Automatic task scheduling optimization and collision-free path planning for multi-areas problem

Gao, Wenxiang; Liu, Chun; Zhan, Youhe; Luo, Yan; Lan, Yu‐Long; Li, Shijie; Tang, Mutian

doi:10.1007/s11370-021-00381-8

Cited by 6 publications

(6 citation statements)

References 32 publications

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“…Solving the MTP directly is challenging due to the large number of possibly collision-free paths. Therefore, we adopt a two-step method similar to [29]. In the first step, we determine the optimal sequence that visits every ToI by simplifying the problem to a TSP.…”

Section: Methodsmentioning

confidence: 99%

“…1) IDP / DP / Iterative Rubber Band Algorithm (IRBA) Comparison: We compare the performance of the IDP with the method of preplanning all M 2 (N − 1) + 2M collision-free paths before performing DP once, which we refer to as DP, and an adaption of the method proposed in [29] used to solve MTPGR, which we refer to as Iterative RBA (IRBA).…”

Section: Experiments a Simulationmentioning

confidence: 99%

“…The variants of GTSP and TSPN addressing collision-free path planning are called Multi-Goal Path Planning Problem (MTP) [27] and MTP for Goal Regions (MTPGR) [28] respectively. Gao et al [29] divide the MTPGR into two subproblems as in [26]. However, instead of iterating between them, they first solve for the optimal sequence once.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments

Chen,

Frey,

Arm

et al. 2023

IEEE Robot. Autom. Lett.

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Robotic exploration or monitoring missions require mobile robots to autonomously and safely navigate between multiple target locations in potentially challenging environments. Currently, this type of multi-goal mission often relies on humans designing a set of actions for the robot to follow in the form of a path or waypoints. In this work, we consider the multigoal problem of visiting a set of pre-defined targets, each of which could be visited from multiple potential locations. To increase autonomy in these missions, we propose a safe multigoal (SMUG) planner that generates an optimal motion path to visit those targets. To increase safety and efficiency, we propose a hierarchical state validity checking scheme, which leverages robot-specific traversability learned in simulation. We use LazyPRM* with an informed sampler to accelerate collisionfree path generation. Our iterative dynamic programming algorithm enables the planner to generate a path visiting more than ten targets within seconds. Moreover, the proposed hierarchical state validity checking scheme reduces the planning time by 30% compared to pure volumetric collision checking and increases safety by avoiding high-risk regions. We deploy the SMUG planner on the quadruped robot ANYmal and show its capability to guide the robot in multi-goal missions fully autonomously on rough terrain.

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Section: Methodsmentioning

confidence: 99%

Section: Experiments a Simulationmentioning

confidence: 99%

See 1 more Smart Citation

SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments

Chen,

Frey,

Arm

et al. 2023

IEEE Robot. Autom. Lett.

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“…In addition, when multiple tasks are perceived, CBASH must perform them with a rule for saving time and resources. Therefore, task planning should be considered to perform tasks [34].…”

Section: G Task Planningmentioning

confidence: 99%

CBASH: A CareBot-Assisted Smart Home System Architecture to Support Aging-in-Place

Zhu

Shen

et al. 2023

IEEE Access

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“…In solving practical problems, MTP problems extend beyond TSP problems, involving the coupling of task sequence optimization and collision-free path planning. Many studies seek to simplify the problem under specified conditions [18]. Ye et al [19] proposed an innovative approach that integrates collision avoidance path planning using the Improved Probabilistic Roadmap method with delivery sequence optimization using the Improved Genetic Algorithm to solve MTP.…”

Section: Introductionmentioning

confidence: 99%

Multi-area Collision-free Path Planning and Efficient Task Scheduling Optimization for Autonomous Agricultural Robots

Yang,

Li,

Wang

et al. 2024

Preprint

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Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access routes in diverse and complex multi-region planning scenarios.

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Automatic task scheduling optimization and collision-free path planning for multi-areas problem

Cited by 6 publications

References 32 publications

SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments

SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments

CBASH: A CareBot-Assisted Smart Home System Architecture to Support Aging-in-Place

Multi-area Collision-free Path Planning and Efficient Task Scheduling Optimization for Autonomous Agricultural Robots

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