Study of rapid collision detection algorithm for manipulator

Shen, Yue; Jia, Qingxuan; Chen, Gang; Wang, Yifan

doi:10.1109/iciea.2015.7334244

Cited by 18 publications

(5 citation statements)

References 7 publications

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“…The components of the combined system shown in Figure 1 can be divided into two categories according to their approximate shapes: cylinder-like and cuboid-like. For efficiency, the coarse collision detection is firstly performed through the center distance criterion (Shen et al , 2015). If the distance between the centers of two components are larger than the sum of the maximum geometric dimensions of them, there is definitely no collision.…”

Section: Detumbling Strategymentioning

confidence: 99%

Novel fuel-saving detumbling strategy for post-capture combined spacecraft

Zhou,

Liu,

Liu

et al. 2023

AEAT

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Purpose In the post-capture stage, the tumbling target rotates the combined spacecraft system, and the detumbling operation performed by the space robot is required. To save the costly onboard fuel of the space robot, this paper aims to present a novel post-capture detumbling strategy. Design/methodology/approach Actuated by the joint rotations of the manipulator, the combined system is driven from three-axis tumbling state to uniaxial rotation about its maximum principal axis. Only unidirectional thrust perpendicular to the axis is needed to slow down the uniaxial rotation, thus saving the thruster fuel. The optimization problem of the collision-free detumbling trajectory of the space robot is described, and it is optimized by the particle swarm optimization algorithm. Findings The numerical simulation results show that along the trajectory planned by the detumbling strategy, the maneuver of the manipulator can precisely drive the combined system to rotate around its maximum principal axis, and the final kinetic energy of the combined system is smaller than the initial. The unidirectional thrust and the lower kinetic energy can ensure the fuel-saving in the subsequent detumbling stage. Originality/value This paper presents a post-capture detumbling strategy to drive the combined system from three-axis tumbling state to uniaxial rotation about its maximum principal axis by redistributing the angular momentum of the parts of the combined system. The strategy reduces the thrust torque for detumbling to effectively save the thruster fuel.

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Section: Detumbling Strategymentioning

confidence: 99%

Novel fuel-saving detumbling strategy for post-capture combined spacecraft

Zhou,

Liu,

Liu

et al. 2023

AEAT

View full text Add to dashboard Cite

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“…For determining collisions, many approaches use geometric volumes to model the robot and detect collisions between objects, such as bounding spheres, axis-aligned bounding boxes (AABB), oriented bounding boxes (OBB) and bounding cylinders [13][14][15][16]. In computer graphics, techniques such as convex polyhedra geometry and trajectory parameterization are used for performing collision detection between geometric shapes [16][17][18].…”

Section: Collision Avoidancementioning

confidence: 99%

Generating Stable and Collision-Free Policies through Lyapunov Function Learning

Coulombe

Lin

2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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The need for rapid and reliable robot deployment is on the rise. Imitation learning (IL) has become popular for producing motion planning policies from a set of demonstrations. However, many methods in IL are not guaranteed to produce stable policies that can be used for motion planning.The generated policy may not converge to the robot target, reducing reliability, and may collide with its environment, reducing the safety of the system. Also, demonstration data is tedious to collect either through kinesthetic teaching or expert performing the task, both involving human labour. Stable estimator of dynamic systems (SEDS) produces stable policies by constraining the Lyapunov stability criteria during learning, but the Lyapunov candidate function has to be manually selected, which can result in unsolvable scenarios. In this work, we propose a novel method for learning a Lyapunov function and a policy using a single neural network model from automatically generating demonstration data in simulation. The method can be equipped with an obstacle avoidance module for convex object pairs to guarantee no collisions. We demonstrated our method is capable of finding policies in several simulation environments and transfers to real-world scenarios.

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“…Then the motion primitive is generated with p d . The calculation of d and p d can be referred to the cylinder-cylinder model in [25]. It is possible that a feasible motion primitive [8] cannot be generated with the position, velocity, and acceleration on the new endpoint because of the limited dynamic performance of the quadrotor.…”

Section: Global Trajectory Fusionmentioning

confidence: 99%

Risk-aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments

Chen¹,

Peng²,

Zhang³

et al. 2022

Preprint

Self Cite

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Obstacle avoidance of quadrotors in dynamic environments, with both static and dynamic obstacles, is still a very open problem. Current works commonly leverage traditional static maps to represent static obstacles and the detection and tracking of moving objects (DATMO) method to model dynamic obstacles separately. The dynamic obstacles are pretrained in the detector and can only be modeled with certain shapes, such as cylinders or ellipsoids. This work utilizes our dual-structure particle-based (DSP) dynamic occupancy map to represent the arbitrary-shaped static obstacles and dynamic obstacles simultaneously and proposes an efficient risk-aware sampling-based local trajectory planner to realize safe flights in this map. The trajectory is planned by sampling motion primitives generated in the state space. Each motion primitive is divided into two phases: short-term planning with a strict risk limitation and relatively long-term planning designed to avoid high-risk regions. The risk is evaluated with the predicted future occupancy status, represented by particles, considering the time dimension. With an approach to split from and merge to global trajectories, the planner can also be used with an arbitrary preplanned global trajectory. Comparison experiments show that the obstacle avoidance system composed of the DSP map and our planner performs the best in dynamic environments. In real-world tests, our quadrotor reaches a speed of 6 m/s with the motion capture system and 2 m/s with everything computed on a low-price single board computer.

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Study of rapid collision detection algorithm for manipulator

Cited by 18 publications

References 7 publications

Novel fuel-saving detumbling strategy for post-capture combined spacecraft

Novel fuel-saving detumbling strategy for post-capture combined spacecraft

Generating Stable and Collision-Free Policies through Lyapunov Function Learning

Risk-aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments

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