Multi-goal Path Planning Autonomous System for Picking up and Delivery Tasks in Mobile Robotics

Hernandez, Kevin; Bacca, Bladimir; Posso, Breyner

doi:10.1109/tla.2017.7854617

Cited by 22 publications

(14 citation statements)

References 17 publications

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“…However, after a task is learned, the issue of optimal implementation of the task follows. But with proper task planning and effective collaborative strategies, a robot can effectively collaborate with a human to accomplish a desired goal [10].…”

Section: Introductionmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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This paper presents a state-of-the-art survey on robotic systems, sensors, actuators and collaborative strategies for Physical Human-Robot Collaboration (pHRC). The paper starts with an overview of some robotic systems with cuttingedge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances made, some research directions, and future challenges are presented.

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Section: Introductionmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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“…An in-depth review of the current literature shows several works focus on two-dimensional (2D) scenarios [13] that limit vehicle behaviour to just a flat surface and consider its height as constant by making a dimensional analysis (2.5D) [14]. However, in complex unstructured situations (including, for example, forests, urban, or underwater environments) a simple 2D algorithm is insufficient and 3D path planning is needed.…”

Section: Introductionmentioning

confidence: 99%

Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

et al. 2019

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A relevant task in unmanned aerial vehicles (UAV) flight is path planning in 3 D environments. This task must be completed using the least possible computing time. The aim of this article is to combine methodologies to optimise the task in time and offer a complete 3 D trajectory. The flight environment will be considered as a 3 D adaptive discrete mesh, where grids are created with minimal refinement in the search for collision-free spaces. The proposed path planning algorithm for UAV saves computational time and memory resources compared with classical techniques. With the construction of the discrete meshing, a cost response methodology is applied as a discrete deterministic finite automaton (DDFA). A set of optimal partial responses, calculated recursively, indicates the collision-free spaces in the final path for the UAV flight.

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“…Furthermore, an improved three-dimensional-like grid map was developed to represent the environment model [7], and then, a simple but efficient path planning algorithm was presented to solve robot navigation problems in a static environment. The authors designed an autonomous multi-goal navigation system for picking up or delivering tasks in mobile robotics and a multi-goal path planning method based on the Lin−Kernighan heuristics (LKH) algorithm for intelligent service mobile robots in Reference [8]. There are also some intelligent methods that can be applied to mobile robot path planning.…”

Section: Introductionmentioning

confidence: 99%

“…Step 3. Calculate the food concentration for each artificial fish using Equation (8) and record the optimal value in the bulletin board.…”

mentioning

confidence: 99%

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

Huang

Jiang

et al. 2019

Applied Sciences

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The cooperative path planning problem of multiple mobile robots in an unknown indoor environment is considered in this article. We presented a novel obstacle avoidance and real-time navigation algorithm. The proposed approach consisted of global path planning and local path planning via HAFSA (hybrid artificial fish swarm algorithm) and an expansion logic strategy. Meanwhile, a kind of scoring function was developed, which shortened the time of local path planning and improved the decision-making ability of the path planning algorithm. Finally, using STDR (simple two dimensional robot simulator) and RVIZ (robot operating system visualizer), a multiple mobile robot simulation platform was designed to verify the presented real-time navigation algorithm. Simulation experiments were performed to validate the effectiveness of the proposed path planning method for multiple mobile robots.

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Multi-goal Path Planning Autonomous System for Picking up and Delivery Tasks in Mobile Robotics

Cited by 22 publications

References 17 publications

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

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