scholarly journals Bi-Directional Drone Design and its Path Planning

2021 ◽  
Vol 10 (3) ◽  
pp. 71-74
Author(s):  
Soutrik Mukherjee ◽  
Keyword(s):  
Path Planning ◽  
Weight Ratio ◽  
Processing System ◽  
Mathematical Induction ◽  
Problem Statement ◽  
Weight Category ◽  
Memory Processing ◽  
Planning Algorithm ◽  
Path Planner ◽  
Path Planning Algorithm

There are two aspects to my project, one is optimized bi-directional drone designing and other is its path planning. The optimization in design lies in the fact that my drone can carry load both in +z and –z axis if its direction of motion is in x-y plane. This design optimization helps the drone to carry more payload than drones of same frame(basic chassis) weight category. In other words, my drone has greater payload to its own weight ratio(almost 0.8) than other drones with almost similar or same functionalities. Coming to path planning algorithm, I have taken a mathematical induction approach to solve the problem statement by clearly defining our conditions to follow to remain in the specified path along with constraints lying in the path. My goal as a path planner has to ensure that the drone follows the conditions specified without grappling into obstacles. Also, to achieve the desired goal in least possible time. The paths traversed by the drone would be stored into the memory processing system of the drone for future development of algorithm.

scholarly journals Intelligent Beetle Antennae Search for UAV Sensing and Avoidance of Obstacles

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1758 ◽  
Author(s):  
Qing Wu ◽  
Xudong Shen ◽  
Yuanzhe Jin ◽  
Zeyu Chen ◽  
Shuai Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
Search Range ◽  
Turning Angle ◽  
Planning Algorithm ◽  
Search Speed ◽  
Path Planner ◽  
Path Planning Algorithm

Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.

scholarly journals A Robot-Centered Path-Planning Algorithm for Multidirectional Additive Manufacturing for WAAM Processes and Pure Object Manipulation

2021 ◽  
Vol 11 (13) ◽  
pp. 5759
Author(s):  
Markus Schmitz ◽  
Jan Wiartalla ◽  
Markus Gelfgren ◽  
Samuel Mann ◽  
Burkhard Corves ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Path Planning ◽  
Critical Path ◽  
Object Manipulation ◽  
Robot Kinematics ◽  
Planning Algorithm ◽  
Path Planner ◽  
Joint Velocity ◽  
Path Planning Algorithm ◽  
Wire Arc Additive Manufacturing

Previous algorithms for slicing, path planning or trajectory planning of additive manufacturing cannot be used consistently for multidirectional additive manufacturing with pure object manipulation in wire-arc additive manufacturing. This work presents a novel path planning approach that directly takes robot kinematics into account and thus ensures the reachability of all critical path poses. In an additional step, the planned path segments are smoothed so that joint velocity limits are respected. It is shown that the implemented path planner generates executable robot paths and at the same time maintains the process quality (in this case, sufficient coverage of the slice area). While the introduced method enables the generation of reachable printing paths, the smoothing algorithm allows for the execution of the path with respect to the robot’s velocity limits and at the same time improves the slice coverage. Future experiments will show the realization of the real robot setup presented.

scholarly journals Obstacle Avoidance Path Planning for Worm-like Robot Using Bézier Curve

Biomimetics ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 57
Author(s):  
Yifan Wang ◽  
Zehao Liu ◽  
Akhil Kandhari ◽  
Kathryn A. Daltorio
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Optimization Techniques ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Planning Optimization ◽  
Planning Algorithm ◽  
Lateral Clearance ◽  
Path Planner ◽  
Path Planning Algorithm

Worm-like robots have demonstrated great potential in navigating through environments requiring body shape deformation. Some examples include navigating within a network of pipes, crawling through rubble for search and rescue operations, and medical applications such as endoscopy and colonoscopy. In this work, we developed path planning optimization techniques and obstacle avoidance algorithms for the peristaltic method of locomotion of worm-like robots. Based on our previous path generation study using a modified rapidly exploring random tree (RRT), we have further introduced the Bézier curve to allow more path optimization flexibility. Using Bézier curves, the path planner can explore more areas and gain more flexibility to make the path smoother. We have calculated the obstacle avoidance limitations during turning tests for a six-segment robot with the developed path planning algorithm. Based on the results of our robot simulation, we determined a safe turning clearance distance with a six-body diameter between the robot and the obstacles. When the clearance is less than this value, additional methods such as backward locomotion may need to be applied for paths with high obstacle offset. Furthermore, for a worm-like robot, the paths of subsequent segments will be slightly different than the path of the head segment. Here, we show that as the number of segments increases, the differences between the head path and tail path increase, necessitating greater lateral clearance margins.

Path Planning for Autonomous Car Parking

2018 ◽  
Author(s):  
Letian Lin ◽  
J. Jim Zhu
Keyword(s):  
Path Planning ◽  
Target Line ◽  
Planning Problem ◽  
Control Laws ◽  
Computational Costs ◽  
Planning Algorithm ◽  
Path Planner ◽  
Path Planning Algorithm ◽  
Path Planning Problem ◽  
Reverse Path

The path planning problem for autonomous car parking has been widely studied. However, it is challenging to design a path planner that can cope with parking in tight environment for all common parking scenarios. The important practical concerns in design, including low computational costs and little human’s knowledge and intervention, make the problem even more difficult. In this work, a path planner is developed using a novel four-phase algorithm. By using some switching control laws to drive two virtual cars to a target line, a forward path and a reverse path are obtained. Then the two paths are connected along the target line. As illustrated by the simulation results, the proposed path planning algorithm is fast, highly autonomous, sufficiently general and can be used in tight environment.

scholarly journals Path Planning for Aircraft Fleet Launching on the Flight Deck of Carriers

Mathematics ◽  
2018 ◽  
Vol 6 (10) ◽  
pp. 175 ◽  
Author(s):  
Yongtao Li ◽  
Yu Wu ◽  
Xichao Su ◽  
Jingyu Song
Keyword(s):  
Path Planning ◽  
Collision Detection ◽  
Planning Problem ◽  
Aircraft Carrier ◽  
Planning Algorithm ◽  
Rolling Optimization ◽  
Path Planner ◽  
Path Planning Algorithm ◽  
Flight Deck ◽  
Path Planning Problem

This paper studies the path planning problem for aircraft fleet taxiing on the flight deck of carriers, which is of great significance for improving the safety and efficiency level of launching. As there are various defects of manual command in the flight deck operation of carriers, the establishment of an automatic path planner for aircraft fleets is imperative. The requirements of launching, the particularities of the flight deck environment, the way of launch, and the work mode of catapult were analyzed. On this basis, a mathematical model was established which contains the constraints of maneuverability and the work mode of catapults; the ground motion and collision detection of aircraft are also taken into account. In the design of path planning algorithm, path tracking was combined with path planning, and the strategy of rolling optimization was applied to get the actual taxi path of each aircraft. Taking the Nimitz-class aircraft carrier as an example, the taxi paths of aircraft fleet launching was planned with the proposed method. This research can guarantee that the aircraft fleet complete launching missions safely with reasonable taxi paths.

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

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