Within Human-Robot Collaboration (HRC) safety is one key-issue that has to be guaranteed at any time during joint collaboration. Collisions in a shared workspace of a Human-Robot-Team (HRT) must be prevented. In addition, the comfort of the collaboration behavior should be provided. Facing these challenges, a robot has to be able to detect critical states at an early stage on the one hand and should react to them within a very short time span on the other hand. In this paper a collision avoidance algorithm using compliance control that guarantees a fast reaction to dynamic obstacles, e.g. humans, without the need of high computational effort is outlined. To further improve the avoidance behavior of the robot, a human motion prediction algorithm based on the minimum-jerk model is integrated. In an experimental analysis of a case-study about collecting LEGO-bricks on a table with various subjects, the impact of the integration of human motion prediction on both the robot's reaction time and human's perception of the robot co-worker is studied. Finally, the comfort and acceptance of the robot colleague by the human collaborator is drawn out through an analysis of the subjective human feedback questionnaires.

In the context of human-robot collaboration in close proximity, safety and comfort are the two important aspects to achieve joint tasks efficiently. For safety, the robot must be able to avoid dynamic obstacles such as a human arm with high reliability. For comfort, the trajectories and avoidance behavior of the robot need to be predictable to the humans. Moreover, these two aspects might be different from person to person or from one task to another. This work presents a framework to generate predictable motions with dynamic obstacle avoidance for the robot interacting with the human by using policy improvement method. The trajectories are generated using Dynamic Motion Primitives with an additional potential field term that penalizes trajectories that may lead to collisions with obstacles. Furthermore, human movements are predicted using a data-driven approach for proactive avoidance. A cost function is defined which measures different aspects that affect the comfort and predictability of human co-workers (e.g., human response time, joint jerk). This cost function is then minimized during human-robot interaction by the means of policy improvement through black-box optimization to generate robot trajectories that adapt to human preferences and avoid obstacles. User studies are performed to evaluate the trust and comfort of human co-workers when working with the robot. In addition, the studies are also extended to various scenarios and different users to analyze the task transferability. This improves the learning performance when switching to a new task or the robot has to adapt to a different co-worker.

This paper introduces a new approach to optimal online motion planning for human-robot interaction scenarios. For a safe, comfortable, and efficient interaction between human and robot working in close proximity, robot motion has to be agile and perceived as natural by the human partner. The robot has to be aware of its environment, including human motions, in order to proactively take actions while ensuring safety, and task fulfillment. Human motion prediction constitutes the fundamental perception input for the motion planner. The prediction system, which is based on probabilistic movement primitives, generates a prediction of human motion as a trajectory distribution learned in an offline phase. The proposed stochastic optimization-based planning algorithm then progressively finds feasible optimization parameters to replan the motion online that ensures collision avoidance while minimizing the task-related trajectory cost. Our simulation results show that the proposed approach produces collision-free trajectories while still reaching the goal successfully. We also highlight the performance of our planner in comparison to previous methods in stochastic motion planning.

Plastic pollution is a growing environmental and social concern, particularly in Southeast Asia, where urban rivers serve as key pathways for transporting waste to marine environments. This scoping review examines 110 peer-reviewed studies to understand how plastic pollution in waterways is being researched, addressed, and reconceptualized. Drawing from the literature across environmental science, technology, and social studies, we identify four interconnected areas of focus: urban pollution pathways, innovations in monitoring and methods, community-based interventions, and interdisciplinary perspectives. Our analysis combines qualitative synthesis with visual mapping techniques, including keyword co-occurrence networks, to explore how real-time tools, such as IoT sensors, multi-sensor systems, and geospatial technologies, are transforming the ways plastic waste is tracked and analyzed. The review also considers the growing use of novel theoretical frameworks, such as post-phenomenology and ecological materialism, to better understand the role of plastics as both pollutants and ecological agents. Despite progress, the literature reveals persistent gaps in longitudinal studies, regional representation, and policy translation, particularly across the Global South. We emphasize the value of participatory models and community-led research in bridging these gaps and advancing more inclusive and responsive solutions. These insights inform the development of plastic tracker technologies currently being piloted in Vietnam and contribute to broader sustainability goals, including SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water).

This paper presents a new approach for the optimal trajectory planning of nonlinear systems in a dynamic environment. Given the start and end goals with an objective function, the problem is to find an optimal trajectory from start to end that minimizes the objective while taking into account the changes in the environment. One of the main challenges here is that the optimal control sequence needs to be computed in a limited amount of time and needs to be adapted on-the-fly. The control method presented in this work has two stages: the first-order gradient algorithm is used at the beginning to compute an initial guess of the control sequence that satisfies the constraints but is not yet optimal; then, sequential action control is used to optimize only the portion of the control sequence that will be applied on the system in the next iteration. This helps to reduce the computational effort while still being optimal with regard to the objective; thus, the proposed approach is more applicable for online computation as well as dealing with dynamic environments. We also show that under mild conditions, the proposed controller is asymptotically stable. Different simulated results demonstrate the capability of the controller in terms of solving various tracking problems for different systems under the existence of dynamic obstacles. The proposed method is also compared to the related indirect optimal control approach and sequential action control in terms of cost and computation time to evaluate the improvement of the proposed method.

This paper introduces an extension to a method for fast and close to optimal trajectory generation for articulated robots in the case where dynamic constraints and real-time capability are considered. We aim at solving reaching motion problems without pre-defined timing requirements, where the robot starts from its current state trying to reach the final desired state. Our approach combines the advantage of Sequential Action Control (SAC) with an indirect optimization process to further improve optimality and not violate final state constraints while still being applicable for online implementation. Simulation results on a 2 degree of freedom (DOF) and a 3 DOF KUKA-based platform are evaluated to show the improvement of our method in comparison to SAC and other optimal control methods in terms of cost efficiency and computation time.