Balance Preservation and Task Prioritization in Whole Body Motion Control of Humanoid Robots PDF Download
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Author: Alexander Sherikov
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
One of the greatest challenges in robot control is closing the gap between themotion capabilities of humans and humanoid robots. The difficulty lies in thecomplexity of the dynamical systems representing the said robots: theirnonlinearity, underactuation, discrete behavior due to collisions and friction,high number of degrees of freedom. Moreover, humanoid robots are supposed tooperate in non-deterministic environments, which require advanced real timecontrol. The currently prevailing approach to coping with these difficulties isto impose various limitations on the motions and employ approximate models ofthe robots. In this thesis, we follow the same line of research and propose anew approach to the design of balance preserving whole body motion controllers.The key idea is to leverage the advantages of whole body and approximate modelsby mixing them within a single predictive control problem with strictlyprioritized objectives.Balance preservation is one of the primary concerns in the control of humanoidrobots. Previous research has already established that anticipation of motionsis crucial for this purpose. We advocate that anticipation is helpful in thissense as a way to maintain capturability of the motion, i.e., the ability tostop. We stress that capturability of anticipated motions can be enforced withappropriate constraints. In practice, it is common to anticipate motions usingapproximate models in order to reduce computational effort, hence, a separatewhole body motion controller is needed for tracking. Instead, we propose tointroduce anticipation with an approximate model into the whole body motioncontroller. As a result, the generated whole body motions respect thecapturability constraints and the anticipated motions of an approximate modeltake into account whole body constraints and tasks. We pose our whole bodymotion controllers as optimization problems with strictly prioritizedobjectives. Though such prioritization is common in the literature, we believethat it is often not properly exploited. We, therefore, propose severalexamples of controllers, where prioritization is useful and necessary toachieve desired behaviors. We evaluate our controllers in two simulatedscenarios, where a whole body task influences walking motions of the robot andthe robot optionally exploits a hand contact to maintain balance whilestanding.
Author: Alexander Sherikov
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
One of the greatest challenges in robot control is closing the gap between themotion capabilities of humans and humanoid robots. The difficulty lies in thecomplexity of the dynamical systems representing the said robots: theirnonlinearity, underactuation, discrete behavior due to collisions and friction,high number of degrees of freedom. Moreover, humanoid robots are supposed tooperate in non-deterministic environments, which require advanced real timecontrol. The currently prevailing approach to coping with these difficulties isto impose various limitations on the motions and employ approximate models ofthe robots. In this thesis, we follow the same line of research and propose anew approach to the design of balance preserving whole body motion controllers.The key idea is to leverage the advantages of whole body and approximate modelsby mixing them within a single predictive control problem with strictlyprioritized objectives.Balance preservation is one of the primary concerns in the control of humanoidrobots. Previous research has already established that anticipation of motionsis crucial for this purpose. We advocate that anticipation is helpful in thissense as a way to maintain capturability of the motion, i.e., the ability tostop. We stress that capturability of anticipated motions can be enforced withappropriate constraints. In practice, it is common to anticipate motions usingapproximate models in order to reduce computational effort, hence, a separatewhole body motion controller is needed for tracking. Instead, we propose tointroduce anticipation with an approximate model into the whole body motioncontroller. As a result, the generated whole body motions respect thecapturability constraints and the anticipated motions of an approximate modeltake into account whole body constraints and tasks. We pose our whole bodymotion controllers as optimization problems with strictly prioritizedobjectives. Though such prioritization is common in the literature, we believethat it is often not properly exploited. We, therefore, propose severalexamples of controllers, where prioritization is useful and necessary toachieve desired behaviors. We evaluate our controllers in two simulatedscenarios, where a whole body task influences walking motions of the robot andthe robot optionally exploits a hand contact to maintain balance whilestanding.
Author: Bernd Henze
Publisher: Springer Nature
ISBN: 3030872122
Category : Technology & Engineering
Languages : en
Pages : 209
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Book Description
This book aims at providing algorithms for balance control of legged, torque-controlled humanoid robots. A humanoid robot normally uses the feet for locomotion. This paradigm is extended by addressing the challenge of multi-contact balancing, which allows a humanoid robot to exploit an arbitrary number of contacts for support. Using multiple contacts increases the size of the support polygon, which in turn leads to an increased robustness of the stance and to an increased kinematic workspace of the robot. Both are important features for facilitating a transition of humanoid robots from research laboratories to real-world applications, where they are confronted with multiple challenging scenarios, such as climbing stairs and ladders, traversing debris, handling heavy loads, or working in confined spaces. The distribution of forces and torques among the multiple contacts is a challenging aspect of the problem, which arises from the closed kinematic chain given by the robot and its environment.
Author: Kensuke Harada
Publisher: Springer Science & Business Media
ISBN: 1849962200
Category : Technology & Engineering
Languages : en
Pages : 320
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Book Description
Research on humanoid robots has been mostly with the aim of developing robots that can replace humans in the performance of certain tasks. Motion planning for these robots can be quite difficult, due to their complex kinematics, dynamics and environment. It is consequently one of the key research topics in humanoid robotics research and the last few years have witnessed considerable progress in the field. Motion Planning for Humanoid Robots surveys the remarkable recent advancement in both the theoretical and the practical aspects of humanoid motion planning. Various motion planning frameworks are presented in Motion Planning for Humanoid Robots, including one for skill coordination and learning, and one for manipulating and grasping tasks. The problem of planning sequences of contacts that support acyclic motion in a highly constrained environment is addressed and a motion planner that enables a humanoid robot to push an object to a desired location on a cluttered table is described. The main areas of interest include: • whole body motion planning, • task planning, • biped gait planning, and • sensor feedback for motion planning. Torque-level control of multi-contact behavior, autonomous manipulation of moving obstacles, and movement control and planning architecture are also covered. Motion Planning for Humanoid Robots will help readers to understand the current research on humanoid motion planning. It is written for industrial engineers, advanced undergraduate and postgraduate students.
Author: Diego Torricelli
Publisher: Frontiers Media SA
ISBN: 2889632709
Category :
Languages : en
Pages : 217
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Author: Tadej Petric
Publisher: Frontiers Media SA
ISBN: 2889632652
Category :
Languages : en
Pages : 129
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Book Description
Author: Kaul, Lukas Sebastian
Publisher: KIT Scientific Publishing
ISBN: 3731509032
Category : Computers
Languages : en
Pages : 258
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Book Description
Robustly maintaining balance on two legs is an important challenge for humanoid robots. The work presented in this book represents a contribution to this area. It investigates efficient methods for the decision-making from internal sensors about whether and where to step, several improvements to efficient whole-body postural balancing methods, and proposes and evaluates a novel method for efficient recovery step generation, leveraging human examples and simulation-based reinforcement learning.
Author: Oscar Efrain Ramos Ponce
Publisher:
ISBN:
Category :
Languages : en
Pages : 137
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Book Description
This thesis aims at providing a solution to the problem of motion generation for humanoid robots. The proposed framework generates whole-body motion using the complete robot dynamics in the task space satisfying contact constraints. This approach is known as operational-space inverse-dynamics control. The specification of the movements is done through objectives in the task space, and the high redundancy of the system is handled with a prioritized stack of tasks where lower priority tasks are only achieved if they do not interfere with higher priority ones. To this end, a hierarchical quadratic program is used, with the advantage of being able to specify tasks as equalities or inequalities at any level of the hierarchy. Motions where the robot sits down in an armchair and climbs a ladder show the capability to handle multiple non-coplanar contacts. The generic motion generation framework is then applied to some case studies using HRP-2 and Romeo. Complex and human-like movements are achieved using human motion imitation where the acquired motion passes through a kinematic and then dynamic retargeting processes. To deal with the instantaneous nature of inverse dynamics, a walking pattern generator is used as an input for the stack of tasks which makes a local correction of the feet position based on the contact points allowing to walk on non-planar surfaces. Visual feedback is also introduced to aid in the walking process. Alternatively, for a fast balance recovery, the capture point is introduced in the framework as a task and it is controlled within a desired region of space. Also, motion generation is presented for CHIMP which is a robot that needs a particular treatment.
Author: Taizo Yoshikawa
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
A major obstacle that prevents humanoid robots from accomplishing real world tasks is their inability to physically interact with, and effectively manipulate, the most common objects generally found in human environments. Even tasks that seem simple for a human remain a significant challenge for most robots. Robots generally employ precision to perform a manipulation task. Humans, in contrast, employ compliance through tactile and force feedback to overcome their imprecision, allowing them to resolve uncertainties associated with the task. The lack of compliance and force control has been indeed a major limiting factor in the ability of robots to interact and manipulate in human environments. One of the major objectives of this research is to endow humanoid robots with whole-body compliant motion abilities. With compliance, a robot overcomes position uncertainties by moving in directions that reduce contact forces, which in turn directs it towards its goal. Whole-body framework was designed to allow the robot to compliantly interact with its environment at multiple contact points. The synthesis of compliant tasks is greatly simplified by being independent of postures and constraints, which are automatically integrated in the control hierarchy. This research focuses on the development of (I) sensor-based whole-body compliant motion primitives, (II) contact sensing and contact force control, (III) whole-body multi-contact for extended support, kneeling, crawling, leaning table, and locomotion strategy to improve support in unstructured terrains, (IV) dynamic collision-free motion planning and (V) dynamic collision-free walking path planning.
Author: 李泓逸
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Jaemin Lee (Ph. D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The fundamental objective of robotics is to enhance the productivity of humans while interacting in potentially unstructured environments. In this sense, Human-centered robots must be fast, stable, and robust when performing varied and complicated tasks during mission execution. Although industrial robots have seen some advancements regarding motion planning and control, they are largely limited to simple pre-defined tasks in structured environments. However, to achieve highly dynamic motions for dexterous manipulation or agile locomotion in complex robots, we need to consider the use of nonlinear dynamics, complex constraints, multiple contacts, disturbances, and uncertainties. These are fundamental requirements needed to advance the use of general purpose robots dynamically interacting in a wider variety of environments. Therefore, this thesis addresses challenges that arise from the employment of optimization techniques and sophisticated realtime algorithms for the control and deployment of realistic and practical robots in human environments. Considering the above challenges, we propose efficient trajectory generation and trajectory tracking methods as the next paradigms for whole-body control (WBC). First, we formulate a class of motion planning problems to directly obtain dynamically feasible state trajectories in multi-contact robots and the corresponding control inputs. Typically, it takes a tremendous amount of time to solve the end-to-end trajectory generation problem using large-scale standard Nonlinear Programming (NLP). We propose a new sampling-based method together with a Partially Observable Markov Decision Process to break down the trajectory generation problem into tractable parts. In doing so, the number of decision variables is drastically reduced. As a result, we solve the optimization problem much faster than using existing NLP techniques. In addition, we incorporate reachability analysis tools for determining whether the planned trajectories are reachable and discard unfeasible trajectories during optimization. Because simplified models are frequently utilized in locomotion studies to generate walking patterns, planned contact locations may not be feasible due to model mismatch and robot constraints. In contrast, our method enables the generation of dynamically feasible trajectories to reach planned contact location considering full-body dynamics and realistic constraints. The proposed methods are applied to contact constrained manipulation and bipedal locomotion problems to enhance capabilities of robots maneuvering in complex environments without slip or loss of balance. Second, we explore the fundamentals of WBC and use this insight to push forward the capabilities of WBC approaches. One of the problems we explore is the verification of stability of legged robots under unknown external perturbations. In such cases, the closed-loop control system controlled by WBC approaches may become unstable if external perturbations are not properly analyzed with stability verification. To verify stability, we leverage the so-called Centroidal Dynamics of legged robots and a type of WBC dubbed Whole-Body Locomotion Control (WBLC). Using a feedback-linearized state-space model, we obtain appropriate feedback gains for WBC to make our robot stable and robust under perturbations. Another challenge of WBC stems from the reliance on classical feedback control theory. Classical PD control is unsuitable for a noisy system, therefore WBC cannot be directly applied to stochastic systems. Classical WBC approaches do not consider the covariance of the terminal states as constraints which is a more efficient way to control robots with precision. We propose a new control approach, called Hierarchical Covariance Control (HCC) to enforce covariance constraints. Our proposed HCC is a stochastic version of WBC to decrease task errors when uncertainty is substantial. The last improvement I explore regarding WBC is the employment of Model Predictive Control (MPC) instead of solving an instantaneous optimization problem, which cannot guarantee global optimality. As such, we consider longer receding time horizons for MPC, thus improving the tracking performance by reducing the accumulated error norm while executing hierarchical tasks. Overall, our research focuses on the end-to-end process spanning trajectory planning to feedback control enabling the generating of multi-contact and constrained dynamic motions of complex robots operating in realistic setups. The various contributions of this thesis are in the areas of computational efficiency for whole-body trajectory generation, robustness of WBC control algorithms, and significant improvements in trajectory tracking using WBC algorithms. We verify the proposed approaches both in simulations and real experiments using various robotic systems
