Wireless Sensor Networks (WSNs) are used to monitor specific areas of the environment by networking multiple sensors and collecting data for analysis. However, due to limited processing capabilities, the collected dat...
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Wireless Sensor Networks (WSNs) are used to monitor specific areas of the environment by networking multiple sensors and collecting data for analysis. However, due to limited processing capabilities, the collected data needs to be transmitted to a Base Station (BS) that has high computational power and storage capacity. As SNs are not directly connected to the BS, multihop data transmissions through other SNs are required to reach the BS, which leads to congestion and additional energy consumption. To address these challenges, we propose a novel algorithm called Congestion, Delay, Energy-aware Intelligent Routing (CDEIR) using the bug algorithm. The CDEIR approach identifies congested nodes using a Directed Spanning Tree and avoids traffic through them by paving an alternate path. This approach minimizes delay and optimizes energy consumption while avoiding congestion, all within a short computational time. We demonstrate the effectiveness of the CDEIR approach through theoretical analyses and simulations.
Localization and navigation is a crucial task of autonomous robots. This paper introduces Ladybug a novel bug algorithm. Using the Received Signal Strength Indication (RSSI) of an electromagnetic signal, the algorithm...
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ISBN:
(纸本)9781728189567
Localization and navigation is a crucial task of autonomous robots. This paper introduces Ladybug a novel bug algorithm. Using the Received Signal Strength Indication (RSSI) of an electromagnetic signal, the algorithm is able to accurately calculate the position of the beacon emitting the aforementioned signal. Various experiments were performed with a simulated robot equipped only with local sensors. The proposed algorithm was compared with similar approaches with very promising results.
Considering the motion constraints of the robot, an autonomous navigation method based on bug algorithm was proposed. Combining Dubins path and bug algorithm, in the process a robot moving from initial pose position t...
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ISBN:
(纸本)9781538635247
Considering the motion constraints of the robot, an autonomous navigation method based on bug algorithm was proposed. Combining Dubins path and bug algorithm, in the process a robot moving from initial pose position to final pose position, a path satisfied the robot's kinematics parameters was planned. Firstly, the robot moved along the initial Dubins path from initial pose position to final pose position with sensing the environment information under the prescribed condition. Once there being obstacles blocking the current path, generate an intermediate pose position to avoid the obstacle, and then calculate two Dubins paths from current pose position to intermediate pose position and from intermediate pose position to final pose position in order to get an updated planning path. Finally, make the robot move along the updated planning path. Repeat the process of moving, sensing, generating intermediate pose position and updating path until the robot achieves the final pose position without collision. The simulation results show that the proposed method is safe, efficient and easy to implement.
This paper introduces a real-time collision avoidance algorithm for a collaborative robot in a work cell with a human operator. Collaborative robots (cobots) are becoming increasingly popular in industrial environment...
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ISBN:
(纸本)9781665490481
This paper introduces a real-time collision avoidance algorithm for a collaborative robot in a work cell with a human operator. Collaborative robots (cobots) are becoming increasingly popular in industrial environments because they enable close collaboration between a human and a robot. Unlike traditional robots, cobot installations do not require a safety cage around the robot. This is because the cobot can come to a stop if it collides with a human. Although the cobot can stop, it is not a pleasant or safe working environment for the human to be hit by the cobot possibly multiple times a day. The advantage of the new approach is its algorithmic simplicity and use of inexpensive proximity sensors on the cobot. Results from pick-and-place experiments show significant improvements in reduction of collisions as the algorithm enables the cobot to maneuver around the human operator in the cell.
This paper presents an adaptation of the mobile robot bug algorithm to control a cobot to reduce collisions with a human worker in the same work cell. Collaborative robots (cobots) are popular in industry because they...
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ISBN:
(纸本)9781665489218
This paper presents an adaptation of the mobile robot bug algorithm to control a cobot to reduce collisions with a human worker in the same work cell. Collaborative robots (cobots) are popular in industry because they enable close collaboration between a human and a robot. Although the cobot can stop when a collision happens, it is not a pleasant or safe working environment for the human to be hit by the cobot possibly multiple times a day. The proposed approach uses inexpensive proximity sensors on the cobot and a simple algorithm. An adjustable yaw angle is introduced to the algorithm to further reduce collisions. Results from pick-and-place experiments with UR 10 cobot show significant reduction of collisions.
We introduce a hybrid algorithm for the autonomous navigation of an Unmanned Ground Vehicle (UGV) using visual topological maps. The main contribution of this paper is the combination of the classical bug algorithm wi...
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ISBN:
(纸本)9783319188331;9783319188324
We introduce a hybrid algorithm for the autonomous navigation of an Unmanned Ground Vehicle (UGV) using visual topological maps. The main contribution of this paper is the combination of the classical bug algorithm with the entropy of digital images captured for the robot. As the entropy of an image is directly related to the presence of a unique object or the presence of different objects inside the image (the lower the entropy of an image, the higher its probability of containing a single object inside it;and conversely, the higher the entropy, the higher its probability of containing several different objects inside it), we propose to implement landmark search and detection using topological maps based on the bug algorithm, where each landmark is considered as the leave point for guide to the robot to reach the target point (robot homing). The robot has the capacity of avoid obstacles in the enviroment using the entropy of images too. After the presentation of the theoretical foundations of the entropy-based search combined with the bug algorithm, the paper ends with the experimental work performed for its validation.
A collaborative robot, or cobot, is an efficient robot that can work alongside human workers in the same work cell. Cobots are popular in industry because they allow close collaboration between humans and robots. Alth...
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A collaborative robot, or cobot, is an efficient robot that can work alongside human workers in the same work cell. Cobots are popular in industry because they allow close collaboration between humans and robots. Although a cobot can stop when a collision occurs, it is not safe or pleasant for the human worker to be hit by the cobot multiple times a day. Therefore, it is important to reduce or eliminate collisions between cobots and human workers. This research uses inexpensive proximity sensors on the cobot to detect obstacles and generate motion decisions. First, a modified bug algorithm is utilized to maneuver the cobot to achieve a pick-and-place task. This algorithm applies a wrist yaw angle to move around the human workers and avoid collisions in the same work cell. Second, a long short-term memory (LSTM)-based artificial neural network was trained using a collision-free path dataset. The network model generates corrections when abnormal movements in the modified algorithm are detected due to interference in sensor *** of pick-and-place tasks considered different locations and orientations of human workers, as well as dynamic scenarios. The results show a significant reduction in collisions using the proposed bug algorithm and machine learning supervisory control strategy. The supervisory control method has the ability to find additional solutions to reach the target and reduce the number of steps needed to explore potential collision-free paths.
This paper proposes a new sensor-based path planning and collision avoidance algorithm for non-holonomic robot travelling in unknown 2D environment named as (M-bug) algorithm in order to reduce the overall of traveled...
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ISBN:
(纸本)9781728152769
This paper proposes a new sensor-based path planning and collision avoidance algorithm for non-holonomic robot travelling in unknown 2D environment named as (M-bug) algorithm in order to reduce the overall of traveled path. It uses the difference between slope lines to find and choose the smallest angle located between slope of straight line from mobile robot to goal point and slope from mobile robot and two endpoints of edges for obstacle, by choosing the smallest one which refers to nearest leave point to reach goal point and repeat this action with other obstacles until mobile robot reach to goal point. this algorithm based on using on-board sensors to generate information about the environment and obstacle located in front of mobile robot. This algorithm creates better performance when compared to the other traditional algorithms. It provides real time obstacle avoidance, compatible with LIDAR sensor and fast enough to be implemented on embedded microcontrollers. The used algorithm is based on a modification for existing path planning and obstacle voidance algorithms (bug1, bug2, Tangent-bug, K-bug, Dist-bug ... etc.), and The Vector Field Histogram (VFH) algorithm in unknown environment. The main advantage of this algorithm is that it is not limited to the origin-destiny line and changes the main line every leave point. MATLAB 2018a is used in simulation to validate the effectiveness of the proposed algorithm, and it is implemented on multiple scenarios to show the ability of the mobile robot to follow a path, detect obstacles, and navigate around them to avoid collision.
One of the primary ability of an intelligent mobile robot system is obstacle avoidance. bug algorithms are classic examples of the algorithms used for achieving obstacle avoidance. Unlike many other planning algorithm...
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One of the primary ability of an intelligent mobile robot system is obstacle avoidance. bug algorithms are classic examples of the algorithms used for achieving obstacle avoidance. Unlike many other planning algorithms based on global knowledge, bug algorithms assume only local knowledge of the environment and a global goal. Among the variations of the bug algorithms that prevail, bug-0, bug-1 and bug-2 are the more prominent versions. The exhaustive search algorithm present in bug-1 makes it more reliable and safer for practical applications. Overall, this provides a more predictable and dependable performance. Hence, the essential focus in this paper is on implementing the bug-1 algorithm across a group of robots to move them from a start location to a target location. The results are compared with the results from bug-1 algorithm implemented on a single robot. The strategy developed in this work reduces the time involved in moving the robots from starting location to the target location. Further, the paper shows that the total distance covered by each robot in a multi robot-system is always lesser than or equal to that travelled by a single robot executing the same problem.
Mobile robots frequently find themselves in a circumstance where they need to find a trajectory to another position in their environment, subject to constraints postured by obstacles and the capabilities of the robot ...
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ISBN:
(纸本)9783038351801
Mobile robots frequently find themselves in a circumstance where they need to find a trajectory to another position in their environment, subject to constraints postured by obstacles and the capabilities of the robot itself. This study compared path planning algorithms for mobile robots to move efficiently in a collision free grid based static environment. Two algorithms have been selected to do the comparison namely wavefront algorithm and bug algorithm. The wavefront algorithm involves a breadth-first search of the graph beginning at the goal position until it reaches the start position. The bug algorithm uses obstacles borders as guidance toward a goal with restricted details about the environment. The algorithms are compared in terms of parameters such as execution time of the algorithm and planned path length by using Player/Stage simulation software. Results shown that wavefront algorithm is a better path planning algorithm compared to bug algorithm in static environment.
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