Path Planning of Industrial Robot Based on Improved RRT Algorithm in Complex Environments

This paper proposes a novel method for active collision avoidance to protect the human who enters a robot's workspace in a human-robot collaborative environment. The proposed method uses a somatosensory sensor to monitor the robot's workspace and detect anyone attempting to enter it. When someone enters the workspace, a Kinect detects and calculates the position of his or her skeleton points in real-time. However, due to the measurement errors and noise of the device, the tracking error increases over time. Therefore, the proposed method applies an improved particle filter (IPF) to accurately estimate the position of the skeleton points. In order to detect the human-robot collision in real-time, the proposed method uses cylinders to establish the bounding box model for human bones and robots and the human-robot collision is replaced by the collision between the cylinders, greatly improving the efficiency of collision detection. Moreover, taking human safety and productivity into account, the robot velocity control is carried out based on the distance between the robot and human. Then, the proposed method uses a rule-based logic system to analyze human motion so that the robot can take appropriate measures to avoid humans. Finally, the dynamic roadmap (DRM) approach plans new paths in real-time to allow robots to bypass humans. By actively avoiding collisions, the proposed method ensures that the robot will never touch the human body. The significant advantage of the proposed method is that it can detect humans in real-time, analyze their behavior and protect humans without any modification to the robot. The proposed method has been tested in practical applications, and the results show that it can successfully guarantee the safety of people entering the robot's workspace.

Section: Related Workmentioning

confidence: 99%

Active Collision Avoidance for Human-Manipulator Safety

Liang

Yao

et al. 2022

“…Existing optimization methods for path planning problems can be divided into two categories [1]: classical path planning methods and heuristic intelligent methods. Scholars have done a lot of research on path planning based on classical methods, for example, rapidly-exploring random tree (RRT) [2], cell decomposition (CD) [3], roadmap approach (RA) [4], artificial potential field (APF) [5], and probabilistic roadmap (PRM) [6], etc. The classical algorithm has the advantages of high computational efficiency and high realtime performance in dynamic path planning.…”

Section: Introductionmentioning

confidence: 99%

A Firefly Algorithm With Self-Adaptive Population Size for Global Path Planning of Mobile Robot

Fan

Hou

2020

In the simulation experiment of path planning of mobile robot based on firefly algorithm, it is found that the matching relationship between the number of fireflies and obstacles in the iterative process has significant conflict impacts on exploration ability and computational complexity of the algorithm. In order to solve the above problem, an optimal method of path planning based on firefly algorithm with selfadaptive population size is proposed. Firstly, the evaluation of degree of collision is established at the cost of avoiding collision. Based on the degree of collision of the population, two nonlinear functions are proposed to determine the population size. Then, individuals are added or deleted for the firefly population. Individuals are added randomly. The feasible solution and the infeasible solution are distinguished in firefly population, and delete the fireflies in the infeasible solution first when performing the eliminating operation. Finally, on the basis of the existing methods for dealing with infeasible paths, a coefficient that is adaptively adjusted according to the population size is introduced to control the degree to which the infeasible path approaches the feasible area. Compared with fixed population size firefly algorithm, the proposed algorithm has better performance in terms of solution stability, convergence speed and running time. INDEX TERMS path planning, improved firefly algorithm, self-adaptive population size.

“…Sampling-based motion planners [2]- [4] are generally regarded as the state-of-the-art method for such high-dimensional planning problems due to the outstanding The associate editor coordinating the review of this manuscript and approving it for publication was Liang Hu . advantages in efficiency, easy implementation and the ability to handle a multitude of different constraints [5]. But these planners are difficult to solve the motion planning with pose (position and/or orientation) constraints imposed on the robot's end-effector [6], [7], which are ubiquitous in industry and our daily life, e.g., moving a cup full of water from one place to another while keeping it vertically all along the motion; opening a door or rotating a valve.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning With Pose Constraints Based on Direct Projection for Anthropomorphic Manipulators

Liu

Zhang

2020

This paper presents an efficient planning algorithm, called Direct-Projection Rapidly-exploring Random Tree (DP-RRT), to address the motion planning with end-effector pose constraints for anthropomorphic manipulators. The key of this planning problem is to find constraint-satisfying configurations on the constraint manifolds and connect them to generate a collision-free and smooth path. In the previous works, the configurations that satisfy pose constraints are generally calculated by the numerical iteration methods based on Jacobian projection techniques. However, such approaches have many technical challenges, such as joint limits and singularity, many numerical iterations and much computing time. In this work, we propose a Direct Projection method based on the analytic inverse kinematics (IK) that can directly project configurations onto the constraint manifolds instead of the numerical iteration methods. The proposed DP-RRT algorithm combines the Direct Projection method with the Rapidly-exploring Random Tree (RRT) algorithm, where the RRT algorithm is employed to explore the ambient space by growing tree branches, and the Direct Projection method is used to project the tree branches onto the constraint manifolds for constructing a constraint-satisfying path. As the analytic IK solver is used to calculate the constraint-satisfying configurations, the DP-RRT algorithm is characterized by high efficiency and no numerical iteration. Besides, avoiding joint limits and singularity, as well as the smoothness of the end-effector and the joints trajectory are also considered. The effectiveness of the proposed algorithm is demonstrated on the Willow Garage's PR2 simulation platform in a wide range of pose-constrained cases. INDEX TERMS Motion planning, direct projection, pose constraints, analytic inverse kinematics, anthropomorphic manipulator.