5,842 research outputs found

    Research and development at ORNL/CESAR towards cooperating robotic systems for hazardous environments

    Get PDF
    One of the frontiers in intelligent machine research is the understanding of how constructive cooperation among multiple autonomous agents can be effected. The effort at the Center for Engineering Systems Advanced Research (CESAR) at the Oak Ridge National Laboratory (ORNL) focuses on two problem areas: (1) cooperation by multiple mobile robots in dynamic, incompletely known environments; and (2) cooperating robotic manipulators. Particular emphasis is placed on experimental evaluation of research and developments using the CESAR robot system testbeds, including three mobile robots, and a seven-axis, kinematically redundant mobile manipulator. This paper summarizes initial results of research addressing the decoupling of position and force control for two manipulators holding a common object, and the path planning for multiple robots in a common workspace

    λͺ¨μ…˜ ν”„λ¦¬λ¨Έν‹°λΈŒλ₯Ό μ΄μš©ν•œ λ³΅μž‘ν•œ λ‘œλ΄‡ μž„λ¬΄ ν•™μŠ΅ 및 μΌλ°˜ν™” 기법

    Get PDF
    ν•™μœ„λ…Όλ¬Έ (박사) -- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ ν•­κ³΅μš°μ£Όκ³΅ν•™κ³Ό, 2020. 8. κΉ€ν˜„μ§„.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.μ‹œμ—° ν•™μŠ΅ 기법(Learning from demonstrations, LfD)은 λ‘œλ΄‡μ΄ νŠΉμ • λ™μž‘μ„ μˆ˜ν–‰ν•  수 μžˆλ„λ‘ ν•˜λŠ” μœ λ§ν•œ λ™μž‘ 생성 기법이닀. λ‘œλ΄‡ μ‘°μž‘κΈ°κ°€ 인간 μ‚¬νšŒμ—μ„œ λ‹€μ–‘ν•œ 업무λ₯Ό λŒ€μ²΄ν•΄ 감에 따라, λ‹€μ–‘ν•œ μž„λ¬΄λ₯Ό μˆ˜ν–‰ν•˜λŠ” λ‘œλ΄‡μ˜ λ™μž‘μ„ μƒμ„±ν•˜κΈ° μœ„ν•΄ LfD μ•Œκ³ λ¦¬μ¦˜λ“€μ€ 널리 μ—°κ΅¬λ˜κ³ , μ‚¬μš©λ˜κ³  μžˆλ‹€. λ³Έ 논문은 LfD 기법 쀑 λͺ¨μ…˜ ν”„λ¦¬λ¨Έν‹°λΈŒ 기반의 λ™μž‘ μž¬μƒμ„± μ•Œκ³ λ¦¬μ¦˜μΈ Parametric dynamic movement primitives(PDMP)에 κΈ°μ΄ˆν•œ μ•Œκ³ λ¦¬μ¦˜μ„ μ œμ‹œν•˜λ©°, 이λ₯Ό 톡해 λ‹€μ–‘ν•œ μž„λ¬΄λ₯Ό μˆ˜ν–‰ν•˜λŠ” λͺ¨λ°”일 μ‘°μž‘κΈ°μ˜ ꢀ적을 μƒμ„±ν•œλ‹€. 기쑴의 λ™μž‘ μž¬μƒμ„± μ•Œκ³ λ¦¬μ¦˜κ³Ό 달리, 이 μ—°κ΅¬λŠ” 제곡된 μ‹œμ—°μ—μ„œ ν‘œν˜„λœ λ™μž‘μ„ λ‹¨μˆœνžˆ μž¬μƒμ„±ν•˜λŠ” 것에 κ·ΈμΉ˜μ§€ μ•Šκ³ , μƒˆλ‘œμš΄ ν™˜κ²½μ— 맞게 μΌλ°˜ν™” ν•˜λŠ” 과정을 ν¬ν•¨ν•œλ‹€. 이 λ…Όλ¬Έμ—μ„œ μ œμ‹œν•˜λŠ” μΌλ°˜ν™” 과정은 PDMPs의 λ‚΄λΆ€ νŒŒλΌλ―Έν„° 값인 μŠ€νƒ€μΌ νŒŒλΌλ―Έν„°μ™€ ν™˜κ²½ λ³€μˆ˜ μ‚¬μ΄μ˜ λΉ„μ„ ν˜• 관계λ₯Ό κ°€μš°μŠ€ νšŒκ·€ 기법 (Gaussian process regression, GPR)을 μ΄μš©ν•˜μ—¬ μˆ˜μ‹μ μœΌλ‘œ ν‘œν˜„ν•œλ‹€. μ œμ•ˆλœ 기법은 λ˜ν•œ 졜적 μ‹œμ—°λ₯Ό ν•™μŠ΅ν•˜λŠ” 방식을 톡해 κ°•λ ₯ν•œ 졜적 μ‹€μ‹œκ°„ 경둜 κ³„νš κΈ°λ²•μœΌλ‘œλ„ μ‘μš©λ  수 μžˆλ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ” λ˜ν•œ λ‘œλ΄‡μ˜ ꡬ동 μ•ˆμ „μ„±λ„ κ³ λ €ν•œλ‹€. κΈ°μ‘΄ μ—°κ΅¬λ“€μ—μ„œ 닀루어진 μ‹œμ—° 관리 기술이 λ‘œλ΄‡μ˜ ꡬ동 νš¨μœ¨μ„±μ„ κ°œμ„ ν•˜λŠ” λ°©ν–₯으둜 μ œμ‹œλœ 것과 달리, 이 μ—°κ΅¬λŠ” κ°•ν•œ κ΅¬μ†μ‘°κ±΄μœΌλ‘œ λ‘œλ΄‡μ˜ ꡬ동 μ•ˆμ „μ„±μ„ ν™•λ³΄ν•˜λŠ” μ‹œμ—° 관리 κΈ°μˆ μ„ 톡해 μ•ˆμ •μ„±μ„ κ³ λ €ν•˜λŠ” μƒˆλ‘œμš΄ 방식을 μ œμ‹œν•œλ‹€. μ œμ•ˆλœ 방식은 μŠ€νƒ€μΌ νŒŒλΌλ―Έν„° κ°’ μƒμ—μ„œ μ•ˆμ „μ„± 기쀀을 κ³„μ‚°ν•˜λ©°, 이 μ•ˆμ „ 기쀀을 톡해 μ‹œμ—°μ„ μ œκ±°ν•˜λŠ” 일련의 μž‘μ—…μ„ μˆ˜ν–‰ν•œλ‹€. λ˜ν•œ, 제거된 μ‹œμœ„λ₯Ό μ•ˆμ „ 기쀀에 따라 μž…μ¦λœ μ‹œμœ„λ‘œ λŒ€μ²΄ν•˜μ—¬ μΌλ°˜ν™” μ„±λŠ₯을 μ €ν•˜μ‹œν‚€μ§€ μ•Šλ„λ‘ μ‹œμœ„λ₯Ό κ΄€λ¦¬ν•œλ‹€. 이λ₯Ό 톡해 λ‹€μˆ˜μ˜ μ‹œμ—° 각각 κ°œλ³„ λ™μž‘ μ•ˆμ „μ„± 뿐 μ•„λ‹ˆλΌ 온라인 λ™μž‘μ˜ μ•ˆμ „μ„±κΉŒμ§€ κ³ λ €ν•  수 있으며, μ‹€μ‹œκ°„ λ‘œλ΄‡ μ‘°μž‘κΈ° μš΄μš©μ‹œ μ•ˆμ „μ„±μ΄ 확보될 수 μžˆλ‹€. μ œμ•ˆλœ μ•ˆμ •μ„±μ„ κ³ λ €ν•œ μ‹œμ—° 관리 κΈ°μˆ μ€ λ˜ν•œ ν™˜κ²½μ˜ 정적 섀정이 λ³€κ²½λ˜μ–΄ λͺ¨λ“  μ‹œμ—°μ„ ꡐ체해야 ν•  수 μžˆλŠ” μƒν™©μ—μ„œ μ‚¬μš©ν•  수 μžˆλŠ” μ‹œμ—°λ“€μ„ νŒλ³„ν•˜κ³ , 효율적으둜 μž¬μ‚¬μš©ν•˜λŠ” 데 μ‘μš©ν•  수 μžˆλ‹€. λ˜ν•œ λ³Έ 논문은 λ³΅μž‘ν•œ μž„λ¬΄μ—μ„œ 적용될 수 μžˆλŠ” PDMPs의 ν™•μž₯ 기법인 seg-PDMPsλ₯Ό μ œμ‹œν•œλ‹€. 이 접근방식은 λ³΅μž‘ν•œ μž„λ¬΄κ°€ 일반적으둜 볡수개의 κ°„λ‹¨ν•œ ν•˜μœ„ μž‘μ—…μœΌλ‘œ κ΅¬μ„±λœλ‹€κ³  κ°€μ •ν•œλ‹€. κΈ°μ‘΄ PDMPs와 달리 seg-PDMPsλŠ” 전체 ꢀ적을 ν•˜μœ„ μž‘μ—…μ„ λ‚˜νƒ€λ‚΄λŠ” μ—¬λŸ¬ 개의 λ‹¨μœ„ λ™μž‘μœΌλ‘œ λΆ„ν• ν•˜κ³ , 각 λ‹¨μœ„λ™μž‘μ— λŒ€ν•΄ μ—¬λŸ¬κ°œμ˜ PDMPsλ₯Ό κ΅¬μ„±ν•œλ‹€. 각 λ‹¨μœ„ λ™μž‘ λ³„λ‘œ μƒμ„±λœ PDMPsλŠ” ν†΅ν•©λœ ν”„λ ˆμž„μ›Œν¬λ‚΄μ—μ„œ 단계 κ²°μ • ν”„λ‘œμ„ΈμŠ€λ₯Ό 톡해 μžλ™μ μœΌλ‘œ ν˜ΈμΆœλœλ‹€. 각 단계 λ³„λ‘œ λ‹¨μœ„ λ™μž‘μ„ μˆ˜ν–‰ν•˜κΈ° μœ„ν•œ μ‹œκ°„ 및 ν•˜μœ„ λͺ©ν‘œμ μ€ κ°€μš°μŠ€ 곡정 νšŒκ·€(GPR)λ₯Ό μ΄μš©ν•œ ν™˜κ²½λ³€μˆ˜μ™€μ˜μ˜ 관계식을 톡해 μ–»λŠ”λ‹€. 결과적으둜, 이 μ—°κ΅¬λŠ” μ „μ²΄μ μœΌλ‘œ μš”κ΅¬λ˜λŠ” μ‹œμ—°μ˜ 수λ₯Ό 효과적으둜 쀄일 뿐 μ•„λ‹ˆλΌ, 각 λ‹¨μœ„λ™μž‘μ˜ ν‘œν˜„ μ„±λŠ₯을 κ°œμ„ ν•œλ‹€. μ œμ•ˆλœ μ•Œκ³ λ¦¬μ¦˜μ€ ν˜‘λ™ λͺ¨λ°”일 λ‘œλ΄‡ μ‘°μž‘κΈ° μ‹€ν—˜μ„ ν†΅ν•˜μ—¬ κ²€μ¦λœλ‹€. μ„Έ κ°€μ§€μ˜ μ‹œλ‚˜λ¦¬μ˜€κ°€ λ³Έ λ…Όλ¬Έμ—μ„œ 닀루어지며, 항곡 μš΄μ†‘κ³Ό κ΄€λ ¨λœ 첫 두 가지 μ‹œλ‚˜λ¦¬μ˜€λŠ” PDMPs 기법이 λ‘œλ΄‡ μ‘°μž‘κΈ°μ—μ„œ λΉ λ₯Έ 적응성, μž„λ¬΄ νš¨μœ¨μ„±κ³Ό μ•ˆμ „μ„± λͺ¨λ‘ λ§Œμ‘±ν•˜λŠ” 것을 μž…μ¦ν•œλ‹€. λ§ˆμ§€λ§‰ μ‹œλ‚˜λ¦¬μ˜€λŠ” 지상 μ°¨λŸ‰μ„ μ΄μš©ν•œ 두 개의 λ‘œλ΄‡ μ‘°μž‘κΈ°μ— λŒ€ν•œ μ‹€ν—˜μœΌλ‘œ λ³΅μž‘ν•œ μž„λ¬΄ μˆ˜ν–‰μ„ ν•˜κΈ° μœ„ν•΄ ν™•μž₯된 기법인 seg-PDMPsκ°€ 효과적으둜 λ³€ν™”ν•˜λŠ” ν™˜κ²½μ—μ„œ μΌλ°˜ν™”λœ λ™μž‘μ„ 생성함을 κ²€μ¦ν•œλ‹€.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

    Coordinated task manipulation by nonholonomic mobile robots

    Get PDF
    Coordinated task manipulation by a group of autonomous mobile robots has received signicant research effort in the last decade. Previous studies in the area revealed that one of the main problems in the area is to avoid the collisions of the robots with obstacles as well as with other members of the group. Another problem is to come up with a model for successful task manipulation. Signicant research effort has accumulated on the denition of forces to generate reference trajectories for each autonomous mobile robots engaged in coordinated behavior. If the mobile robots are nonholonomic, this approach fails to guarantee successful manipulation of the task since the so-generated reference trajectories might not satisfy the nonholonomic constraint. In this work, we introduce a novel coordinated task manipulation model inclusive of an online collision avoidance algorithm. The reference trajectory for each autonomous nonholonomic mobile robot is generated online in terms of linear and angular velocity references for the robot; hence these references automatically satisfy the nonholonomic constraint. The generated reference velocities inevitably depend on the nature of the specied coordinated task. Several coordinated task examples, on the basis of a generic task, have been presented and the proposed model is veried through simulations

    A Novel Graph-based Motion Planner of Multi-Mobile Robot Systems with Formation and Obstacle Constraints

    Full text link
    Multi-mobile robot systems show great advantages over one single robot in many applications. However, the robots are required to form desired task-specified formations, making feasible motions decrease significantly. Thus, it is challenging to determine whether the robots can pass through an obstructed environment under formation constraints, especially in an obstacle-rich environment. Furthermore, is there an optimal path for the robots? To deal with the two problems, a novel graphbased motion planner is proposed in this paper. A mapping between workspace and configuration space of multi-mobile robot systems is first built, where valid configurations can be acquired to satisfy both formation constraints and collision avoidance. Then, an undirected graph is generated by verifying connectivity between valid configurations. The breadth-first search method is employed to answer the question of whether there is a feasible path on the graph. Finally, an optimal path will be planned on the updated graph, considering the cost of path length and formation preference. Simulation results show that the planner can be applied to get optimal motions of robots under formation constraints in obstacle-rich environments. Additionally, different constraints are considered

    Swarm Robotics: An Extensive Research Review

    Get PDF
    • …
    corecore