Abderraouf Maoudj

Reliable legged locomotion in unstructured environments requires the integration of fast local response, terrain-aware gait adaptation, and longer-horizon navigation. In many existing systems, these functions are implemented in separate modules, which weakens real-time interaction between low-level control and high-level planning. This paper presents a Hierarchical Perception-Action Coupling (HiPAC) framework organized as a three-level control hierarchy supported by a sensory layer embedded in the onboard robot controller, operating at different time scales. The framework comprises a reactive layer for immediate terrain response and gait execution, a context layer for local environmental recognition and locomotion modulation, and a planning layer for cognitive-map construction, path generation, and replanning. The sensory layer acquires exteroceptive and proprioceptive measurements and forwards them to the higher layers through a lightweight topological representation. In HiPAC, the layers exchange compact topological representations instead of dense raw sensory data, which keeps memory and computation bounded. The framework is evaluated in physics-based simulation on a quadruped robot across flat, rough, and multi-story environments, and is further demonstrated in real–robot deployment. The results show that the reactive layer maintains response times below 30 ms, the proposed topological attention mechanism reduces processing cost by up to 70% relative to uniform-density representations, and local environment reconstruction remains below 18 kB during extended exploration. These findings show that HiPAC provides an efficient architecture for combining real-time terrain response, local adaptation, and global navigation in legged robots.

Collaborative robots (cobots) have revolutionized industrial automation by enabling seamless human–robot interaction in shared workspaces. These advanced robots are designed to work alongside humans, enhancing productivity, efficiency, and safety through sophisticated sensing and control capabilities. This review provides a comprehensive analysis of collaborative robotics, while introducing the Internet of Cobots (IoC) as a pivotal enabler of next-generation manufacturing systems. Through IoC capabilities, cobots are transforming industrial environments by implementing advanced safety protocols, enabling dynamic task allocation, and achieving seamless integration with human workers over 5G networks. This paper synthesizes the key technological advancements, starting from fundamental sensing, control, and actuation architectures to sophisticated artificial intelligence and machine learning algorithms that enable human-like adaptability. Special attention is given to the industrial safety norms that enhance workplace safety of the IoC through real-time data sharing of job processing, human proximity, and potential hazards, while building trust in the decision-making of human–robot collaboration. This review highlights emerging trends within the Industry 5.0 paradigm, where IoC-enabled cobots drive the human-centric and personalized manufacturing processes. By providing a comprehensive overview of networked cobots, their transformative impact, and regulatory frameworks, this review offers a valuable insight into the future of intelligent, adaptable, and human-centred industrial automation.

Industrial robots are extensively employed in a variety of surface industrial processes. To increase productivity, automated manufacturing systems operate in the factory on the physical product to perform operations such as processing, material handling, polishing and sometimes accomplishing more than one of these operations in the same system. It is necessary to locate objects or its features as rapidly as possible so that further image-processing algorithms can extract features. Robotic-based pick-and-place applications can be achieved by determining the correct orientation of a part within 2D or 3D space. A machine vision image acquisition hardware platform was set up, HALCON software used for noise filter, threshold segmentation and edge detection of the workpiece image to comply with identification accuracy requirements. Based on the preprocessing result, the workpiece features information extracted. Integration of the machine vision system allows the polishing system to simultaneously processing a variety of workpieces identifying the shape, size and color on the test bench. The workpiece was classified by these characteristics and the classification information is output to the controller. The recognition result is output to the PLC to generate the corresponding polishing paths, reduce time of operation and increase the flexible processing capacity.

This paper presents an efficient motion control approach for indoor mobile robots in both static and dynamic workspaces. The proposed system consists of two layers. In the Optimization Layer (Global planner), a Deterministic Constructive Algorithm (DCA) generates the best path for the mobile robot, represented as a set of nodes, to move from the initial position (Source) to the final position (Target) while avoiding the static obstacles. The Control layer (Local planner), which is the main focus of this article, consists of an Efficient Fuzzy-Logic Controller (EFLC), mimicking the human reasoning, to avoid the dynamic obstacles and drive safely the mobile robot along the generated path. The strength of the proposed EFLC approach is shown using V-REP software in dynamic workspaces.