In the design of a controller for grasping objects through a robotic manipulator, there are two key problems: to find the position of the object to be grasped accurately, and to apply the appropriate force to each finger to handle the object properly without causing undesirable movement of it during its manipulation. A proportional-integral-derivative (PID) controller is widely used to grasp objects in robotics; however, its main shortcomings are its sensitivity to controller gains, sluggish response, and high starting overshooting. This research presents three coupled (position/force) controllers for object manipulation using an assembled robotic manipulator (i.e., a gripper attached to a robotic arm mounted on a mobile robot). Specifically, an angular gripper was employed in this study, which was composed of two independent fingers with a piezoelectric force sensor attached to each fingertip. The main contributions of this study are the designs and implementations of three controllers: a classic PID controller, a type-I controller, and a type-II fuzzy controller. These three controllers were used to find an object to be grasped properly (position) and apply an equivalent force to each finger (force).

Last-mile logistics is a critical operational and environmental challenge in urban areas. This paper introduces an intelligent path planning system using the Resistive Grid Path Planning Methodology (RGPPM) to optimize distribution based on energy and environmental metrics. The foundational innovation is the integration of electrical-circuit analogies, modeling the distribution network as a resistive grid where optimal routes emerge naturally as current flows, offering a paradigm shift from conventional algorithms. Using a multi-connected grid with georeferenced resistances, RGPPM estimates minimum and maximum paths for various starting points and multi-agent scenarios. We introduce five key performance indicators (KPIs)—Percentage of Distance Savings (PDS), Coefficient of Savings (CS), Coefficient of Global Savings (CGS), Percentage of Load Imbalance (PLI), and Percentage of Deviation with Multi-Agent (PDM)—to evaluate system performance. Simulations for textbook delivery to 129 schools in the Veracruz–Boca del Río area show that RGPPM significantly reduces travel distances. This leads to substantial savings in energy consumption, CO₂ emissions, and operating costs, particularly with electric vehicles. Finally, the results validate RGPPM as a flexible and scalable strategy for sustainable urban logistics.

This work proposes a vision system mounted on the head of an omnidirectional robot to track pineapples and maintain them at the center of its field of view. The robot head is equipped with a pan–tilt unit that facilitates dynamic adjustments. The system architecture, implemented in Robot Operating System 2 (ROS2), performs the following tasks: it captures images from a webcam embedded in the robot head, segments the object of interest based on color, and computes its centroid. If the centroid deviates from the center of the image plane, a proportional–integral–derivative (PID) controller adjusts the pan–tilt unit to reposition the object at the center, enabling continuous tracking. A multivariate Gaussian function is employed to segment objects with complex color patterns, such as the body of a pineapple. The parameters of both the PID controller and the multivariate Gaussian filter are optimized using a genetic algorithm. The PID controller receives as input the (x, y) positions of the pan–tilt unit, obtained via an embedded board and MicroROS, and generates control signals for the servomotors that drive the pan–tilt mechanism. The experimental results demonstrate that the robot successfully tracks a moving pineapple. Additionally, the color segmentation filter can be further optimized to detect other textured fruits, such as soursop and melon. This research contributes to the advancement of smart agriculture, particularly for fruit crops with rough textures and complex color patterns.