Fast Humanoid Robot Collision-Free Footstep Planning Using Swept Volume Approximations

Perrin, Nicolás; Stasse, Olivier; Baudouin, Léo; Lamiraux, Florent; Yoshida, Eiichi

doi:10.1109/tro.2011.2172152

Cited by 102 publications

(89 citation statements)

References 36 publications

(43 reference statements)

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“…To do so we use an approach similar to the one introduced in [5] where swept volume approximations are precomputed offline. The principle follows this remark: after the smoothing process, the final trajectory of one step or half-step of the robot depends on many parameters (smoothing parameters and parameters of the current and next half-steps), but before the smoothing, when the sequence of half-step is "raw", the trajectory of the robot locally only depends on the parameters of the current half-step.…”

Section: B Collision Detectionmentioning

confidence: 99%

“…So what we do is that we decide a finite set of half-steps in advance (about 200), and we will use only these half-steps to produce sequences of walk. There are two types of half-steps (see [5]): upward and downward halfsteps, and roughly for N upward half-steps and N downward half-steps, it is possible to generate N × N different single steps. Thus, with only a relatively limited number of halfsteps we have a large number of available steps, so it gives us a very good expressiveness compared with methods where only 15 to 30 steps are considered [1].…”

Section: B Collision Detectionmentioning

confidence: 99%

“…For the upper part of the robot we are less precise and use rough bounding boxes. In [5], the swept volume approximation are stored in tree structures and designed to be efficiently tested against points of the environment, but in our implementation we use these tree structures to first build triangle meshes representing the swept volume approximations, and then use the PQP algorithm [9] to check collisions against triangle meshes in the environment.…”

Section: B Collision Detectionmentioning

confidence: 99%

“…During the planning phase, we use a discretized version of RRT [10] similar to the one used in [5] to search for a collision-free sequence of half-steps. This method is ad hoc and not yet completely satisfying as RRT is not very well suited for discrete footstep planning, but since we have about 200 half-steps, this approach is overall better than an A* search whose performances quickly dwindle when the number of possible actions increases.…”

Section: Footstep Planningmentioning

confidence: 99%

“…Recently interactive 3D navigation by humanoid [3] [4] has been reported, but it is for static environments. In this paper, we consider 3D moving obstacles and handle the collision avoidance with the legs in an accurate way, based on fast motion planning with precomputed dense swept volumes [5], whereas only sparse finite footsteps are considered in [3] [4]. In the following sections we describe the algorithms and software architecture that enabls realtime planning and replanning with the humanoid robot HRP-2 in an environment where obstacles are sensed through motion capture.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Real-time replanning using 3D environment for humanoid robot

Baudouin

Perrin

Moulard

et al. 2011

2011 11th IEEE-RAS International Conference on Humanoid Robots

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Abstract-In this paper, we illustrate experimentally an original real-time replanning scheme and architecture for humanoid robot reactive walking. Based on a dense set of actions, our approach uses a large panel of the humanoid robot capabilities and is particularly well suited for 3D collision avoidance. Indeed A-* approaches becomes difficult in such situation, thus the method demonstrated here relies on RRT. Combined with an approximation of the volume swept by the robot legs while walking, our method is able to cope with 3D obstacles while maintaining real-time computation. We experimentally validate our approach on the robot HRP-2.

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Section: B Collision Detectionmentioning

confidence: 99%

Section: B Collision Detectionmentioning

confidence: 99%

Section: B Collision Detectionmentioning

confidence: 99%

Section: Footstep Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Real-time replanning using 3D environment for humanoid robot

Baudouin

Perrin

Moulard

et al. 2011

2011 11th IEEE-RAS International Conference on Humanoid Robots

Self Cite

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Motion planning for tree climbing with inchworm‐like robots

Lam

2012

Journal of Field Robotics

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This paper proposes a global path‐ and motion‐planning algorithm that enables inchworm‐like robots to navigate their way up tree branches. The intuitive climbing space representation method proposed here greatly simplifies the path‐planning problem. The dynamic programming algorithm can be used to identify the optimal path leading to the target position in the target direction according to the constraints and requirements specified. The planned path can be applied in any tree‐climbing robot that utilizes the nonenclosure gripping method. An efficient motion‐planning algorithm for continuum inchworm‐like robots is then developed to enable them to climb along the planned path with a high degree of accuracy. In comparison with the method proposed in our previous study, the method proposed herein significantly improves consistency between the planned path and the motions of the robot, and therefore makes it more practical to implement the motion‐planning algorithm in trees of different shapes. The paper also describes hardware experiments in which the proposed planning algorithm is applied to enable inchworm‐like robots to climb real trees, thus validating the proposed planning algorithm in practice. © 2012 Wiley Periodicals, Inc.

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Adaptive Motion Planning for Autonomous Rough Terrain Traversal with a Walking Robot

Belter

Łabźcki

Skrzypczyński

2015

Journal of Field Robotics

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Achieving full autonomy in a mobile robot requires combining robust environment perception with onboard sensors, efficient environment mapping, and real‐time motion planning. All these tasks become more challenging when we consider a natural, outdoor environment and a robot that has many degrees of freedom (DOF). In this paper, we address the issues of motion planning in a legged robot walking over a rough terrain, using only its onboard sensors to gather the necessary environment model. The proposed solution takes the limited perceptual capabilities of the robot into account. A multisensor system is considered for environment perception. The key idea of the motion planner is to use the dual representation concept of the map: (i) a higher‐level planner applies the A* algorithm for coarse path planning on a low‐resolution elevation grid, and (ii) a lower‐level planner applies the guided‐RRT (rapidly exploring random tree) algorithm to find a sequence of feasible motions on a more precise but smaller map. This paper contributes a new method that can identify the terrain traversability cost to the benefit of the A* algorithm. A probabilistic regression technique is applied for the traversability assessment with the typical RRT‐based motion planner used to explore the space of traversability values. The efficiency of our motion planning approach is demonstrated in simulations that provide ground truth data unavailable in field tests. However, the simulation‐verified approach is then thoroughly tested under real‐world conditions in experiments with two six‐legged walking robots having different perception systems.

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Fast Humanoid Robot Collision-Free Footstep Planning Using Swept Volume Approximations

Cited by 102 publications

References 36 publications

Real-time replanning using 3D environment for humanoid robot

Real-time replanning using 3D environment for humanoid robot

Motion planning for tree climbing with inchworm‐like robots

Adaptive Motion Planning for Autonomous Rough Terrain Traversal with a Walking Robot

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