This paper presents a comprehensive review of path planning in dynamic environments. This review covers the entire process, starting from obstacle detection techniques, through path-planning strategies, and also extending to formation control and communication styles. The review discusses the key trends, challenges, and gaps in current methods to emphasize the need for more efficient and robust algorithms that can handle complex and unpredictable dynamic environments. Moreover, it discusses the importance of collaborative decision making and communication between robots to optimize path planning in dynamic scenarios. This work serves as a valuable resource for advancing research and practical applications in dynamic obstacle navigation.

Summary: This paper presents an advanced robust active disturbance rejection control (ADRC) for flexible link manipulator (FLM) to track desired trajectories in the joint space and minimize the link’s vibrations. It has been shown that the ADRC technique has a very good disturbance rejection capability. Both the internal dynamics and the external disturbances can be estimated and compensated in real time. The proposed robust ADRC control law is developed to solve the problems existing in the original version of the ADRC related to the disturbance estimation errors and the variation of the parameters. Indeed, these parameters cannot be included in the existing disturbances and then be estimated by the extended state observer. The proposed control law is based on the sliding mode technique, which considers the uncertainties in the control gains and disturbance estimation errors. Lyapunov theory is used to prove the closed-loop stability of the system. The proposed control strategy is simulated and tested experimentally on one FLM. The effect of the observer bandwidth on the system performance is simulated and studied to select the best values of the bandwidth frequency. The simulation and experimental results show that the proposed robust ADRC has better performance than the traditional ADRC.

Several recent studies have indicated that upper extremity injuries are classified as a top common workplace injury. Therefore, upper extremity rehabilitation has become a leading research area in the last few decades. However, this high number of upper extremity injuries is viewed as a challenging problem due to the insufficient number of physiotherapists. With the recent advancements in technology, robots have been widely involved in upper extremity rehabilitation exercises. Although robotic technology and its involvement in the rehabilitation field are rapidly evolving, the literature lacks a recent review that addresses the updates in the robotic upper extremity rehabilitation field. Thus, this paper presents a comprehensive review of state-of-the-art robotic upper extremity rehabilitation solutions, with a detailed classification of various rehabilitative robots. The paper also reports some experimental robotic trials and their outcomes in clinics.

There has been a continuous interest in multi-robot formation systems in the last few years due to several significant advantages such as robustness, scalability, and efficiency. However, multi-robot formation systems suffer from well-known problems such as energy consumption, processing speed, and security. Therefore, developers are continuously researching for optimal solutions that can gather the benefits of multi-robot formation systems while overcoming the possible challenges. A backbone process required by any multi-robot system is path planning. Thus, path planning for multi-robot systems is a recent top research topic. However, the literature lacks a recent comprehensive review of path planning works designed for multi-robot systems. The aim of this review paper is to provide a comprehensive assessment and an insightful look into various path planning techniques developed in multi-robot formation systems, in addition to highlighting the basic problems involved in this field. This will allow the reader to discover the research gaps that must be solved for a better path planning experience for multi-robot formation systems. Finally, an illustrative comparative example is presented at the end of the paper to show the advantages and disadvantages of some popular path planning techniques.

The traditional trade-off between execution speed and path quality has forced real-time robotic path planning algorithms to sacrifice path quality in order to execute in real-time. Producing a path planning algorithm that targets enhancing both, the path quality and swiftness is a challenging problem. However, this paper proposes a novel path planning strategy that aims to break this traditional trade-off, by targeting both, increasing the swiftness, and enhancing the path quality represented by the path length and smoothness. The proposed strategy is based on the observation that most path planning algorithms waste the processing efforts in less critical areas of the map. Therefore, the proposed path planning strategy tends to focus on critical areas such as the areas around obstacles and areas around the goal point, and exhausts the processing power on these critical areas. This is done by neglecting all static obstacles that do not lie between the robot and the destination. For obstacles that intersect with the linear line from the robot to the destination, a basis traditional path planning algorithm such as A*, D* or the Probabilistic RoadMap (PRM) technique is only implemented around the obstacles in order to find a feasible path around each selected obstacle. This procedure would minimize the computational efforts compared to applying the basis algorithm on the whole map. Finally, the path quality is enhanced by finding any linear shortcuts between any two points in the path and fix these shortcuts as the final path from the starting point to the goal point. The proposed path planning strategy was tested on a P3-DX Pioneer mobile robot using a kinematic controller. The experimental results have proven that the path planning strategy was able to show a superior advantage over other path planning techniques in both aspects, computational time (reached up to 97.05% improvement) and path quality (reached up to 16.21% improvement for path length and 98.50% for smoothness).

Summary: This paper presents a vision-based path planning strategy that aims to reduce the computational time required by a robot to find a feasible path from a starting point to the goal point. The proposed algorithm presents a novel strategy that can be implemented on any well-known path planning algorithm such as A*, D* and probabilistic roadmap (PRM), to improve the swiftness of these algorithms. This path planning algorithm is suitable for real-time scenarios since it reduces the computational time compared to the basis and traditional algorithms. To test the proposed path planning strategy, a tracking control strategy is implemented on a mobile platform. This control strategy consists of three major stages. The first stage deals with gathering information about the surrounding environment using vision techniques. In the second stage, a free-obstacle path is generated using the proposed reduced scheme. In the final stage, a Lyapunov kinematic tracking controller and two Artificial Neural Network (ANN) based-controllers are implemented to track the proposed path by adjusting the rotational and linear velocity of the robot. The proposed path planning strategy is tested on a Pioneer P3-DX differential wheeled mobile robot and an Xtion PRO depth camera. Experimental results prove the efficiency of the proposed path planning scheme, which was able to reduce the computational time by a large percentage which reached up to 88% of the time needed by the basis and traditional scheme, without significant adverse effect on the workability of the basis algorithm. Moreover, the proposed path planning algorithm has improved the path efficiency, in terms of the path length and trackability, challenging the traditional trade-off between swiftness and path efficiency.