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Advanced Techniques in Control and Path Planning for Autonomous and Collaborative Robots in Dynamic Environments

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 4351

Special Issue Editor

Dr. Brahim Brahmi

E-Mail Website
Guest Editor
1. Electrical Engineering Department, College Ahuntsic, Montreal, QC H2M 1Y8, Canada
2. Department of Electrical Engineering, Center for Interdisciplinary Research Center for Intelligent Manufacturing & Robotics (IRC-IMR), King Fahd University of Petroleum & Minerals, Dhahran 31261, Eastern Province, Saudi Arabia
Interests: nonlinear and adaptive control; bio-robotics; rehabilitation robots; industrial automation; IoT; fundamental motion control concepts for nonholonomic/underactuated vehicle systems; haptic systems; intelligent and autonomous control of unmanned systems; intelligent systems; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue explores recent advancements in control techniques and path planning for autonomous and collaborative robotic systems in dynamic environments. As robots are increasingly deployed in real-world scenarios with unpredictable obstacles, traditional methods face challenges in real-time decision-making, multi-robot coordination, and efficient navigation. By integrating Artificial Intelligence (AI), Digital Twin technology, and advanced control strategies, this issue highlights innovations that improve robot mobility, perception, and collaboration. AI-driven algorithms enable intelligent trajectory generation, while Digital Twin models facilitate real-time environmental reconstruction for accurate path optimization. Furthermore, the issue delves into collaborative robotics, focusing on communication, coordination, and task sharing between robots for enhanced system performance. This collection aims to provide insights into the future of autonomous and collaborative robotics, addressing both theoretical advancements and practical applications in intelligent navigation and control systems.

Dr. Brahim Brahmi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autonomous path planning
  • collaborative robotics
  • advanced control techniques
  • AI-driven navigation
  • multi-robot coordination

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Published Papers (3 papers)

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Research

23 pages, 13635 KB  
Article
Deep Reinforcement Learning for Autonomous Underwater Navigation: A Comparative Study with DWA and Digital Twin Validation
by Zamirddine Mari, Mohamad Motasem Nawaf and Pierre Drap
Sensors 2026, 26(7), 2179; https://doi.org/10.3390/s26072179 - 1 Apr 2026
Viewed by 641
Abstract
Autonomous navigation in underwater environments is challenged by the absence of GPS, degraded visibility, and submerged obstacles. This article investigates these issues using the BlueROV2, an open platform for scientific experimentation. We propose a deep reinforcement learning approach based on the Proximal Policy [...] Read more.
Autonomous navigation in underwater environments is challenged by the absence of GPS, degraded visibility, and submerged obstacles. This article investigates these issues using the BlueROV2, an open platform for scientific experimentation. We propose a deep reinforcement learning approach based on the Proximal Policy Optimization (PPO) algorithm, using an observation space that combines target-oriented navigation information, a virtual occupancy grid, and raycasting along the boundaries of the operational area. This information is encoded into a high-dimensional observation space of 84 dimensions, providing the agent with comprehensive local and global situational awareness. The learned policy is compared against a reference deterministic kinematic planner, the Dynamic Window Approach (DWA), a robust baseline for obstacle avoidance. The evaluation is conducted in a realistic simulation environment and complemented by validation on a physical BlueROV2 supervised by a 3D digital twin of the test site, reducing risks associated with real-world experimentation. The results show that the PPO policy consistently outperforms DWA in highly cluttered environments, notably thanks to better local adaptation and reduced collisions. Finally, experiments demonstrate the transferability of the learned behavior from simulation to the real world, confirming the relevance of deep RL for autonomous navigation in underwater robotics. Full article
(This article belongs to the Special Issue Advanced Techniques in Control and Path Planning for Autonomous and Collaborative Robots in Dynamic Environments)
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22 pages, 1675 KB  
Article
HybridNER: A Multi-Model Ensemble Framework for Robust Named Entity Recognition—From General Domains to Adversarial GNSS Scenarios
by Yixuan Liu, Jing Zhang, Ruipeng Luan and Xuewen Yu
Sensors 2026, 26(5), 1553; https://doi.org/10.3390/s26051553 - 2 Mar 2026
Viewed by 482
Abstract
Named entity recognition (NER), a core task in natural language processing (NLP), remains constrained by heavy reliance on annotated data, limited cross domain generalization, and difficulty in recognizing name entities out of vocabulary entities. In specialized domains such as analysis of Global Navigation [...] Read more.
Named entity recognition (NER), a core task in natural language processing (NLP), remains constrained by heavy reliance on annotated data, limited cross domain generalization, and difficulty in recognizing name entities out of vocabulary entities. In specialized domains such as analysis of Global Navigation Satellite System (GNSS) countermeasures, including anti-jamming and anti-spoofing, where datasets are small and domain knowledge is scarce, existing models exhibit marked performance degradation. To address these challenges, we propose HybridNER, a framework that integrates locally trained span-based models with large language models (LLMs). The approach employs a span prediction metasystem that first fuses outputs from multiple base learners by computing span to label compatibility scores and assigns an uncertainty estimate to each candidate entity. Entities with uncertainty above a preset threshold are then routed to an LLM for a second stage classification, and the final decision integrates both sources to realize complementary strengths. Experiments on multiple general purpose and domain specific datasets show that HybridNER achieves higher precision, recall, and F1 than traditional ensemble methods such as majority voting and weighted voting, with especially pronounced gains in specialized domains, thereby improving the robustness and generalization of NER. Full article
(This article belongs to the Special Issue Advanced Techniques in Control and Path Planning for Autonomous and Collaborative Robots in Dynamic Environments)
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32 pages, 12557 KB  
Article
Controlling an Industrial Robot Using Stereo 3D Vision Systems with AI Elements
by Jarosław Panasiuk
Sensors 2025, 25(20), 6402; https://doi.org/10.3390/s25206402 - 16 Oct 2025
Cited by 4 | Viewed by 2751
Abstract
Robotization of production processes and the use of 3D vision systems are currently becoming more and more popular. It allows for more flexibility in the robotic process as well as expands the possibilities of process control, depending on changes in the parameters of [...] Read more.
Robotization of production processes and the use of 3D vision systems are currently becoming more and more popular. It allows for more flexibility in the robotic process as well as expands the possibilities of process control, depending on changes in the parameters of the object, its pose, and changes in the process itself. Unfortunately, the use of standard solutions is limited to a relatively small space in which the robot’s vision system operates. The use of the latest solutions in the field of Artificial Intelligence (AI) and external vision systems, in combination with the closed structures of industrial robot control systems, provides advantages by enhancing the digital awareness of the environment of robotic systems. This article presents an example of solving the problem of low digital awareness of the environment of robotic systems resulting from the limited field of view of vision systems used in industrial robots, while maintaining high precision of the systems consisting of the combination of a 3D vision system using a stereovision camera and software with AI elements with the control system of an industrial robot from FANUC and an integrated Robot Vision (iRVision) system to maintain the positioning accuracy of the robot tool. Full article
(This article belongs to the Special Issue Advanced Techniques in Control and Path Planning for Autonomous and Collaborative Robots in Dynamic Environments)
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