Real-Time and Efficient Collision Avoidance Planning Approach for Safe Human-Robot Interaction

Liu, Hongyan; Qu, Daokui; Xu, Fang; Du, Zhi‐Zhi; Jia, Kai; Song, Jilai; Liu, Mingmin

doi:10.1007/s10846-022-01687-0

Cited by 19 publications

(14 citation statements)

References 47 publications

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“…The same configuration parameters as the first and second tests were maintained. The results showed that the algorithm runs at an average computation time of T comp = 4.649ms, which is clearly higher than the previous cases, but still within 16ms to meet real-time requirements [2]. In this test, 143 possible collision situations were evaluated in 10 different experiments, obtaining a success rate of ρ = 94.41%, with a normalized average path deviation of ∆L = 0.304m and an average stop time of T stop = 0.514s.…”

Section: B Resultsmentioning

confidence: 72%

“…Specifically, repulsive fields are associated with obstacles, while attractive fields are associated with the target. The original concept has been further refined and applied to properly modify the shape of motion trajectories, which are seen as elastic bands subject to virtual forces [16], and is now a standard technique for real-time trajectory planning [2], [17].…”

Section: A Related Workmentioning

confidence: 99%

“…The connection between B-Splines and HMM domains is established through the continuous observation likelihoods N (µ i , Σ i ) = b i (o t ), i = 1, ..., M , as shown in (2). In particular, (µ i , Σ i ) are the components of a GMM that encodes the likelihood of a point r ∈ R 3 being described by the HMM's model λ: 6), together with transition probabilities a i,j among control points p i computed such that a left-right HMM is obtained, thus ensuring always going from a start position to a goal one.…”

Section: B Temporal Modulation With Varying Transition Probabilities ...mentioning

confidence: 99%

“…In general obstacle avoidance applications [2], the robot uses the position of a target object to modify its path and avoid collision with it. Although safety distance margins can be imposed on the target object, unexpected movements can cause the robot to collide if the algorithm is not reactive enough to prevent the impact [3].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Online Motion Planning for Safe Human-Robot Cooperation using B-Splines and Hidden Markov Models

Braglia¹,

Tagliavini²,

Pini³

et al. 2023

Preprint

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<p>When humans and robots work together, ensuring safe cooperation must be a priority. This research aims to develop a novel real-time planning algorithm that can handle unpredictable human movements by both slowing down task execution and modifying the robot's path based on the proximity of the human operator. To achieve this, an efficient method for updating the robot's motion is developed using a two-fold control approach that combines B-Splines and Hidden Markov Models. This allows the algorithm to adapt to a changing environment and avoid collisions. The proposed framework is thus validated using the Franka Emika Panda robot in a simple start-goal task. Our algorithm successfully avoids collision with the moving hand of an operator monitored by a fixed camera.</p>

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Section: B Resultsmentioning

confidence: 72%

Section: A Related Workmentioning

confidence: 99%

Section: B Temporal Modulation With Varying Transition Probabilities ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online Motion Planning for Safe Human-Robot Cooperation using B-Splines and Hidden Markov Models

Braglia¹,

Tagliavini²,

Pini³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Potential fields also are often investigated [10]- [12]. In [10], a force that depends on the distance between the robot and the obstacles deforms an initial trajectory; a subsequent force seeks to restore the trajectory.…”

Section: A Related Workmentioning

confidence: 99%

Anytime Informed Multi-Path Replanning Strategy for Complex Environments

et al. 2023

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In many real-world applications (e.g., human-robot collaboration), the environment changes rapidly, and the intended path may become invalid due to moving obstacles. In these situations, the robot should quickly find a new path to reach the goal, possibly without stopping. Planning from scratch or repairing the current graph can be too expensive and time-consuming. This paper proposes a path replanner, MARS, that enables a robot to move in dynamic environments with unpredictable obstacles. The method exploits a set of pre-computed paths to compute a new solution in a few hundred milliseconds when an obstacle obstructs the robot's way. It enhances the search speed using informed sampling and improves the current solution in an anytime fashion to make the robot reactive to environmental changes. In addition, the paper presents a multithread architecture, applicable to several replanning algorithms, to handle the execution of the robot's trajectory with continuous replanning and the collision checking of the traversed path. The paper compares state-of-the-art sampling-based path-replanning algorithms in complex and high-dimensional scenarios, showing that MARS is superior in terms of success rate and quality of solutions found. An open-source ROS-compatible implementation of the algorithm is also provided.

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Review of AI-Based Vision Detection Algorithms for Autonomous Mobile Robots

Moveh,

Merchán-Cruz

2024

Lecture Notes in Networks and Systems

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Real-Time and Efficient Collision Avoidance Planning Approach for Safe Human-Robot Interaction

Cited by 19 publications

References 47 publications

Online Motion Planning for Safe Human-Robot Cooperation using B-Splines and Hidden Markov Models

Online Motion Planning for Safe Human-Robot Cooperation using B-Splines and Hidden Markov Models

Anytime Informed Multi-Path Replanning Strategy for Complex Environments

Review of AI-Based Vision Detection Algorithms for Autonomous Mobile Robots

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