A real-time motion planning algorithm for a hyper-redundant set of mechanisms

Robotica ◽  
2013 ◽  
Vol 31 (8) ◽  
pp. 1327-1335 ◽  
Author(s):  
Nir Shvalb ◽  
Boaz Ben Moshe ◽  
Oded Medina

SUMMARYWe introduce a novel probabilistic algorithm (CPRM) for real-time motion planning in the configuration space${\EuScript C}$. Our algorithm differs from a probabilistic road map (PRM) algorithm in the motion between a pair of anchoring points (local planner) which takes place on the boundary of the obstacle subspace${\EuScript O}$. We define a varying potential fieldfon ∂${\EuScript O}$as a Morse function and follow$\vec{\nabla} f$. We then exemplify our algorithm on a redundant worm climbing robot withndegrees of freedom and compare our algorithm running results with those of the PRM.

2019 ◽  
Vol 9 (15) ◽  
pp. 3009 ◽  
Author(s):  
Qing Chang ◽  
Xiao Luo ◽  
Zhixia Qiao ◽  
Qian Li

A novel robot capable of performing maintenance and inspection tasks for railway bridges is proposed in this paper. Termed CMBOT (climbing manipulator robot), the robot is a combination of a five-degrees-of-freedom (5-Dof) biped climbing robot with two electromagnetic feet and a redundant manipulator with 7-Dof. This capability offers important advantages for performing maintenance and inspection tasks for railway bridges. Several fundamental issues of the CMBOT, such as robotic system development and motion planning algorithms, are addressed in this paper. A series of simulations and prototype experiments were conducted to validate the proposed robotic systems and motion planning algorithm. The results of the experiments show the reliability of the robotic systems and the efficiency of the motion planning algorithm.


2020 ◽  
Vol 10 (21) ◽  
pp. 7716
Author(s):  
Tamás Hegedűs ◽  
Balázs Németh ◽  
Péter Gáspár

In the development of autonomous vehicles, the design of real-time motion-planning is a crucial problem. The computation of the vehicle trajectory requires the consideration of safety, dynamic and comfort aspects. Moreover, the prediction of the vehicle motion in the surroundings and the real-time planning of the autonomous vehicle trajectory can be complex tasks. The goal of this paper is to present low-complexity motion-planning for overtaking scenarios in parallel traffic. The developed method is based on the generation of a graph, which contains feasible vehicle trajectories. The reduction of the complexity in the real-time computation is achieved through the reduction of the graph with clustering. In the motion-planning algorithm, the predicted motion of the surrounding vehicles is taken into consideration. The prediction algorithm is based on density functions of the surrounding vehicle motion, which are developed through real measurements. The resulted motion-planning algorithm is able to guarantee a safe and comfortable trajectory for the autonomous vehicle. The effectiveness of the method is illustrated through simulation examples using a high-fidelity vehicle dynamic simulator.


2021 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Khawaja Fahad Iqbal ◽  
Akira Kanazawa ◽  
Silvia Romana Ottaviani ◽  
Jun Kinugawa ◽  
Kazuhiro Kosuge

2016 ◽  
Vol 40 (3) ◽  
pp. 383-397 ◽  
Author(s):  
Bahman Nouri Rahmat Abadi ◽  
Sajjad Taghvaei ◽  
Ramin Vatankhah

In this paper, an optimal motion planning algorithm and dynamic modeling of a planar kinematically redundant manipulator are considered. Kinematics of the manipulator is studied, Jacobian matrix is obtained and the dynamic equations are derived using D’Alembert’s principle. Also, a novel actuation method is introduced and applied to the 3-PRPR planar redundant manipulator. In this approach, the velocity of actuators is determined in such a way to minimize the 2-norm of the velocity vector, subjected to the derived kinematic relations as constraints. Having the optimal motion planning, the motion is controlled via a feedback linearization controller. The motion of the manipulator is simulated and the effectiveness of the proposed actuation strategy and the designed controller is investigated.


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