Iterative Path Planning of a Serial Manipulator in a Cluttered Known Environment

Bottin, Matteo; Rosati, Giulio; Cipriani, Giulio

doi:10.1007/978-3-030-55807-9_27

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…In the same way, RL-based training is a quintessential choice for implementation that can collapse different required processes of industrial robots into the decision-making process of an adaptive robot. Task-specific trajectory planning from traditional control methods have proven to be more efficient for designated tasks [29,30] though may lack the flexibility to repurpose a robot to cope with the changes of dynamic environments. On the other hand, optimization techniques from various trajectory planning methods [31], have effectively been adopted for reinforcement learning agents [32,33] that allow for better convergence and learning of the autonomous decision-making process.…”

Section: Curriculum Learningmentioning

confidence: 99%

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2021

Int J Robot Eng

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To facilitate the current and future automation needs, the research community constantly seeks to develop dynamic and efficient autonomous decision-making agents. These agents must not only be robust to modeling uncertainties, internal and external changes, but can adapt to a range of tasks also. Recent progress in deep reinforcement learning has corroborated to its potential to train such autonomous and robust agents. At the same time, the introduction of curriculum learning has made the reinforcement learning process significantly more efficient and allowed for training on much broader tasks. This combination, Curriculum-based Deep Reinforcement Learning (CDRL), presents a powerful solution to meet the increasing complexity of today's automation industry that demands highly intelligent machines. With this work we present a concise review of CDRL methods within the context of their application to the field of adaptive robotics.

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Section: Curriculum Learningmentioning

confidence: 99%

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2021

Int J Robot Eng

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“…Path planning strategies are contingent upon environmental characteristics, which can be broadly categorized into three types: known environments [1], partially known environments [2], and unknown environments [3]. Furthermore, path planning can be categorized into two primary modes: static and dynamic, contingent on the nature of the obstacles encountered.…”

Section: Introductionmentioning

confidence: 99%

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

Gharbi

2024

ACI

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PurposeThe present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.Design/methodology/approachThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.FindingsThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.Research limitations/implicationsThe rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.Originality/valueThe originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

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“…Thus, the system must be able to detect, or even predict, them and consequently act. Methods and algorithms [3,4,5] have been developed to overcome this issue, making the correct execution of the tasks possible.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of an End-Effector for Mitigation of Collisions

Tommasino

Bottin

Cipriani

et al. 2021

Journal of Mechanical Design

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In robotics the risk of collisions is present both in industrial applications and in remote handling. If a collision occurs, the impact may damage both the robot and external equipment, which may result in successive imprecise robot tasks or line stops, reducing robot efficiency. As a result, appropriate collision avoidance algorithms should be used or, if it is not possible, the robot must be able to react to impacts reducing the contact forces. For this purpose, this paper focuses on the development of a special end-effector that can withstand impacts. It is able to protect the robot from impulsive forces caused by collisions of the end-effector, but it has no effect on possible collisions between the links and obstacles. The novel end-effector is based on a bi-stable mechanism that decouples the dynamics of the end-effector from the dynamics of the robot. The intrinsically non-linear behavior of the endeffector is investigated with the aid of numerical simulations. The effect of design parameters and operating conditions are analyzed and the interaction between the functioning of the bi-stable mechanism and the control system is studied. In particular, the effect of the mechanism in different scenarios characterized by different robot velocities is shown. Results of numerical simulations assess the validity of the proposed end-effector, which can lead to large reductions in impact forces. Numerical results are validated by means of specific laboratory tests.

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Iterative Path Planning of a Serial Manipulator in a Cluttered Known Environment

Cited by 7 publications

References 14 publications

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Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

Development and Validation of an End-Effector for Mitigation of Collisions

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