Mobile robots path planning based on dynamic movement primitives library

Zhuang, Mei; Yang, Chen; Jiang, Minghao; Wu, Huaiyu; Cheng, Lin

doi:10.23919/chicc.2017.8028446

Cited by 13 publications

(13 citation statements)

References 8 publications

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“…Integrating observed interaction forces in this DBN can infer user intent, however, a similar issue exist for parameterizing the interaction forces. Furthermore, the DBN would be more akin to hierarchical frameworks which utilize individual DMP systems such as seen in Mei et al (2017), where an arbitration occurs to determine the appropriate DMP. A constraint for pHRIP is the reliance on DTW to phase align the partial observations, requiring a reference trajectory.…”

Section: Discussionmentioning

confidence: 99%

User intent estimation during robot learning using physical human robot interaction primitives

et al. 2022

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As robotic systems transition from traditional setups to collaborative work spaces, the prevalence of physical Human Robot Interaction has risen in both industrial and domestic environments. A popular representation for robot behavior is movement primitives which learn, imitate, and generalize from expert demonstrations. While there are existing works in context-aware movement primitives, they are usually limited to contact-free human robot interactions. This paper presents physical Human Robot Interaction Primitives (pHRIP), which utilize only the interaction forces between the human user and robot to estimate user intent and generate the appropriate robot response during physical human robot interactions. The efficacy of pHRIP is evaluated through multiple experiments based on target-directed reaching and obstacle avoidance tasks using a real seven degree of freedom robot arm. The results are validated against Interaction Primitives which use observations of robotic trajectories, with discussions of future pHRI applications utilizing pHRIP.

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Section: Discussionmentioning

confidence: 99%

User intent estimation during robot learning using physical human robot interaction primitives

et al. 2022

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“…In some cases, however, it is better to turn to the left so as not to lose too much time avoiding the obstacle. It can be done by analyzing the sign of the angles as represented in Equation (12).…”

Section: = • • (13)mentioning

confidence: 99%

“…POA, such as the other algorithm, makes the same action. Otherwise, it uses Equation (12) to determinate which side turns the AGV.…”

Section: Dynamic Window Approach Comparisonmentioning

confidence: 99%

A Free Navigation of an AGV to a Non-Static Target with Obstacle Avoidance

et al. 2019

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The industry is changing in order to improve the economy sector. This is the reason why technology is improving and developing new devices. The autonomous guided vehicle with free navigation is a new machine, which uses different techniques to move such as mapping, localization, path planning, and path following. In this paper, a path following is proposed. The path following is called moving to a point, which uses the proportional distance between the target and the autonomous guided vehicles (AGV) to calculate the velocity and direction. If some obstacles appear in the trajectory, however, the vehicle stops. Instead of stopping the machine, by using moving to a point logic, an obstacle avoidance function will be implemented. In this implementation, different parameters can be configured, such as: security distance, which determinates when the obstacle avoidance must correct the pose; and proportional values, which modify the velocity and steering commands. It is also compared to a dynamic window approach (DWA) obstacle avoidance solution. Additionally, the AGV navigates to a non-static target with a path following algorithm.

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“…These aspects move the swarm toward the best solutions, which is the goal position. Dynamic movement primitives (DMP), as Mei et al [8] analyzed, are another solution, which were motivated by the desire to find a way to represent complex motor actions that can be flexibly adjusted without manual parameter tuning or having to worry about instability. On the end, this intelligence tries to simplify complex movements on a set of primitive actions.…”

Section: Of 19mentioning

confidence: 99%

“…Dynamic movements primitives [8] The complex movements are simplified by creating a set of primitive actions.…”

Section: Reference Advantages Weaknessmentioning

confidence: 99%

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

et al. 2019

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The aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters’ values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the α D , β D , and γ D parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.

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Mobile robots path planning based on dynamic movement primitives library

Cited by 13 publications

References 8 publications

User intent estimation during robot learning using physical human robot interaction primitives

User intent estimation during robot learning using physical human robot interaction primitives

A Free Navigation of an AGV to a Non-Static Target with Obstacle Avoidance

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

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