RPF-Search: Field-based Search for Robot Person Following in Unknown Dynamic Environments

Autonomous robot person-following (RPF) systems are crucial for personal assistance and security but suffer from target loss due to occlusions in dynamic, unknown environments. Current methods rely on pre-built maps and assume static environments, limiting their effectiveness in real-world settings. There is a critical gap in re-finding targets under topographic (e.g., walls, corners) and dynamic (e.g., moving pedestrians) occlusions. In this paper, we propose a novel heuristic-guided search framework that dynamically builds environmental maps while following the target and resolves various occlusions by prioritizing high-probability areas for locating the target. For topographic occlusions, a belief-guided search field is constructed and used to evaluate the likelihood of the target's presence, while for dynamic occlusions, a fluid-field approach allows the robot to adaptively follow or overtake moving occluders. Past motion cues and environmental observations refine the search decision over time. Our results demonstrate that the proposed method outperforms existing approaches in terms of search efficiency and success rates, both in simulations and real-world tests. Our target search method enhances the adaptability and reliability of RPF systems in unknown and dynamic environments to support their use in real-world applications. Our code, video, experimental results and appendix are available at https://medlartea.github.io/rpf-search/.

Motion Planning for Robotics: A Review for Sampling-based Planners

Recent advancements in robotics have transformed industries such as manufacturing, logistics, surgery, and planetary exploration. A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length, sweep area, execution time, and energy consumption. Among the available algorithms, sampling-based methods have gained the most traction in both research and industry due to their ability to handle complex environments, explore free space, and offer probabilistic completeness along with other formal guarantees. Despite their widespread application, significant challenges still remain. To advance future planning algorithms, it is essential to review the current state-of-the-art solutions and their limitations. In this context, this work aims to shed light on these challenges and assess the development and applicability of sampling-based methods. Furthermore, we aim to provide an in-depth analysis of the design and evaluation of ten of the most popular planners across various scenarios. Our findings highlight the strides made in sampling-based methods while underscoring persistent challenges. This work offers an overview of the important ongoing research in robotic motion planning.

Curiosity-based Robot Navigation under Uncertainty in Crowded Environments

Mobile robots have become more and more popular in our daily life. In large-scale and crowded environments, how to navigate safely with localization precision is a critical problem. To solve this problem, we proposed a curiosity-based framework that can find an effective path with the consideration of human comfort, localization uncertainty, crowds, and the cost-to-go to the target. Three parts are involved in the proposed framework: the distance assessment module, the curiosity gain of the information-rich area, and the curiosity negative gain of crowded areas. The curiosity gain of the information-rich area was proposed to provoke the robot to approach localization referenced landmarks. To guarantee human comfort while coexisting with robots, we propose curiosity gain of the spacious area to bypass the crowd and maintain an appropriate distance between robots and humans. The evaluation is conducted in an unstructured environment. The results show that our method can find a feasible path, which can consider the localization uncertainty while simultaneously avoiding the crowded area. Curiosity-based Robot Navigation under Uncertainty in Crowded Environments

Mobile Robot Path Planning in Dynamic Environments: A Survey

The environment that the robot operating in is becoming more and more complex, which poses great challenges on robot navigation. This paper gives an overview of the navigation framework for robot running in dense environment. The path planning in the navigation framework of mobile robots is divided into global planning and local planning according to the planning scope and the executability. Robot navigation is a multi-objective problem, which not only needs to complete the given tasks but also needs to simultaneously maintain the social comfort level. Consequently, we focus on the reinforcement learning-based path planning algorithms and analyze the development status, advantages, and disadvantages of the existing algorithms. Besides, path planning in a dynamic environment for robots will be further studied in the future in the areas of the advanced algorithm, hybrid algorithm, mu10.15878/j.cnki.instrumentation.2019.02.010lti-robot collaboration, social model, and artificial intelligence algorithm combination.