Path planning method with obstacle avoidance for manipulators in dynamic environment

Abstract

Obstacle avoidance is of great importance for path planning of manipulators in dynamic environment. To help manipulators successfully perform tasks, a method of path planning with obstacle avoidance is proposed in this article. It consists of two consecutive phases, namely, collision detection and obstacle-avoidance path planning. The collision detection is realized by establishing point-cloud model and testing intersection of axis-aligned bounding boxes trees, while obstacleavoidance path planning is achieved through preplanning a global path and adjusting it in real time. This article has the following contributions. The point-cloud model is of high resolution while the speed of collision detection is improved, and collision points can be found exactly. The preplanned global path is optimized based on the improved D-star algorithm, which reduces inflection points and decreases collision probability. The real-time path adjusting strategy satisfies the requirement of reachability and obstacle avoidance for manipulators in dynamic environment. Simulations and experiments are carried out to evaluate the validity of the proposed method, and the method is available to manipulators of any degree of freedom in dynamic environment.

A Novel Clustering-Based Algorithm for Solving Spatially Constrained Robotic Task Sequencing Problems

“…An example can be found in [28], where the use of voxels to represent obstacles in the 3D environment enabled inflation to take place by appending additional voxels in the horizontal and vertical directions to enhance navigational safety. In contrast, Chen et al [29] used an axisaligned bounding box (AABB) tree to approximate obstacles represented by a point cloud, where nodes higher up in the tree level corresponded to AABBs with greater levels of obstacle clearance. Indeed such approaches are considered conservative and could introduce narrow passages in the C-space, leading to increased computational cost for motion planning.…”

Crane Lifting Optimization and Construction Monitoring in Steel Bridge Construction Project Based on BIM and UAV

“…However, those sensors can only obtain the rough shape of obstacles but are difficult to acquire high-resolution 3D information of the environment. And, those onboard sensors can only observe some nearest dynamic change or obstacles; thus, the replanned path which is adjusted based on such local information may keep close to obstacles; this increases the collision possibility during the crane operation [30]. To fully identify the shape of obstacles and changes of environmental information in the construction site, many 3D measurement technologies are developed to build a comprehensive 3D model of the as-is construction site, such as the GIS and the laser-based sensors [31] and the camera-based sensors [8,32].…”

Real-time motion planning of self-driving vehicle on closed structured road

“…In the vehicle coordinate system, P(x, y) is the target point, l d is the distance between the target point and the rear wheel center of the vehicle, d is the front wheel angle, L is the vehicle wheelbase, R is the expected path curvature radius, a is the azimuth of the target point relative to the vehicle. Figure 10(a) shows the geometric relationship of vehicle steering, which conforms to the Ackerman steering geometry as shown in Formula (10).…”

“…At present, the research of motion planning algorithm mainly focuses on grid based search algorithm, 1,2 artificial potential field method, 3,4 random sampling based planning algorithm, 5 neural network, and other artificial intelligence planning algorithm. 6,7 Grid based search algorithms mainly include Dijkstra search algorithm, 8 A-star algorithm, 9 D-star algorithm, 10 etc. These algorithms need to preprocess the perceptual information, generate a search graph including obstacles and feasible areas, then apply the graph search algorithm to find the lowest cost edge, and finally connect the edge from the beginning to the end to form a driving path.…”