mathematical analysis of the model describing how the robot interacts with its environment to achieve the desired behaviour. We demonstrate this procedure through the analysis of a particular task in mobile robotics: door traversal
Comparing robot controllers through system identification
In the mobile robotics field, it is very common to find different control programs designed to achieve a particular robot task. Although there are many ways to evaluate these controllers qualitatively, there is a lack of formal methodology to compare them from a mathematical point of view. In this paper we present a novel approach to compare robot control codes quantitatively based on system identification: Initially the transparent mathematical models of the controllers are obtained using the NARMAX system identification process. Then we use these models to analyse the general characteristics of the cotrollers from a mathematical point of view. In this way, we are able to compare different control programs objectively based on quantitative measures. We demonstrate our approach by comparing two different robot control programs, which were designed to drive the robot through door-like openings
Model identification and model analysis in robot training
Robot training is a fast and efficient method of obtaining robot control code. Many current machine learning paradigms used for this purpose, however, result in opaque models that are difficult, if not impossible to analyse, which is an impediment in safety-critical applications or application
scenarios where humans and robots occupy the same workspace.
In experiments with a Magellan Pro mobile robot we demonstrate that it is possible to obtain transparent models of sensor-motor couplings that are amenable to subsequent analysis, and how such analysis can be used
to refine and tune the models post hoc
Versatile Multi-Contact Planning and Control for Legged Loco-Manipulation
Loco-manipulation planning skills are pivotal for expanding the utility of
robots in everyday environments. These skills can be assessed based on a
system's ability to coordinate complex holistic movements and multiple contact
interactions when solving different tasks. However, existing approaches have
been merely able to shape such behaviors with hand-crafted state machines,
densely engineered rewards, or pre-recorded expert demonstrations. Here, we
propose a minimally-guided framework that automatically discovers whole-body
trajectories jointly with contact schedules for solving general
loco-manipulation tasks in pre-modeled environments. The key insight is that
multi-modal problems of this nature can be formulated and treated within the
context of integrated Task and Motion Planning (TAMP). An effective bilevel
search strategy is achieved by incorporating domain-specific rules and
adequately combining the strengths of different planning techniques: trajectory
optimization and informed graph search coupled with sampling-based planning. We
showcase emergent behaviors for a quadrupedal mobile manipulator exploiting
both prehensile and non-prehensile interactions to perform real-world tasks
such as opening/closing heavy dishwashers and traversing spring-loaded doors.
These behaviors are also deployed on the real system using a two-layer
whole-body tracking controller
Behavior Trees in Robotics and AI: An Introduction
A Behavior Tree (BT) is a way to structure the switching between different
tasks in an autonomous agent, such as a robot or a virtual entity in a computer
game. BTs are a very efficient way of creating complex systems that are both
modular and reactive. These properties are crucial in many applications, which
has led to the spread of BT from computer game programming to many branches of
AI and Robotics. In this book, we will first give an introduction to BTs, then
we describe how BTs relate to, and in many cases generalize, earlier switching
structures. These ideas are then used as a foundation for a set of efficient
and easy to use design principles. Properties such as safety, robustness, and
efficiency are important for an autonomous system, and we describe a set of
tools for formally analyzing these using a state space description of BTs. With
the new analysis tools, we can formalize the descriptions of how BTs generalize
earlier approaches. We also show the use of BTs in automated planning and
machine learning. Finally, we describe an extended set of tools to capture the
behavior of Stochastic BTs, where the outcomes of actions are described by
probabilities. These tools enable the computation of both success probabilities
and time to completion
Authoring and Operating Humanoid Behaviors On the Fly using Coactive Design Principles
Humanoid robots have the potential to perform useful tasks in a world built
for humans. However, communicating intention and teaming with a humanoid robot
is a multi-faceted and complex problem. In this paper, we tackle the problems
associated with quickly and interactively authoring new robot behavior that
works on real hardware. We bring the powerful concepts of Affordance Templates
and Coactive Design methodology to this problem to attempt to solve and explain
it. In our approach we use interactive stance and hand pose goals along with
other types of actions to author humanoid robot behavior on the fly. We then
describe how our operator interface works to author behaviors on the fly and
provide interdependence analysis charts for task approach and door opening. We
present timings from real robot performances for traversing a push door and
doing a pick and place task on our Nadia humanoid robot.Comment: 8 pages, 12 figures, for Humanoids 202
DECISIVE Test Methods Handbook: Test Methods for Evaluating sUAS in Subterranean and Constrained Indoor Environments, Version 1.1
This handbook outlines all test methods developed under the Development and
Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle
Evaluations (DECISIVE) project by the University of Massachusetts Lowell for
evaluating small unmanned aerial systems (sUAS) performance in subterranean and
constrained indoor environments, spanning communications, field readiness,
interface, obstacle avoidance, navigation, mapping, autonomy, trust, and
situation awareness. For sUAS deployment in subterranean and constrained indoor
environments, this puts forth two assumptions about applicable sUAS to be
evaluated using these test methods: (1) able to operate without access to GPS
signal, and (2) width from prop top to prop tip does not exceed 91 cm (36 in)
wide (i.e., can physically fit through a typical doorway, although successful
navigation through is not guaranteed). All test methods are specified using a
common format: Purpose, Summary of Test Method, Apparatus and Artifacts,
Equipment, Metrics, Procedure, and Example Data. All test methods are designed
to be run in real-world environments (e.g., MOUT sites) or using fabricated
apparatuses (e.g., test bays built from wood, or contained inside of one or
more shipping containers).Comment: Approved for public release: PAO #PR2022_4705
๊ธฐ๊ตฌํ์ ๋ฐ ๋์ ์ ํ์กฐ๊ฑด๋ค์ ๊ณ ๋ คํ ๋ชจ๋ฐ์ผ ๋งค๋ํฐ๋ ์ดํฐ์ ์์ ์ค์ฌ ์ ์ ๋์ ์์ฑ ์ ๋ต
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์ตํฉ๊ณผํ๊ธฐ์ ๋ํ์ ์ตํฉ๊ณผํ๋ถ(์ง๋ฅํ์ตํฉ์์คํ
์ ๊ณต), 2023. 2. ๋ฐ์ฌํฅ.๋ชจ๋ฐ์ผ ๋งค๋ํฐ๋ ์ดํฐ๋ ๋ชจ๋ฐ์ผ ๋ก๋ด์ ์ฅ์ฐฉ๋ ๋งค๋ํฐ๋ ์ดํฐ์
๋๋ค. ๋ชจ๋ฐ์ผ ๋งค๋ํฐ๋ ์ดํฐ๋ ๊ณ ์ ํ ๋งค๋ํฐ๋ ์ดํฐ์ ๋นํด ๋ชจ๋ฐ์ผ ๋ก๋ด์ ์ด๋์ฑ์ ์ ๊ณต๋ฐ๊ธฐ ๋๋ฌธ์ ๋ค์ํ๊ณ ๋ณต์กํ ์์
์ ์ํํ ์ ์์ต๋๋ค. ๊ทธ๋ฌ๋ ๋ ๊ฐ์ ์๋ก ๋ค๋ฅธ ์์คํ
์ ๊ฒฐํฉํจ์ผ๋ก์จ ๋ชจ๋ฐ์ผ ๋งค๋ํฐ๋ ์ดํฐ์ ์ ์ ์ ๊ณํํ๊ณ ์ ์ดํ ๋ ์ฌ๋ฌ ํน์ง์ ๊ณ ๋ คํด์ผ ํฉ๋๋ค. ์ด๋ฌํ ํน์ง๋ค์ ์ฌ์์ ๋, ๋ ์์คํ
์ ๊ด์ฑ ์ฐจ์ด ๋ฐ ๋ชจ๋ฐ์ผ ๋ก๋ด์ ๋นํ๋ก๋
ธ๋ฏน ์ ํ ์กฐ๊ฑด ๋ฑ์ด ์์ต๋๋ค. ๋ณธ ๋
ผ๋ฌธ์ ๋ชฉ์ ์ ๊ธฐ๊ตฌํ์ ๋ฐ ๋์ ์ ํ์กฐ๊ฑด๋ค์ ๊ณ ๋ คํ์ฌ ๋ชจ๋ฐ์ผ ๋งค๋ํฐ๋ ์ดํฐ์ ์ ์ ๋์ ์์ฑ ์ ๋ต์ ์ ์ํ๋ ๊ฒ์
๋๋ค.
๋จผ์ , ๋ชจ๋ฐ์ผ ๋งค๋ํฐ๋ ์ดํฐ๊ฐ ์ด๊ธฐ ์์น์์ ๋ฌธ์ ํต๊ณผํ์ฌ ๋ชฉํ ์์น์ ๋๋ฌํ๊ธฐ ์ํ ์ ์ ๊ฒฝ๋ก๋ฅผ ๊ณ์ฐํ๋ ํ๋ ์์ํฌ๋ฅผ ์ ์ํฉ๋๋ค. ์ด ํ๋ ์์ํฌ๋ ๋ก๋ด๊ณผ ๋ฌธ์ ์ํด ์๊ธฐ๋ ๊ธฐ๊ตฌํ์ ์ ํ์กฐ๊ฑด์ ๊ณ ๋ คํฉ๋๋ค. ์ ์ํ๋ ํ๋ ์์ํฌ๋ ๋ ๋จ๊ณ๋ฅผ ๊ฑฐ์ณ ์ ์ ์ ๊ฒฝ๋ก๋ฅผ ์ป์ต๋๋ค. ์ฒซ ๋ฒ์งธ ๋จ๊ณ์์๋ ๊ทธ๋ํ ํ์ ์๊ณ ๋ฆฌ์ฆ์ ์ด์ฉํ์ฌ ๋ชจ๋ฐ์ผ ๋ก๋ด์ ์์ธ ๊ฒฝ๋ก์ ๋ฌธ์ ๊ฐ๋ ๊ฒฝ๋ก๋ฅผ ๊ณ์ฐํฉ๋๋ค. ํนํ, ๊ทธ๋ํ ํ์์์ area indicator๋ผ๋ ์ ์ ๋ณ์๋ฅผ ์ํ์ ๊ตฌ์ฑ ์์๋ก์ ์ ์ํ๋๋ฐ, ์ด๋ ๋ฌธ์ ๋ํ ๋ชจ๋ฐ์ผ ๋ก๋ด์ ์๋์ ์์น๋ฅผ ๋ํ๋
๋๋ค. ๋ ๋ฒ์งธ ๋จ๊ณ์์๋ ๋ชจ๋ฐ์ผ ๋ก๋ด์ ๊ฒฝ๋ก์ ๋ฌธ์ ๊ฐ๋๋ฅผ ํตํด ๋ฌธ์ ์์ก์ด ์์น๋ฅผ ๊ณ์ฐํ๊ณ ์ญ๊ธฐ๊ตฌํ์ ํ์ฉํ์ฌ ๋งค๋ํฐ๋ ์ดํฐ์ ๊ด์ ์์น๋ฅผ ๊ณ์ฐํฉ๋๋ค. ์ ์๋ ํ๋ ์์ํฌ์ ํจ์จ์ฑ์ ๋นํ๋ก๋
ธ๋ฏน ๋ชจ๋ฐ์ผ ๋งค๋ํฐ๋ ์ดํฐ๋ฅผ ์ฌ์ฉํ ์๋ฎฌ๋ ์ด์
๋ฐ ์ค์ ์คํ์ ํตํด ๊ฒ์ฆ๋์์ต๋๋ค.
๋ ์งธ, ์ต์ ํ ๋ฐฉ๋ฒ์ ๊ธฐ๋ฐ์ผ๋กํ ์ ์ ์ ์ด๊ธฐ๋ฅผ ์ ์ํฉ๋๋ค. ์ด ๋ฐฉ๋ฒ์ ๋ฑ์ ๋ฐ ๋ถ๋ฑ์ ์ ํ์กฐ๊ฑด ๋ชจ๋์ ๋ํด ๊ฐ์ค ํ๋ ฌ์ ๋ฐ์ํ ๊ณ์ธต์ ์ต์ ํ ๋ฌธ์ ์ ํด๋ฅผ ๊ณ์ฐํฉ๋๋ค. ์ด ๋ฐฉ๋ฒ์ ๋ชจ๋ฐ์ผ ๋งค๋ํฐ๋ ์ดํฐ ๋๋ ํด๋จธ๋
ธ์ด๋์ ๊ฐ์ด ์์ ๋๊ฐ ๋ง์ ๋ก๋ด์ ์ฌ์์ ๋๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ๊ฐ๋ฐ๋์ด ์์
์ฐ์ ์์์ ๋ฐ๋ผ ๊ฐ์ค์น๊ฐ ๋ค๋ฅธ ๊ด์ ๋์์ผ๋ก ์ฌ๋ฌ ์์
์ ์ํํ ์ ์์ต๋๋ค. ์ ์๋ ๋ฐฉ๋ฒ์ ๊ฐ์ค ํ๋ ฌ์ ์ต์ ํ ๋ฌธ์ ์ 1์ฐจ ์ต์ ์กฐ๊ฑด์ ๋ง์กฑํ๋๋ก ํ๋ฉฐ, Active-set ๋ฐฉ๋ฒ์ ํ์ฉํ์ฌ ๋ฑ์ ๋ฐ ๋ถ๋ฑ์ ์์
์ ์ฒ๋ฆฌํฉ๋๋ค. ๋ํ, ๋์นญ์ ์ธ ์๊ณต๊ฐ ์ฌ์ ํ๋ ฌ์ ์ฌ์ฉํ์ฌ ๊ณ์ฐ์ ํจ์จ์ ์
๋๋ค. ๊ฒฐ๊ณผ์ ์ผ๋ก, ์ ์๋ ์ ์ด๊ธฐ๋ฅผ ํ์ฉํ๋ ๋ก๋ด์ ์ฐ์ ์์์ ๋ฐ๋ผ ๊ฐ๋ณ์ ์ธ ๊ด์ ๊ฐ์ค์น๋ฅผ ๋ฐ์ํ์ฌ ์ ์ ์์ง์์ ํจ๊ณผ์ ์ผ๋ก ๋ณด์ฌ์ค๋๋ค. ์ ์๋ ์ ์ด๊ธฐ์ ํจ์ฉ์ฑ์ ๋ชจ๋ฐ์ผ ๋งค๋ํฐ๋ ์ดํฐ์ ํด๋จธ๋
ธ์ด๋๋ฅผ ์ด์ฉํ ์คํ์ ํตํด ๊ฒ์ฆํ์์ต๋๋ค.
๋ง์ง๋ง์ผ๋ก, ๋ชจ๋ฐ์ผ ๋งค๋ํฐ๋ ์ดํฐ์ ๋์ ์ ํ์กฐ๊ฑด๋ค ์ค ํ๋๋ก์ ์๊ฐ ์ถฉ๋ ํํผ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํฉ๋๋ค. ์ ์๋ ๋ฐฉ๋ฒ์ ๋งค๋ํฐ๋ ์ดํฐ์ ๋ชจ๋ฐ์ผ ๋ก๋ด ๊ฐ์ ์๊ฐ ์ถฉ๋์ ์ค์ ์ ๋ก๋๋ค. ๋ชจ๋ฐ์ผ ๋ก๋ด์ ๋ฒํผ ์์ญ์ ๋๋ฌ์ธ๋ 3์ฐจ์ ๊ณก๋ฉด์ธ distance buffer border์ ๊ฐ๋
์ ์ ์ํฉ๋๋ค. ๋ฒํผ ์์ญ์ ๋๊ป๋ ๋ฒํผ ๊ฑฐ๋ฆฌ์
๋๋ค. ๋งค๋ํฐ๋ ์ดํฐ์ ๋ชจ๋ฐ์ผ ๋ก๋ด ์ฌ์ด์ ๊ฑฐ๋ฆฌ๊ฐ ๋ฒํผ ๊ฑฐ๋ฆฌ๋ณด๋ค ์์ ๊ฒฝ์ฐ, ์ฆ ๋งค๋ํฐ๋ ์ดํฐ๊ฐ ๋ชจ๋ฐ์ผ ๋ก๋ด์ ๋ฒํผ ์์ญ ๋ด๋ถ์ ์๋ ๊ฒฝ์ฐ ์ ์๋ ์ ๋ต์ ๋งค๋ํฐ๋ ์ดํฐ๋ฅผ ๋ฒํผ ์์ญ ๋ฐ์ผ๋ก ๋ด๋ณด๋ด๊ธฐ ์ํด ๋ชจ๋ฐ์ผ ๋ก๋ด์ ์์ง์์ ์์ฑํฉ๋๋ค. ๋ฐ๋ผ์ ๋งค๋ํฐ๋ ์ดํฐ๋ ๋ฏธ๋ฆฌ ์ ์๋ ๋งค๋ํฐ๋ ์ดํฐ์ ์์ง์์ ์์ ํ์ง ์๊ณ ๋ ๋ชจ๋ฐ์ผ ๋ก๋ด๊ณผ์ ์๊ฐ ์ถฉ๋์ ํผํ ์ ์์ต๋๋ค. ๋ชจ๋ฐ์ผ ๋ก๋ด์ ์์ง์์ ๊ฐ์์ ํ์ ๊ฐํจ์ผ๋ก์จ ์์ฑ๋ฉ๋๋ค. ํนํ, ํ์ ๋ฐฉํฅ์ ์ฐจ๋ ๊ตฌ๋ ์ด๋ ๋ก๋ด์ ๋นํ๋ก๋
ธ๋ฏน ์ ์ฝ ๋ฐ ์กฐ์๊ธฐ์ ๋๋ฌ ๊ฐ๋ฅ์ฑ์ ๊ณ ๋ คํ์ฌ ๊ฒฐ์ ๋ฉ๋๋ค. ์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ 7์์ ๋ ๋ก๋ดํ์ ๊ฐ์ง ์ฐจ๋ ๊ตฌ๋ ๋ชจ๋ฐ์ผ ๋ก๋ด์ ์ ์ฉํ์ฌ ๋ค์ํ ์คํ ์๋๋ฆฌ์ค์์ ์
์ฆ๋์์ต๋๋ค.A mobile manipulator is a manipulator mounted on a mobile robot. Compared to a fixed-base manipulator, the mobile manipulator can perform various and complex tasks because the mobility is offered by the mobile robot. However, combining two different systems causes several features to be considered when generating the whole-body motion of the mobile manipulator. The features include redundancy, inertia difference, and non-holonomic constraint. The purpose of this thesis is to propose the whole-body motion generation strategy of the mobile manipulator for considering kinematic and dynamic constraints.
First, a planning framework is proposed that computes a path for the whole-body configuration of the mobile manipulator to navigate from the initial position, traverse through the door, and arrive at the target position. The framework handles the kinematic constraint imposed by the closed-chain between the robot and door. The proposed framework obtains the path of the whole-body configuration in two steps. First, the path for the pose of the mobile robot and the path for the door angle are computed by using the graph search algorithm. In graph search, an integer variable called area indicator is introduced as an element of state, which indicates where the robot is located relative to the door. Especially, the area indicator expresses a process of door traversal. In the second step, the configuration of the manipulator is computed by the inverse kinematics (IK) solver from the path of the mobile robot and door angle. The proposed framework has a distinct advantage over the existing methods that manually determine several parameters such as which direction to approach the door and the angle of the door required for passage. The effectiveness of the proposed framework was validated through experiments with a nonholonomic mobile manipulator.
Second, a whole-body controller is presented based on the optimization method that can consider both equality and inequality constraints. The method computes the optimal solution of the weighted hierarchical optimization problem. The method is developed to resolve the redundancy of robots with a large number of Degrees of Freedom (DOFs), such as a mobile manipulator or a humanoid, so that they can execute multiple tasks with differently weighted joint motion for each task priority. The proposed method incorporates the weighting matrix into the first-order optimality condition of the optimization problem and leverages an active-set method to handle equality and inequality constraints. In addition, it is computationally efficient because the solution is calculated in a weighted joint space with symmetric null-space projection matrices for propagating recursively to a low priority task. Consequently, robots that utilize the proposed controller effectively show whole-body motions handling prioritized tasks with differently weighted joint spaces. The effectiveness of the proposed controller was validated through experiments with a nonholonomic mobile manipulator as well as a humanoid.
Lastly, as one of dynamic constraints for the mobile manipulator, a reactive self-collision avoidance algorithm is developed. The proposed method mainly focuses on self-collision between a manipulator and the mobile robot. We introduce the concept of a distance buffer border (DBB), which is a 3D curved surface enclosing a buffer region of the mobile robot. The region has the thickness equal to buffer distance. When the distance between the manipulator and mobile robot is less than the buffer distance, i.e. the manipulator lies inside the buffer region of the mobile robot, the proposed strategy is to move the mobile robot away from the manipulator in order for the manipulator to be placed outside the border of the region, the DBB. The strategy is achieved by exerting force on the mobile robot. Therefore, the manipulator can avoid self-collision with the mobile robot without modifying the predefined motion of the manipulator in a world Cartesian coordinate frame. In particular, the direction of the force is determined by considering the non-holonomic constraint of the differentially driven mobile robot. Additionally, the reachability of the manipulator is considered to arrive at a configuration in which the manipulator can be more maneuverable. To realize the desired force and resulting torque, an avoidance task is constructed by converting them into the accelerations of the mobile robot and smoothly inserted with a top priority into the controller. The proposed algorithm was implemented on a differentially driven mobile robot with a 7-DOFs robotic arm and its performance was demonstrated in various experimental scenarios.1 INTRODUCTION 1
1.1 Motivation 1
1.2 Contributions of thesis 2
1.3 Overview of thesis 3
2 WHOLE-BODY MOTION PLANNER : APPLICATION TO NAVIGATION INCLUDING DOOR TRAVERSAL 5
2.1 Background & related works 7
2.2 Proposed framework 9
2.2.1 Computing path for mobile robot and door angle - S1 10
2.2.1.1 State 10
2.2.1.2 Action 13
2.2.1.3 Cost 15
2.2.1.4 Search 18
2.2.2 Computing path for arm configuration - S2 20
2.3 Results 21
2.3.1 Application to pull and push-type door 21
2.3.2 Experiment in cluttered environment 22
2.3.3 Experiment with different robot platform 23
2.3.4 Comparison with separate planning by existing works 24
2.3.5 Experiment with real robot 29
2.4 Conclusion 29
3 WHOLE-BODY CONTROLLER : WEIGHTED HIERARCHICAL QUADRATIC PROGRAMMING 31
3.1 Related works 32
3.2 Problem statement 34
3.2.1 Pseudo-inverse with weighted least-squares norm for each task 35
3.2.2 Problem statement 37
3.3 WHQP with equality constraints 37
3.4 WHQP with inequality constraints 45
3.5 Experimental results 48
3.5.1 Simulation experiment with nonholonomic mobile manipulator 48
3.5.1.1 Scenario description 48
3.5.1.2 Task and weighting matrix description 49
3.5.1.3 Results 51
3.5.2 Real experiment with nonholonomic mobile manipulator 53
3.5.2.1 Scenario description 53
3.5.2.2 Task and weighting matrix description 53
3.5.2.3 Results 54
3.5.3 Real experiment with humanoid 55
3.5.3.1 Scenario description 55
3.5.3.2 Task and weighting matrix description 55
3.5.3.3 Results 57
3.6 Discussions and implementation details 57
3.6.1 Computation cost 57
3.6.2 Composite weighting matrix in same hierarchy 59
3.6.3 Nullity of WHQP 59
3.7 Conclusion 59
4 WHOLE-BODY CONSTRAINT : SELF-COLLISION AVOIDANCE 61
4.1 Background & related Works 64
4.2 Distance buffer border and its score computation 65
4.2.1 Identification of potentially colliding link pairs 66
4.2.2 Distance buffer border 67
4.2.3 Evaluation of distance buffer border 69
4.2.3.1 Singularity of the differentially driven mobile robot 69
4.2.3.2 Reachability of the manipulator 72
4.2.3.3 Score of the DBB 74
4.3 Self-collision avoidance algorithm 75
4.3.1 Generation of the acceleration for the mobile robot 76
4.3.2 Generation of the repulsive acceleration for the other link pair 82
4.3.3 Construction of an acceleration-based task for self-collision avoidance 83
4.3.4 Insertion of the task in HQP-based controller 83
4.4 Experimental results 86
4.4.1 System overview 87
4.4.2 Experimental results 87
4.4.2.1 Self-collision avoidance while tracking the predefined trajectory 87
4.4.2.2 Self-collision avoidance while manually guiding the end-effector 89
4.4.2.3 Extension to obstacle avoidance when opening the refrigerator 91
4.4.3 Discussion 94
4.5 Conclusion 95
5 CONCLUSIONS 97
Abstract (In Korean) 113
Acknowlegement 116๋ฐ
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