A node selection algorithm to graph-based multi-waypoint optimization navigation and mapping

Abstract

Autonomous robot multi-waypoint navigation and mapping have been demanded in many real-world applications found in search and rescue (SAR), environmental exploration, and disaster response. Many solutions to this issue have been discovered via graph-based methods in need of switching the robotos trajectory between the nodes and edges within the graph to create a trajectory for waypoint-to-waypoint navigation. However, studies of how waypoints are locally bridged to nodes or edges on the graphs have not been adequately undertaken. In this paper, an adjacent node selection (ANS) algorithm is developed to implement such a protocol to build up regional path from waypoints to nearest nodes or edges on the graph. We propose this node selection algorithm along with the generalized Voronoi diagram (GVD) and Improved Particle Swarm Optimization (IPSO) algorithm as well as a local navigator to solve the safety-aware concurrent graph-based multi-waypoint navigation and mapping problem. Firstly, GVD is used to form a Voronoi diagram in an obstacle populated environment to construct safety-aware routes. Secondly, the sequence of multiple waypoints is created by the IPSO algorithm to minimize the total travelling cost. Thirdly, while the robot attempts to visit multiple waypoints, it traverses along the edges of the GVD to plan a collision-free trajectory. The regional path from waypoints to the nearest nodes or edges needs to be created to join the trajectory by the proposed ANS algorithm. Finally, a sensor-based histogram local reactive navigator is adopted for moving obstacle avoidance while local maps are constructed as the robot moves. An improved -spline curve-based smooth scheme is adopted that further refines the trajectory and enables the robot to be navigated smoothly. Simulation and comparison studies validate the effectiveness and robustness of the proposed model.

Autonomous multi-robot allocation and formation control for remote sensing in environmental exploration

“…Step 3: Once the winning neuron and its neighbors have been chosen, the weight update rule involves moving the winner neuron and its neighbors towards the input target position while keeping the other neurons static. The rule can be summarized from Equation (6) Step 4: When updating a neural network's weights, it's important to adjust the learning rate and neighborhood size over time. To achieve this, we typically decrease the learning rate as time passes.…”

“…In this example, neuron is the only one in the winner's neighborhood . In Figure 3(d), the movement of the winner and its neighbors is depicted according to the rule in Equation (6). Neurons R 23 and R 22 move a little toward Target T i by changing the weight vectors while others do not move.…”

“…5 In order to make these systems more effective, researchers have been studying different aspects of robotics, including task allocation, formation control, and path planning. [6][7][8][9][10][11][12][13] As the field of robotics continues to evolve, there is an increasing interest in developing systems that can perform complex tasks in collaboration with other robots. Task allocation is a critical aspect of multi-robot systems, as it determines how individual robots are assigned to different tasks based on their capabilities and the overall mission requirements.…”

Deep learning-based heterogeneous system for autonomous navigation

“…[5][6][7] Heterogeneous systems have been used in a variety of different methodologies, such as in robotic networks, [8][9][10][11] aerial sensing, 12,13 target detection, [14][15][16][17] swarm robotics, 9, 18-20 mapping and exploration. [21][22][23][24][25][26][27][28] Crux et al propose an effective communication coverage strategy to guarantee the connection between robots to maintain and improve the communication strategies between various AVs. 8 Straub set out to improve data collection strategies of surveillance strategies of AVs and proposed a data collection strategy to improve the efficiency of orbital, aerial, and ground vehicles through task allocation strategies.…”

Sensor-based multi-waypoint autonomous robot navigation with graph-based models

“…Sellers et al proposed a safety-aware model utilizing a Generalized Voronoi Diagram (GVD). 40 After defining the safetyaware paths, a particle swam optimization algorithm is applied to the list of multiple waypoints to determine the visiting order. This Adjacent Node Algorithm (ANS) is developed to select the closest nodes on the safety-aware available paths to generate a final collision free navigation plan while minimizing the travel distance.…”