2,927 research outputs found
Time-Optimal Trajectories for Cooperative Multi-Manipulator Systems
In this paper we present two schemes for planning the time-optimal trajectory for cooperative multi-manipulator system(CMMS) carrying a common object. We assume that the desired path is given and parameterizable by an arclength variable. Both approaches take into account the dynamics of the manipulators and the dynamics of the object. The first approach employs linear programming techniques, and it allows us to obtain the timeoptimal execution of the given task utilizing the maximum torque capacities of the joint motors. The second approach is a sub-time-optimal method which is computationally very efficient. In the second approach we try to divide the given load into a share for each manipulator in the CMMS in a manner in which the trajectory acceleration/deceleration is maximized, hence the trajectory execution time is minimized. This load distribution approach uses optimization schemes which degenerate to a linear search algorithm for the case of two robots manipulating a common load, and this results in significant savings on the computation time. The load distribution scheme not only enables us to reduce the computation time but also gives us the possibility of applying this method in real time planning and control of CMMS. Further, we show that under certain object trajectories the load distribution scheme yields truly time-optimal trajectories
Trajectory generation of space telerobots
The purpose is to review a variety of trajectory generation techniques which may be applied to space telerobots and to identify problems which need to be addressed in future telerobot motion control systems. As a starting point for the development of motion generation systems for space telerobots, the operation and limitations of traditional path-oriented trajectory generation approaches are discussed. This discussion leads to a description of more advanced techniques which have been demonstrated in research laboratories, and their potential applicability to space telerobots. Examples of this work include systems that incorporate sensory-interactive motion capability and optimal motion planning. Additional considerations which need to be addressed for motion control of a space telerobot are described, such as redundancy resolution and the description and generation of constrained and multi-armed cooperative motions. A task decomposition module for a hierarchical telerobot control system which will serve as a testbed for trajectory generation approaches which address these issues is also discussed briefly
Cooperative Adaptive Control for Cloud-Based Robotics
This paper studies collaboration through the cloud in the context of
cooperative adaptive control for robot manipulators. We first consider the case
of multiple robots manipulating a common object through synchronous centralized
update laws to identify unknown inertial parameters. Through this development,
we introduce a notion of Collective Sufficient Richness, wherein parameter
convergence can be enabled through teamwork in the group. The introduction of
this property and the analysis of stable adaptive controllers that benefit from
it constitute the main new contributions of this work. Building on this
original example, we then consider decentralized update laws, time-varying
network topologies, and the influence of communication delays on this process.
Perhaps surprisingly, these nonidealized networked conditions inherit the same
benefits of convergence being determined through collective effects for the
group. Simple simulations of a planar manipulator identifying an unknown load
are provided to illustrate the central idea and benefits of Collective
Sufficient Richness.Comment: ICRA 201
A Nonlinear Model Predictive Control Scheme for Cooperative Manipulation with Singularity and Collision Avoidance
This paper addresses the problem of cooperative transportation of an object
rigidly grasped by robotic agents. In particular, we propose a Nonlinear
Model Predictive Control (NMPC) scheme that guarantees the navigation of the
object to a desired pose in a bounded workspace with obstacles, while complying
with certain input saturations of the agents. Moreover, the proposed
methodology ensures that the agents do not collide with each other or with the
workspace obstacles as well as that they do not pass through singular
configurations. The feasibility and convergence analysis of the NMPC are
explicitly provided. Finally, simulation results illustrate the validity and
efficiency of the proposed method.Comment: Simulation results with 3 agents adde
The KALI multi-arm robot programming and control environment
The KALI distributed robot programming and control environment is described within the context of its use in the Jet Propulsion Laboratory (JPL) telerobot project. The purpose of KALI is to provide a flexible robot programming and control environment for coordinated multi-arm robots. Flexibility, both in hardware configuration and software, is desired so that it can be easily modified to test various concepts in robot programming and control, e.g., multi-arm control, force control, sensor integration, teleoperation, and shared control. In the programming environment, user programs written in the C programming language describe trajectories for multiple coordinated manipulators with the aid of KALI function libraries. A system of multiple coordinated manipulators is considered within the programming environment as one motion system. The user plans the trajectory of one controlled Cartesian frame associated with a motion system and describes the positions of the manipulators with respect to that frame. Smooth Cartesian trajectories are achieved through a blending of successive path segments. The manipulator and load dynamics are considered during trajectory generation so that given interface force limits are not exceeded
λͺ¨μ ν리머ν°λΈλ₯Ό μ΄μ©ν 볡μ‘ν λ‘λ΄ μ무 νμ΅ λ° μΌλ°ν κΈ°λ²
νμλ
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€.
λ³Έ λ
Όλ¬Έμ LfD κΈ°λ² μ€ λͺ¨μ
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€.
λ³Έ λ
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€.
λν λ³Έ λ
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
- β¦