4,474 research outputs found
MPC-based humanoid pursuit-evasion in the presence of obstacles
We consider a pursuit-evasion problem between humanoids in the presence of obstacles. In our scenario, the pursuer enters the safety area of the evader headed for collision, while the latter executes a fast evasive motion. Control schemes are designed for both the pursuer and the evader. They are structurally identical, although the objectives are different: the pursuer tries to align its direction of motion with the line- of-sight to the evader, whereas the evader tries to move in a direction orthogonal to the line-of-sight to the pursuer. At the core of the control architecture is a Model Predictive Control scheme for generating a stable gait. This allows for the inclusion of workspace obstacles, which we take into account at two levels: during the determination of the footsteps orientation and as an explicit MPC constraint. We illustrate the results with simulations on NAO humanoids
Overcoming barriers and increasing independence: service robots for elderly and disabled people
This paper discusses the potential for service robots to overcome barriers and increase independence of
elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly
people and advances in technology which will make new uses possible and provides suggestions for some of these new
applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses
the complementarity of assistive service robots and personal assistance and considers the types of applications and
users for which service robots are and are not suitable
An intelligent, free-flying robot
The ground based demonstration of the extensive extravehicular activity (EVA) Retriever, a voice-supervised, intelligent, free flying robot, is designed to evaluate the capability to retrieve objects (astronauts, equipment, and tools) which have accidentally separated from the Space Station. The major objective of the EVA Retriever Project is to design, develop, and evaluate an integrated robotic hardware and on-board software system which autonomously: (1) performs system activation and check-out; (2) searches for and acquires the target; (3) plans and executes a rendezvous while continuously tracking the target; (4) avoids stationary and moving obstacles; (5) reaches for and grapples the target; (6) returns to transfer the object; and (7) returns to base
Advancing automation and robotics technology for the Space Station Freedom and for the US economy
The progress made by levels 1, 2, and 3 of the Office of Space Station in developing and applying advanced automation and robotics technology is described. Emphasis is placed upon the Space Station Freedom Program responses to specific recommendations made in the Advanced Technology Advisory Committee (ATAC) progress report 10, the flight telerobotic servicer, and the Advanced Development Program. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for the Space Station Freedom
Analysis of Load-Carrying Capacity for Redundant Free-Floating Space Manipulators in Trajectory Tracking Task
The aim of this paper is to analyze load-carrying capacity of redundant free-floating space manipulators (FFSM) in trajectory tracking task. Combined with the analysis of influential factors in load-carrying process, evaluation of maximum load-carrying capacity (MLCC) is described as multiconstrained nonlinear programming problem. An efficient algorithm based on repeated line search within discontinuous feasible region is presented to determine MLCC for a given trajectory of the end-effector and corresponding joint path. Then, considering the influence of MLCC caused by different initial configurations for the starting point of given trajectory, a kind of maximum payload initial configuration planning method is proposed by using PSO algorithm. Simulations are performed for a particular trajectory tracking task of the 7-DOF space manipulator, of which MLCC is evaluated quantitatively. By in-depth research of the simulation results, significant gap between the values of MLCC when using different initial configurations is analyzed, and the discontinuity of allowable load-carrying capacity is illustrated. The proposed analytical method can be taken as theoretical foundation of feasibility analysis, trajectory optimization, and optimal control of trajectory tracking task in on-orbit load-carrying operations
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν ν곡μ°μ£Όκ³΅νκ³Ό, 2020. 8. κΉνμ§.Learning from demonstrations (LfD) is a promising approach that enables robots to perform a specific movement. As robotic manipulations are substituting a variety of tasks, LfD algorithms are widely used and studied for specifying the robot configurations for the various types of movements.
This dissertation presents an approach based on parametric dynamic movement primitives (PDMP) as a motion representation algorithm which is one of relevant LfD techniques. Unlike existing motion representation algorithms, this work not only represents a prescribed motion but also computes the new behavior through a generalization of multiple demonstrations in the actual environment. The generalization process uses Gaussian process regression (GPR) by representing the nonlinear relationship between the PDMP parameters that determine motion and the corresponding environmental variables. The proposed algorithm shows that it serves as a powerful optimal and real-time motion planner among the existing planning algorithms when optimal demonstrations are provided as dataset.
In this dissertation, the safety of motion is also considered. Here, safety refers to keeping the system away from certain configurations that are unsafe. The safety criterion of the PDMP internal parameters are computed to check the safety. This safety criterion reflects the new behavior computed through the generalization process, as well as the individual motion safety of the demonstration set. The demonstrations causing unsafe movement are identified and removed. Also, the demolished demonstrations are replaced by proven demonstrations upon this criterion.
This work also presents an extension approach reducing the number of required demonstrations for the PDMP framework. This approach is effective where a single mission consists of multiple sub-tasks and requires numerous demonstrations in generalizing them. The whole trajectories in provided demonstrations are segmented into multiple sub-tasks representing unit motions. Then, multiple PDMPs are formed independently for correlated-segments. The phase-decision process determines which sub-task and associated PDMPs to be executed online, allowing multiple PDMPs to be autonomously configured within an integrated framework. GPR formulations are applied to obtain execution time and regional goal configuration for each sub-task.
Finally, the proposed approach and its extension are validated with the actual experiments of mobile manipulators. The first two scenarios regarding cooperative aerial transportation demonstrate the excellence of the proposed technique in terms of quick computation, generation of efficient movement, and safety assurance. The last scenario deals with two mobile manipulations using ground vehicles and shows the effectiveness of the proposed extension in executing complex missions.μμ° νμ΅ κΈ°λ²(Learning from demonstrations, LfD)μ λ‘λ΄μ΄ νΉμ λμμ μνν μ μλλ‘ νλ μ λ§ν λμ μμ± κΈ°λ²μ΄λ€. λ‘λ΄ μ‘°μκΈ°κ° μΈκ° μ¬νμμ λ€μν μ
무λ₯Ό λμ²΄ν΄ κ°μ λ°λΌ, λ€μν μ무λ₯Ό μννλ λ‘λ΄μ λμμ μμ±νκΈ° μν΄ LfD μκ³ λ¦¬μ¦λ€μ λ리 μ°κ΅¬λκ³ , μ¬μ©λκ³ μλ€.
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ν리머ν°λΈ κΈ°λ°μ λμ μ¬μμ± μκ³ λ¦¬μ¦μΈ Parametric dynamic movement primitives(PDMP)μ κΈ°μ΄ν μκ³ λ¦¬μ¦μ μ μνλ©°, μ΄λ₯Ό ν΅ν΄ λ€μν μ무λ₯Ό μννλ λͺ¨λ°μΌ μ‘°μκΈ°μ κΆ€μ μ μμ±νλ€. κΈ°μ‘΄μ λμ μ¬μμ± μκ³ λ¦¬μ¦κ³Ό λ¬λ¦¬, μ΄ μ°κ΅¬λ μ 곡λ μμ°μμ ννλ λμμ λ¨μν μ¬μμ±νλ κ²μ κ·ΈμΉμ§ μκ³ , μλ‘μ΄ νκ²½μ λ§κ² μΌλ°ν νλ κ³Όμ μ ν¬ν¨νλ€. μ΄ λ
Όλ¬Έμμ μ μνλ μΌλ°ν κ³Όμ μ PDMPsμ λ΄λΆ νλΌλ―Έν° κ°μΈ μ€νμΌ νλΌλ―Έν°μ νκ²½ λ³μ μ¬μ΄μ λΉμ ν κ΄κ³λ₯Ό κ°μ°μ€ νκ· κΈ°λ² (Gaussian process regression, GPR)μ μ΄μ©νμ¬ μμμ μΌλ‘ νννλ€. μ μλ κΈ°λ²μ λν μ΅μ μμ°λ₯Ό νμ΅νλ λ°©μμ ν΅ν΄ κ°λ ₯ν μ΅μ μ€μκ° κ²½λ‘ κ³ν κΈ°λ²μΌλ‘λ μμ©λ μ μλ€.
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Όλ¬Έμμλ λν λ‘λ΄μ ꡬλ μμ μ±λ κ³ λ €νλ€. κΈ°μ‘΄ μ°κ΅¬λ€μμ λ€λ£¨μ΄μ§ μμ° κ΄λ¦¬ κΈ°μ μ΄ λ‘λ΄μ ꡬλ ν¨μ¨μ±μ κ°μ νλ λ°©ν₯μΌλ‘ μ μλ κ²κ³Ό λ¬λ¦¬, μ΄ μ°κ΅¬λ κ°ν ꡬμ쑰건μΌλ‘ λ‘λ΄μ ꡬλ μμ μ±μ ν보νλ μμ° κ΄λ¦¬ κΈ°μ μ ν΅ν΄ μμ μ±μ κ³ λ €νλ μλ‘μ΄ λ°©μμ μ μνλ€. μ μλ λ°©μμ μ€νμΌ νλΌλ―Έν° κ° μμμ μμ μ± κΈ°μ€μ κ³μ°νλ©°, μ΄ μμ κΈ°μ€μ ν΅ν΄ μμ°μ μ κ±°νλ μΌλ ¨μ μμ
μ μννλ€. λν, μ κ±°λ μμλ₯Ό μμ κΈ°μ€μ λ°λΌ μ
μ¦λ μμλ‘ λ체νμ¬ μΌλ°ν μ±λ₯μ μ νμν€μ§ μλλ‘ μμλ₯Ό κ΄λ¦¬νλ€. μ΄λ₯Ό ν΅ν΄ λ€μμ μμ° κ°κ° κ°λ³ λμ μμ μ± λΏ μλλΌ μ¨λΌμΈ λμμ μμ μ±κΉμ§ κ³ λ €ν μ μμΌλ©°, μ€μκ° λ‘λ΄ μ‘°μκΈ° μ΄μ©μ μμ μ±μ΄ ν보λ μ μλ€. μ μλ μμ μ±μ κ³ λ €ν μμ° κ΄λ¦¬ κΈ°μ μ λν νκ²½μ μ μ μ€μ μ΄ λ³κ²½λμ΄ λͺ¨λ μμ°μ κ΅μ²΄ν΄μΌ ν μ μλ μν©μμ μ¬μ©ν μ μλ μμ°λ€μ νλ³νκ³ , ν¨μ¨μ μΌλ‘ μ¬μ¬μ©νλ λ° μμ©ν μ μλ€.
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Όλ¬Έμ 볡μ‘ν μ무μμ μ μ©λ μ μλ PDMPsμ νμ₯ κΈ°λ²μΈ seg-PDMPsλ₯Ό μ μνλ€. μ΄ μ κ·Όλ°©μμ 볡μ‘ν μλ¬΄κ° μΌλ°μ μΌλ‘ 볡μκ°μ κ°λ¨ν νμ μμ
μΌλ‘ ꡬμ±λλ€κ³ κ°μ νλ€. κΈ°μ‘΄ PDMPsμ λ¬λ¦¬ seg-PDMPsλ μ 체 κΆ€μ μ νμ μμ
μ λνλ΄λ μ¬λ¬ κ°μ λ¨μ λμμΌλ‘ λΆν νκ³ , κ° λ¨μλμμ λν΄ μ¬λ¬κ°μ PDMPsλ₯Ό ꡬμ±νλ€. κ° λ¨μ λμ λ³λ‘ μμ±λ PDMPsλ ν΅ν©λ νλ μμν¬λ΄μμ λ¨κ³ κ²°μ νλ‘μΈμ€λ₯Ό ν΅ν΄ μλμ μΌλ‘ νΈμΆλλ€. κ° λ¨κ³ λ³λ‘ λ¨μ λμμ μννκΈ° μν μκ° λ° νμ λͺ©νμ μ κ°μ°μ€ 곡μ νκ·(GPR)λ₯Ό μ΄μ©ν νκ²½λ³μμμμ κ΄κ³μμ ν΅ν΄ μ»λλ€. κ²°κ³Όμ μΌλ‘, μ΄ μ°κ΅¬λ μ 체μ μΌλ‘ μꡬλλ μμ°μ μλ₯Ό ν¨κ³Όμ μΌλ‘ μ€μΌ λΏ μλλΌ, κ° λ¨μλμμ νν μ±λ₯μ κ°μ νλ€.
μ μλ μκ³ λ¦¬μ¦μ νλ λͺ¨λ°μΌ λ‘λ΄ μ‘°μκΈ° μ€νμ ν΅νμ¬ κ²μ¦λλ€. μΈ κ°μ§μ μλ리μ€κ° λ³Έ λ
Όλ¬Έμμ λ€λ£¨μ΄μ§λ©°, ν곡 μ΄μ‘κ³Ό κ΄λ ¨λ 첫 λ κ°μ§ μλ리μ€λ PDMPs κΈ°λ²μ΄ λ‘λ΄ μ‘°μκΈ°μμ λΉ λ₯Έ μ μμ±, μ무 ν¨μ¨μ±κ³Ό μμ μ± λͺ¨λ λ§μ‘±νλ κ²μ μ
μ¦νλ€. λ§μ§λ§ μλ리μ€λ μ§μ μ°¨λμ μ΄μ©ν λ κ°μ λ‘λ΄ μ‘°μκΈ°μ λν μ€νμΌλ‘ 볡μ‘ν μ무 μνμ νκΈ° μν΄ νμ₯λ κΈ°λ²μΈ seg-PDMPsκ° ν¨κ³Όμ μΌλ‘ λ³ννλ νκ²½μμ μΌλ°νλ λμμ μμ±ν¨μ κ²μ¦νλ€.1 Introduction 1
1.1 Motivations 1
1.2 Literature Survey 3
1.2.1 Conventional Motion Planning in Mobile Manipulations 3
1.2.2 Motion Representation Algorithms 5
1.2.3 Safety-guaranteed Motion Representation Algorithms 7
1.3 Research Objectives and Contributions 7
1.3.1 Motion Generalization in Motion Representation Algorithm 9
1.3.2 Motion Generalization with Safety Guarantee 9
1.3.3 Motion Generalization for Complex Missions 10
1.4 Thesis Organization 11
2 Background 12
2.1 DMPs 12
2.2 Mobile Manipulation Systems 13
2.2.1 Single Mobile Manipulation 14
2.2.2 Cooperative Mobile Manipulations 14
2.3 Experimental Setup 17
2.3.1 Test-beds for Aerial Manipulators 17
2.3.2 Test-beds for Robot Manipulators with Ground Vehicles 17
3 Motion Generalization in Motion Representation Algorithm 22
3.1 Parametric Dynamic Movement Primitives 22
3.2 Generalization Process in PDMPs 26
3.2.1 Environmental Parameters 26
3.2.2 Mapping Function 26
3.3 Simulation Results 29
3.3.1 Two-dimensional Hurdling Motion 29
3.3.2 Cooperative Aerial Transportation 30
4 Motion Generalization with Safety Guarantee 36
4.1 Safety Criterion in Style Parameter 36
4.2 Demonstration Management 39
4.3 Simulation Validation 42
4.3.1 Two-dimensional Hurdling Motion 46
4.3.2 Cooperative Aerial Transportation 47
5 Motion Generalization for Complex Missions 51
5.1 Overall Structure of Seg-PDMPs 51
5.2 Motion Segments 53
5.3 Phase-decision Process 54
5.4 Seg-PDMPs for Single Phase 54
5.5 Simulation Results 55
5.5.1 Initial/terminal Offsets 56
5.5.2 Style Generalization 59
5.5.3 Recombination 61
6 Experimental Validation and Results 63
6.1 Cooperative Aerial Transportation 63
6.2 Cooperative Mobile Hang-dry Mission 70
6.2.1 Demonstrations 70
6.2.2 Simulation Validation 72
6.2.3 Experimental Results 78
7 Conclusions 82
Abstract (in Korean) 93Docto
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