MOTION PLANNING USING PREDICTED PERCEPTIVE CAPABILITY

2009 ◽  
Vol 06 (03) ◽  
pp. 435-457 ◽  
Author(s):  
PHILIPP MICHEL ◽  
JOEL CHESTNUTT ◽  
SATOSHI KAGAMI ◽  
KOICHI NISHIWAKI ◽  
JAMES J. KUFFNER ◽  
...  
Keyword(s):  
Motion Planning ◽  
Planning Process ◽  
Humanoid Robots ◽  
Experimental Results ◽  
Success Rates ◽  
Perception System ◽  
Task Success

We present an approach to motion planning for humanoid robots that aims to ensure reliable execution by augmenting the planning process to reason about the robot's ability to successfully perceive its environment during operation. By efficiently simulating the robot's perception system during search, our planner utilizes a perceptive capability metric that quantifies the 'sensability' of the environment in each state given the task to be accomplished. We have applied our method to the problem of planning robust autonomous grasping motions and walking sequences as performed by an HRP-2 humanoid. A fast GPU-accelerated 3D tracker is used for perception, with a grasp planner and footstep planner incorporating reasoning about the robot's perceptive capability. Experimental results show that considering information about the predicted perceptive capability ensures that sensing remains operational throughout the grasping or walking sequence and yields higher task success rates than perception-unaware planning.

Sampling-Based Methods for Motion Planning with Constraints

2018 ◽  
Vol 1 (1) ◽  
pp. 159-185 ◽  
Author(s):  
Zachary Kingston ◽  
Mark Moll ◽  
Lydia E. Kavraki
Keyword(s):  
Motion Planning ◽  
Degrees Of Freedom ◽  
Planning Process ◽  
Disaster Relief ◽  
Humanoid Robots ◽  
High Dimensional ◽  
Mobile Manipulators ◽  
Continuous Motion ◽  
Task Constraints ◽  
Planning Algorithms

Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms are effective for these high-dimensional systems; however, incorporating task constraints (e.g., keeping a cup level or writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based on two core primitive operations: ( a) sampling constraint-satisfying configurations and ( b) generating constraint-satisfying continuous motion. Although this article presents the basics of sampling-based planning for contextual background, it focuses on the representation of constraints and sampling-based planners that incorporate constraints.

scholarly journals A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199858
Author(s):  
Gianpaolo Gulletta ◽  
Eliana Costa e Silva ◽  
Wolfram Erlhagen ◽  
Ruud Meulenbroek ◽  
Maria Fernanda Pires Costa ◽  
...  
Keyword(s):  
Motor Control ◽  
Upper Limb ◽  
Planning Process ◽  
Computational Cost ◽  
Humanoid Robots ◽  
Efficient Manner ◽  
Daily Lives ◽  
Human Motor Control ◽  
Planning Algorithm ◽  
Human Motor

As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and motion are often considered key features for intuitive human–robot interactions because they allow human peers to easily predict the final intention of a robotic movement. Here, we present a novel motion planning algorithm, the Human-like Upper-limb Motion Planner, for the upper limb of anthropomorphic robots, that generates collision-free trajectories with human-like characteristics. Mainly inspired from established theories of human motor control, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions. For experimental validation, we generate arm-hand trajectories in a series of tasks including simple point-to-point reaching movements and sequential object-manipulation paradigms. Being a major contribution to the current literature, specific focus is on the kinematics of naturalistic arm movements during the avoidance of obstacles. To evaluate human-likeness, we observe kinematic regularities and adopt smoothness measures that are applied in human motor control studies to distinguish between well-coordinated and impaired movements. The results of this study show that the proposed algorithm is capable of planning arm-hand movements with human-like kinematic features at a computational cost that allows fluent and efficient human–robot interactions.

scholarly journals Efficient Planning of Humanoid Motions by Modifying Constraints

2013 ◽  
Vol 4 (1) ◽  
Author(s):  
ChangHyun Sung ◽  
Takahiro Kagawa ◽  
Yoji Uno
Keyword(s):  
Motion Planning ◽  
Sufficient Conditions ◽  
Computation Time ◽  
Humanoid Robots ◽  
Body Motion ◽  
Whole Body ◽  
Planning Problem ◽  
Point Representation ◽  
Efficient Planning ◽  
Unpredictable Environment

AbstractIn this paper, we propose an effective planning method for whole-body motions of humanoid robots under various conditions for achieving the task. In motion planning, various constraints such as range of motion have to be considered. Specifically, it is important to maintain balance in whole-body motion. In order to be useful in an unpredictable environment, rapid planning is an essential problem. In this research, via-point representation is used for assigning sufficient conditions to deal with various constraints in the movement. The position, posture and velocity of the robot are constrained as a state of a via-point. In our algorithm, the feasible motions are planned by modifying via-points. Furthermore, we formulate the motion planning problem as a simple iterative method with a Linear Programming (LP) problem for efficiency of the motion planning. We have applied the method to generate the kicking motion of a HOAP-3 humanoid robot. We confirmed that the robot can successfully score a goal with various courses corresponding to changing conditions of the location of an obstacle. The computation time was less than two seconds. These results indicate that the proposed algorithm can achieve efficient motion planning.

Supervoxel Plane Segmentation and Multi-Contact Motion Generation for Humanoid Stair Climbing

2017 ◽  
Vol 14 (01) ◽  
pp. 1650022 ◽  
Author(s):  
Tianwei Zhang ◽  
Stéphane Caron ◽  
Yoshihiko Nakamura
Keyword(s):  
Motion Planning ◽  
Humanoid Robot ◽  
Real Life ◽  
Estimation Method ◽  
Humanoid Robots ◽  
Stair Climbing ◽  
Lidar Data ◽  
Motion Generation ◽  
Prior Models ◽  
Motion Generator

Stair climbing is still a challenging task for humanoid robots, especially in unknown environments. In this paper, we address this problem from perception to execution. Our first contribution is a real-time plane-segment estimation method using Lidar data without prior models of the staircase. We then integrate this solution with humanoid motion planning. Our second contribution is a stair-climbing motion generator where estimated plane segments are used to compute footholds and stability polygons. We evaluate our method on various staircases. We also demonstrate the feasibility of the generated trajectories in a real-life experiment with the humanoid robot HRP-4.

Dynamics and Simulation of General Human and Humanoid Motion in Sports

2011 ◽  
pp. 36-62 ◽  
Author(s):  
Veljko Potkonjak ◽  
Miomir Vukobratovic ◽  
Kalman Babkovic ◽  
Branislav Borovac
Keyword(s):  
Mathematical Models ◽  
Motion Planning ◽  
Mathematical Analysis ◽  
Humanoid Robot ◽  
Dynamic Control ◽  
Humanoid Robots ◽  
Assistive Devices ◽  
Kinematics And Dynamics ◽  
Biologically Inspired ◽  
Biologically Inspired Robot

This chapter relates biomechanics to robotics. The mathematical models are derived to cover the kinematics and dynamics of virtually any motion of a human or a humanoid robot. Benefits for humanoid robots are seen in fully dynamic control and a general simulator for the purpose of system designing and motion planning. Biomechanics in sports and medicine can use these as a tool for mathematical analysis of motion and disorders. Better results in sports and improved diagnostics are foreseen. This work is a step towards the biologically-inspired robot control needed for a diversity of tasks expected in humanoids, and robotic assistive devices helping people to overcome disabilities or augment their physical potentials. This text deals mainly with examples coming from sports in order to justify this aspect of research.

Experiments in the Commutation and Motion Planning of a Spherical Stepper Motor

2000 ◽  
Author(s):  
David Stein ◽  
Gregory S. Chirikjian
Keyword(s):  
Motion Planning ◽  
Small Area ◽  
Permanent Magnets ◽  
Difficult Problem ◽  
Experimental Results ◽  
Stepper Motor ◽  
Stepper Motors

Abstract This paper addresses the commutation and motion planning of a spherical stepper motor in which the poles of the stator are electromagnets and the poles of the rotor (rotating ball) are permanent magnets. The stator only overlaps a very small area of the rotor, leading to a design that has a much wider range of unhindered motion than other spherical stepper motors in the literature. The problem with having a small stator is that it makes commutation and motion planning a difficult problem. We first explore the techniques and model used to construct the proper sequence of stator activations that result in the desired rotor trajectory. Experimental results are then presented for the model and prototype we have constructed.

scholarly journals Robot Motion Planning in an Unknown Environment with Danger Space

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 201 ◽  
Author(s):  
Hadi Jahanshahi ◽  
Mohsen Jafarzadeh ◽  
Naeimeh Najafizadeh Sari ◽  
Viet-Thanh Pham ◽  
Van Van Huynh ◽  
...  
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Markov Decision Processes ◽  
Free Space ◽  
Optimal Path ◽  
Humanoid Robots ◽  
Decision Processes ◽  
Inference System ◽  
Unknown Environment ◽  
Markov Decision

This paper discusses the real-time optimal path planning of autonomous humanoid robots in unknown environments regarding the absence and presence of the danger space. The danger is defined as an environment which is not an obstacle nor free space and robot are permitted to cross when no free space options are available. In other words, the danger can be defined as the potentially risky areas of the map. For example, mud pits in a wooded area and greasy floor in a factory can be considered as a danger. The synthetic potential field, linguistic method, and Markov decision processes are methods which have been reviewed for path planning in a free-danger unknown environment. The modified Markov decision processes based on the Takagi–Sugeno fuzzy inference system is implemented to reach the target in the presence and absence of the danger space. In the proposed method, the reward function has been calculated without the exact estimation of the distance and shape of the obstacles. Unlike other existing path planning algorithms, the proposed methods can work with noisy data. Additionally, the entire motion planning procedure is fully autonomous. This feature makes the robot able to work in a real situation. The discussed methods ensure the collision avoidance and convergence to the target in an optimal and safe path. An Aldebaran humanoid robot, NAO H25, has been selected to verify the presented methods. The proposed methods require only vision data which can be obtained by only one camera. The experimental results demonstrate the efficiency of the proposed methods.

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