A PRM Approach to Path Planning with Obstacle Avoidance of an Autonomous Robot

Alarabi, Saleh; Luo, Chaomin; Santora, Michael

doi:10.1109/icara55094.2022.9738559

Cited by 25 publications

(4 citation statements)

References 6 publications

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“…This makes it difficult for the robot to reach the target point. Comparatively speaking, the Probabilistic Road Map method (PRM) based on graph search (Alarabi and Luo, 2022 ) can effectively solve the problem of chaotic obstacle distribution. This algorithm establishes probabilistic road maps in the robot's free configuration space (C space) (Lozano-Perez, 1990 ) by generating and interconnecting a large number of random configurations.…”

Section: Related Workmentioning

confidence: 99%

Deep reinforcement learning-aided autonomous navigation with landmark generators

Wang,

Sun,

Xie

et al. 2023

Front. Neurorobot.

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Mobile robots are playing an increasingly significant role in social life and industrial production, such as searching and rescuing robots, autonomous exploration of sweeping robots, and so on. Improving the accuracy of autonomous navigation of mobile robots is a hot issue to be solved. However, traditional navigation methods are unable to realize crash-free navigation in an environment with dynamic obstacles, more and more scholars are gradually using autonomous navigation based on deep reinforcement learning (DRL) to replace overly conservative traditional methods. But on the other hand, DRL's training time is too long, and the lack of long-term memory easily leads the robot to a dead end, which makes its application in the actual scene more difficult. To shorten training time and prevent mobile robots from getting stuck and spinning around, we design a new robot autonomous navigation framework which combines the traditional global planning and the local planning based on DRL. Therefore, the entire navigation process can be transformed into first using traditional navigation algorithms to find the global path, then searching for several high-value landmarks on the global path, and then using the DRL algorithm to move the mobile robot toward the designated landmarks to complete the final navigation, which makes the robot training difficulty greatly reduced. Furthermore, in order to improve the lack of long-term memory in deep reinforcement learning, we design a feature extraction network containing memory modules to preserve the long-term dependence of input features. Through comparing our methods with traditional navigation methods and reinforcement learning based on end-to-end depth navigation methods, it shows that while the number of dynamic obstacles is large and obstacles are rapidly moving, our proposed method is, on average, 20% better than the second ranked method in navigation efficiency (navigation time and navigation paths' length), 34% better than the second ranked method in safety (collision times), 26.6% higher than the second ranked method in success rate, and shows strong robustness.

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Section: Related Workmentioning

confidence: 99%

Deep reinforcement learning-aided autonomous navigation with landmark generators

Wang,

Sun,

Xie

et al. 2023

Front. Neurorobot.

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“…To overcome the difficulties of teaching robots and the computational complexity of robot motion planning, researchers have developed various sampling-based algorithms that focus on the concept and/or conversion of samples within the C-space in an iterative manner [4,7,10]. These planners aim to define sampling-based motion in high-dimensional C-spaces using two main approaches: (1) based on a roadmap [11][12][13][14] and (2) based on random trees [15][16][17][18]. Kavraki et al [11] introduced the probabilistic roadmap (PRM) approach for motion planning in collision-free paths.…”

Section: Related Work and Our Approach To Robot Motion Planningmentioning

confidence: 99%

A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

Alam,

Nishi,

Liu

et al. 2023

Energies

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Energy usage in robotic applications is rapidly increasing as industrial robot installations grow. This research introduces a novel approach, using the rapidly exploring random tree (RRT)-based scheme for optimizing the robot’s motion planning and minimizing energy consumption. Sampling-based algorithms for path planning, such as RRT and its many other variants, are widely used in robotic motion planning due to their efficiency in solving complex high-dimensional problems efficiently. However, standard versions of these algorithms cannot guarantee that the generated trajectories are always optimum and mostly ignore the energy consumption in robotic applications. This paper proposes an energy-efficient industrial robotics motion planning approach using the novel flight cost-based RRT (FC-RRT*) algorithm in pick-and-place operation to generate nodes in a predetermined direction and then calculate energy consumption using the circle point method. After optimizing the motion trajectory, power consumption is computed for the rotary axes of a six degree of freedom (6DOF) serial type of industrial robot using the work–energy hypothesis for the rotational motion of a rigid body. The results are compared to the traditional RRT and RRT* (RRT-star) algorithm as well as the kinematic solutions. The experimental results of axis indexing tests indicate that by employing the sampling-based FC-RRT* algorithm, the robot joints consume less energy (1.6% to 16.5% less) compared to both the kinematic solution and the conventional RRT* algorithm.

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“…The existing path planning methods primarily include: 1. Heuristic rulegraph search algorithms, such as the Dijkstra algorithm [8] and A* algorithm [9-1 Probability-based path planning methods, such as the Rapidly-exploring Random algorithm (RRT) [12,13], Optimal Rapidly-exploring Random Tree algorithm (R [14,15], and Probabilistic Roadmap Method (PRM) [16]; 3. Algorithms based on th sumed gravitational field, such as the Artificial Potential Field method (APF) [17,1 Meta-heuristic algorithms, such as the Grey Wolf Optimization algorithm (GWO) [1 Particle Swarm Optimization algorithm (PSO) [21,22], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Heuristic rulebased graph search algorithms, such as the Dijkstra algorithm [8] and A* algorithm [9][10][11]; 2. Probability-based path planning methods, such as the Rapidly-exploring Random Tree algorithm (RRT) [12,13], Optimal Rapidly-exploring Random Tree algorithm (RRT*) [14,15], and Probabilistic Roadmap Method (PRM) [16]; 3. Algorithms based on the assumed gravitational field, such as the Artificial Potential Field method (APF) [17,18]; 4.…”

Section: Introductionmentioning

confidence: 99%

Innovative Optimal Control Path Planning for PMCU in Cruise Ship Construction: A Kinematic and Obstacle Avoidance Model

Li,

Dong,

Huang

2023

Applied Sciences

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In cruise ship manufacturing, it is common practice for prefabricated modular cabin units (PMCU) to be assembled in the workshop and transported to a designated installation location on board the ship. This paper proposed a Radau pseudospectral method based on a probabilistic roadmap (PRM) for PMCU path planning in complex deck environments. Firstly, a kinematic model is constructed, and the continuous-time optimal control path planning problem is established. Among them, an area-based obstacle avoidance constraint is applied to the approach. Then, the continuous-time optimal control problem is discretized into a nonlinear programming (NLP) problem using the orthogonal collocation method. For the initial value-sensitive NLP problem solving, a strategy using PRM combined with a heuristic rule is used to obtain a high-quality initial guess. Finally, experiments under two operating scenarios of PMCU pushing demonstrate that the method is capable of generating a better path while ensuring that the whole process is collision-free. The results show that the algorithm proposed in this paper can effectively solve the PMCU pushing path planning problem in the complex deck environment, and the path considering kinematic characteristics can be used for cabin installation, which has strong operability and applicability.

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A PRM Approach to Path Planning with Obstacle Avoidance of an Autonomous Robot

Cited by 25 publications

References 6 publications

Deep reinforcement learning-aided autonomous navigation with landmark generators

Deep reinforcement learning-aided autonomous navigation with landmark generators

A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

Innovative Optimal Control Path Planning for PMCU in Cruise Ship Construction: A Kinematic and Obstacle Avoidance Model

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