scholarly journals Robust Parking Path Planning with Error-Adaptive Sampling under Perception Uncertainty

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3560 ◽  
Author(s):  
Seongjin Lee ◽  
Wonteak Lim ◽  
Myoungho Sunwoo
Keyword(s):  
Path Planning ◽  
Adaptive Sampling ◽  
Planning Process ◽  
Optimal Decision ◽  
Parking Space ◽  
Decision Under Uncertainty ◽  
Perception System ◽  
True Position ◽  
Position And Orientation ◽  
Path Planner

In automated parking systems, a path planner generates a path to reach the vacant parking space detected by a perception system. To generate a safe parking path, accurate detection performance is required. However, the perception system always includes perception uncertainty, such as detection errors due to sensor noise and imperfect algorithms. If the parking path planner generates the parking path under uncertainty, problems may arise that cause the vehicle to collide due to the automated parking system. To avoid these problems, it is a challenging problem to generate the parking path from the erroneous parking space. To solve this conundrum, it is important to estimate the perception uncertainty and adapt the detection error in the planning process. This paper proposes a robust parking path planning that combines an error-adaptive sampling of generating possible path candidates with a utility-based method of making an optimal decision under uncertainty. By integrating the sampling-based method and the utility-based method, the proposed algorithm continuously generates an adaptable path considering the detection errors. As a result, the proposed algorithm ensures that the vehicle is safely located in the true position and orientation of the parking space under perception uncertainty.

scholarly journals Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot

Biomimetics ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 26 ◽  
Author(s):  
Yifan Wang ◽  
Prathamesh Pandit ◽  
Akhil Kandhari ◽  
Zehao Liu ◽  
Kathryn A. Daltorio
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
The Body ◽  
Random Tree ◽  
Lateral Motion ◽  
Body Width ◽  
Single Body ◽  
Wave Path ◽  
Position And Orientation ◽  
Path Planner

Inspired by earthworms, worm-like robots use peristaltic waves to locomote. While there has been research on generating and optimizing the peristalsis wave, path planning for such worm-like robots has not been well explored. In this paper, we evaluate rapidly exploring random tree (RRT) algorithms for path planning in worm-like robots. The kinematics of peristaltic locomotion constrain the potential for turning in a non-holonomic way if slip is avoided. Here we show that adding an elliptical path generating algorithm, especially a two-step enhanced algorithm that searches path both forward and backward simultaneously, can make planning such waves feasible and efficient by reducing required iterations by up around 2 orders of magnitude. With this path planner, it is possible to calculate the number of waves to get to arbitrary combinations of position and orientation in a space. This reveals boundaries in configuration space that can be used to determine whether to continue forward or back-up before maneuvering, as in the worm-like equivalent of parallel parking. The high number of waves required to shift the body laterally by even a single body width suggests that strategies for lateral motion, planning around obstacles and responsive behaviors will be important for future worm-like robots.

Adaptive rapidly-exploring random tree for efficient path planning of high-degree-of-freedom articulated robots

2015 ◽  
Vol 229 (18) ◽  
pp. 3361-3367 ◽  
Author(s):  
Dong-Hyung Kim ◽  
Youn-Sung Choi ◽  
Sang-Ho Kim ◽  
Jing Wu ◽  
Chao Yuan ◽  
...  
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
Planning Process ◽  
Degree Of Freedom ◽  
Random Tree ◽  
High Dimensional ◽  
Simulation Results ◽  
Articulated Robots ◽  
Path Planner ◽  
High Degree

This article proposes a method for the path planning of high-degree-of-freedom articulated robots with adaptive dimensionality. For efficient path planning in a high-dimensional configuration space, we first describe an adaptive body selection that selects the robot bodies depending on the complexity of the path planning. Then, the involved joints of the selected body are included in the planning process. That is, it builds the C-space (configuration space) with adaptive dimensionality for sampling-based path planner. Next, by using adaptive body selection, the adaptive rapidly-exploring random tree (RRT) algorithm is introduced, which incrementally grows RRTs in the adaptive dimensional C-space. We show through several simulation results that the proposed method is more efficient than the basic RRT-based path planner, which requires full-dimensional planning.

scholarly journals Collision-free path planning for welding manipulator via hybrid algorithm of deep reinforcement learning and inverse kinematics

2021 ◽  
Author(s):  
Jie Zhong ◽  
Tao Wang ◽  
Lianglun Cheng
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Free Path ◽  
Inverse Kinematics ◽  
Multiple Dimensions ◽  
Continuous State ◽  
Planning Algorithm ◽  
Convergence Performance ◽  
Path Planner ◽  
Action Spaces

AbstractIn actual welding scenarios, an effective path planner is needed to find a collision-free path in the configuration space for the welding manipulator with obstacles around. However, as a state-of-the-art method, the sampling-based planner only satisfies the probability completeness and its computational complexity is sensitive with state dimension. In this paper, we propose a path planner for welding manipulators based on deep reinforcement learning for solving path planning problems in high-dimensional continuous state and action spaces. Compared with the sampling-based method, it is more robust and is less sensitive with state dimension. In detail, to improve the learning efficiency, we introduce the inverse kinematics module to provide prior knowledge while a gain module is also designed to avoid the local optimal policy, we integrate them into the training algorithm. To evaluate our proposed planning algorithm in multiple dimensions, we conducted multiple sets of path planning experiments for welding manipulators. The results show that our method not only improves the convergence performance but also is superior in terms of optimality and robustness of planning compared with most other planning algorithms.

MOTION PLANNING USING PREDICTED PERCEPTIVE CAPABILITY

2009 ◽  
Vol 06 (03) ◽  
pp. 435-457 ◽  
Author(s):  
PHILIPP MICHEL ◽  
JOEL CHESTNUTT ◽  
SATOSHI KAGAMI ◽  
KOICHI NISHIWAKI ◽  
JAMES J. KUFFNER ◽  
...  
Keyword(s):  
Motion Planning ◽  
Planning Process ◽  
Humanoid Robots ◽  
Experimental Results ◽  
Success Rates ◽  
Perception System ◽  
Task Success

We present an approach to motion planning for humanoid robots that aims to ensure reliable execution by augmenting the planning process to reason about the robot's ability to successfully perceive its environment during operation. By efficiently simulating the robot's perception system during search, our planner utilizes a perceptive capability metric that quantifies the 'sensability' of the environment in each state given the task to be accomplished. We have applied our method to the problem of planning robust autonomous grasping motions and walking sequences as performed by an HRP-2 humanoid. A fast GPU-accelerated 3D tracker is used for perception, with a grasp planner and footstep planner incorporating reasoning about the robot's perceptive capability. Experimental results show that considering information about the predicted perceptive capability ensures that sensing remains operational throughout the grasping or walking sequence and yields higher task success rates than perception-unaware planning.

Optimal path planning of a disinfection mobile robot against COVID-19 in a ROS-based research platform

2021 ◽  
Vol 16 (4) ◽  
pp. 405-417
Author(s):  
L. Banjanovic-Mehmedovic ◽  
I. Karabegovic ◽  
J. Jahic ◽  
M. Omercic
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Pso Algorithm ◽  
Optimal Path Planning ◽  
Localization And Mapping ◽  
Increasing Demand ◽  
Window Approach ◽  
Path Planner ◽  
Hardware Platforms ◽  
Reactive Collision

Due to COVID-19 pandemic, there is an increasing demand for mobile robots to substitute human in disinfection tasks. New generations of disinfection robots could be developed to navigate in high-risk, high-touch areas. Public spaces, such as airports, schools, malls, hospitals, workplaces and factories could benefit from robotic disinfection in terms of task accuracy, cost, and execution time. The aim of this work is to integrate and analyse the performance of Particle Swarm Optimization (PSO) algorithm, as global path planner, coupled with Dynamic Window Approach (DWA) for reactive collision avoidance using a ROS-based software prototyping tool. This paper introduces our solution – a SLAM (Simultaneous Localization and Mapping) and optimal path planning-based approach for performing autonomous indoor disinfection work. This ROS-based solution could be easily transferred to different hardware platforms to substitute human to conduct disinfection work in different real contaminated environments.

Autonomous Parking Strategy

2008 ◽  
Author(s):  
Nikolai Moshchuk ◽  
Shih-Ken Chen
Keyword(s):  
Path Planning ◽  
Parking Space ◽  
Novice Drivers ◽  
Parallel Parking ◽  
Parking Spot ◽  
Innovative System ◽  
Planning Strategies ◽  
Vehicle Configuration

Parallel parking can be a difficult task for novice drivers or drivers who seldom drive in congested city where parking space is limited. Parking Assist is an innovative system designed to aid the driver in performing sometimes difficult parallel parking maneuvers. Many companies are developing such systems with major automakers, such as Valeo, Aisin Seiki, Hella, Robert Bosch, and TRW. For example, Toyota IPA (Intelligent Parking Assist) system uses a rear view camera and automatically steer the vehicle into the parking spot with driver controlling braking. This paper describes the development of parking path planning strategies based on available parking space. A virtual turn center will first be defined and derived based on vehicle configuration. Required parking space for one or two cycle parking maneuver will then be determined. Path planning strategies for both one and two turn parking maneuvers will be developed next. Finally CarSim simulation will be performed to verify the design.

Efficient CNC Tool Path Planning Using Point Cloud

2018 ◽  
Author(s):  
Sumedh Ghogare ◽  
S. S. Pande
Keyword(s):  
Path Planning ◽  
Point Cloud ◽  
Tool Path ◽  
Planning Process ◽  
Cnc Milling ◽  
Tool Path Planning ◽  
Planning Strategy ◽  
Part Quality ◽  
Nc Program ◽  
Cutter Path

This paper reports the development of an efficient iso-scallop tool path planning strategy for machining of freeform surfaces on a three axis CNC milling center using the point cloud as the input. Boundary of the point cloud is chosen as the Master Cutter Path, using which the scallop points are computed. Adjacent side tool paths are computed using these scallop points and the path planning process is completed till the entire surface is covered. The system generates post-processed NC program in ISO format which was extensively tested for various case studies. The results were compared with the iso-planar tool path strategy from commercial software. Our system was found to generate efficient tool path in terms of part quality, productivity and storage memory.

scholarly journals Real - Time Trajectory and Velocity Planning for Autonomous Vehicles

2021 ◽  
Vol 10 (5) ◽  
pp. 439-448
Author(s):  
Hrishikesh Dey ◽  
Rithika Ranadive ◽  
Abhishek Chaudhari
Keyword(s):  
Path Planning ◽  
Velocity Profile ◽  
Real Time ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Autonomous Driving ◽  
Navigation Algorithm ◽  
Velocity Planning ◽  
Planning Algorithm ◽  
Path Planner

Path planning algorithm integrated with a velocity profile generation-based navigation system is one of the most important aspects of an autonomous driving system. In this paper, a real-time path planning solution to obtain a feasible and collision-free trajectory is proposed for navigating an autonomous car on a virtual highway. This is achieved by designing the navigation algorithm to incorporate a path planner for finding the optimal path, and a velocity planning algorithm for ensuring a safe and comfortable motion along the obtained path. The navigation algorithm was validated on the Unity 3D Highway-Simulated Environment for practical driving while maintaining velocity and acceleration constraints. The autonomous vehicle drives at the maximum specified velocity until interrupted by vehicular traffic, whereas then, the path planner, based on the various constraints provided by the simulator using µWebSockets, decides to either decelerate the vehicle or shift to a more secure lane. Subsequently, a splinebased trajectory generation for this path results in continuous and smooth trajectories. The velocity planner employs an analytical method based on trapezoidal velocity profile to generate velocities for the vehicle traveling along the precomputed path. To provide smooth control, an s-like trapezoidal profile is considered that uses a cubic spline for generating velocities for the ramp-up and ramp-down portions of the curve. The acceleration and velocity constraints, which are derived from road limitations and physical systems, are explicitly considered. Depending upon these constraints and higher module requirements (e.g., maintaining velocity, and stopping), an appropriate segment of the velocity profile is deployed. The motion profiles for all the use-cases are generated and verified graphically.

Mechanical arm obstacle avoidance path planning based on improved artificial potential field method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tianying Xu ◽  
Haibo Zhou ◽  
Shuaixia Tan ◽  
Zhiqiang Li ◽  
Xia Ju ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Planning Process ◽  
Field Method ◽  
Artificial Potential Field ◽  
Local Minima ◽  
Content Type ◽  
Potential Field Method ◽  
Artificial Potential Field Method

Purpose This paper aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process. Design/methodology/approach In this paper, an improved artificial potential field method is proposed, where the object can leave the local minima point, where the algorithm falls into, while it avoids the obstacle, following a shorter feasible path along the repulsive equipotential surface, which is locally optimized. The whole obstacle avoidance process is based on the improved artificial potential field method, applied during the mechanical arm path planning action, along the motion from the starting point to the target point. Findings Simulation results show that the algorithm in this paper can effectively perceive the obstacle shape in all the selected cases and can effectively shorten the distance of the planned path by 13%–41% with significantly higher planning efficiency compared with the improved artificial potential field method based on rapidly-exploring random tree. The experimental results show that the improved artificial potential field method can effectively plan a smooth collision-free path for the object, based on an algorithm with good environmental adaptability. Originality/value An improved artificial potential field method is proposed for optimized obstacle avoidance path planning of a mechanical arm in three-dimensional space. This new approach aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process.

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