In this work, we present a complete hybrid navigation system for a two-wheel differential drive mobile robot that includes static-environment- global-path planning and dynamic environment obstacle-avoidance tasks. By the given map, we propose a multi-agent A-heuristic algorithm for finding the optimal obstacle-free path. The result is less time-consuming and involves fewer changes in path length when dealing with multiple agents than the ordinary A-heuristic algorithm. The obtained path was smoothed based on curvature-continuous piecewise cubic Bézier curve (C2 PCBC) before being used as a trajectory by the robot. In the second task of the robot, we supposed any unforeseen obstacles were recognized and their moving frames were estimated by the sensors when the robot tracked on the trajectory. In order to adapt to the dynamic environment with the presence of constant velocity obstacles, a weighted-sum model (WSM) was employed. The 2D LiDAR data, the robot’s frame and the detected moving obstacle’s frame were collected and fed to the WSM during the movement of the robot. Through this information, the WSM chose a temporary target and a C2 PCBC-based subtrajectory was generated that led the robot to avoid the presented obstacle. Experimentally, the proposed model responded well in existing feasible solution cases with fine-tuned model parameters. We further provide the re-path algorithm that helped the robot track on the initial trajectory. The experimental results show the real-time performance of the system applied in our robot.

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In this study, a microscopic model for a swarm of mobile robots is developed to implement self-organized aggregation behavior. The proposed model relies on the concept of subjective expectation, which is defined as the “minimum wished cluster size” of a robot in the swarm. During the whole process, a robot’s expectation constantly changes based on context awareness. This awareness is obtained by employing a low-cost communication system commonly found in swarm robot studies: infrared-based communication. Robots can make their own decisions by comparing their expected and estimated observed cluster sizes, which leads to macroscopic swarm aggregation. However, due to the limitations of local communication and mobility, robots are restricted in their ability to perceive global information, particularly regarding cluster size. Inspired by the slime mold aggregation process, a wave-based communication mechanism is implemented to help robots estimate a cluster size. Moreover, each transmission includes a modulated message that allows robots to explicitly share their knowledge with others. In this way, despite the fact that the robot may not belong to that cluster due to its perception range, it can easily grasp the cluster size when passing the cluster. Once a robot detects a desired cluster, it can approach this cluster with its direction determined by using average origin of wave (AOW) method. The performance of the aggregation algorithm was tested both in simulation and with a real swarm robot. Dispersion metrics and cluster metrics were employed to evaluate the proposed algorithm’s performance. The results show that the proposed microscopic model utilizes collective behavior which aggregates all randomly distributed robots into a single aggregate cluster with a reasonable swarm density and evaluation time.

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Cable-driven parallel manipulators (CDPMs) have been of great interest to researchers in recent years because they have many advantages compared to the traditional parallel robot. However, in many studies they lack the cable’s elasticity that leads to flexible cables just being considered as extendable rigid links. Furthermore, an external force acts on the extremities of cable and the self-weight is relevant to the length of it. Experimentally, a small change in length produces a huge change in tension act on the entire cable. By this property, the adjusting length of cable is often added to the traditional inverse kinematic solution in order to reduce the tension force exerted on the cable. This means that the load on the actuator is also reduced. Because of the relationship between tension that acts on the cable and its length, the kinematic problem itself does not make sense without concerning the static or dynamic problems. There is often interest in planning forces for actuators and the length of cables based on a given quasi-static trajectory of the moving platform. The mentioned problem is combined with the quasi-static problem with the inverse kinematic problem of CDPM. In this study, we introduce a novel procedure to produce the quasi-static model and inverse kinematic model for CDPM with the presence of sagging by using both an analytic approach and empirical approach. The produced model is time-efficient and is generalized for spatial CDPM. To illustrate the performance of the proposed model, the numerical and experimental approaches are employed to determine particular solutions in the feasible solutions set produced by our model in order to control the two redundant actuators’ CDPM tracking on a certain desired trajectory. Its results are clearly described in the experimental section.