Quantitative Hardness Assessment with Vision-based Tactile Sensing for Fruit Classification and Grasping

Accurate estimation of fruit hardness is essential for automated classification and handling systems, particularly in determining fruit variety, assessing ripeness, and ensuring proper harvesting force. This study presents an innovative framework for quantitative hardness assessment utilizing vision-based tactile sensing, tailored explicitly for robotic applications in agriculture. The proposed methodology derives normal force estimation from a vision-based tactile sensor, and, based on the dynamics of this normal force, calculates the hardness. This approach offers a rapid, non-destructive evaluation through single-contact interaction. The integration of this framework into robotic systems enhances real-time adaptability of grasping forces, thereby reducing the likelihood of fruit damage. Moreover, the general applicability of this approach, through a universal criterion based on average normal force dynamics, ensures its effectiveness across a wide variety of fruit types and sizes. Extensive experimental validation conducted across different fruit types and ripeness-tracking studies demonstrates the efficacy and robustness of the framework, marking a significant advancement in the domain of automated fruit handling.

Robot Navigation in Unknown and Cluttered Workspace with Dynamical System Modulation in Starshaped Roadmap

This paper presents a novel reactive motion planning framework for navigating robots in unknown and cluttered 2D workspace. Typical existing methods are developed by enforcing the robot staying in free regions represented by the locally extracted ellipse or polygon. Instead, we navigate the robot in free space with an alternate starshaped decomposition, which is calculated directly from real-time sensor data. Additionally, a roadmap is constructed incrementally to maintain the connectivity information of the starshaped regions. Compared to the roadmap built upon connected polygons or ellipses in the conventional approaches, the concave starshaped region is better suited to capture the natural distribution of sensor data, so that the perception information can be fully exploited for robot navigation. In this sense, conservative and myopic behaviors are avoided with the proposed approach, and intricate obstacle configurations can be suitably accommodated in unknown and cluttered environments. Then, we design a heuristic exploration algorithm on the roadmap to determine the frontier points of the starshaped regions, from which short-term goals are selected to attract the robot towards the goal configuration. It is noteworthy that, a recovery mechanism is developed on the roadmap that is triggered once a non-extendable short-term goal is reached. This mechanism renders it possible to deal with dead-end situations that can be typically encountered in unknown and cluttered environments. Furthermore, safe and smooth motion within the starshaped regions is generated by employing the Dynamical System Modulation (DSM) approach on the constructed roadmap. Through comprehensive evaluation in both simulations and real-world experiments, the proposed method outperforms the benchmark methods in terms of success rate and traveling time.

Integrated Decision-Making and Control for Urban Autonomous Driving with Traffic Rules Compliance

In urban driving scenarios, autonomous vehicles are expected to conform to traffic rules covering traffic lights, traversable and non-traversable traffic lines, etc. In this article, we propose an optimization-based integrated decision-making and control scheme for urban autonomous driving. Inherently, to ensure the compliance with traffic rules, an innovative design of potential functions (PFs) is presented to characterize various traffic rules that are commonly encountered in urban driving scenarios, and these PFs are further incorporated as part of the model predictive control (MPC) formulation. In this sense, it circumvents the necessity of typical hand-crafted rule design, and high-level decision-making is attained implicitly along with control as an integrated architecture, facilitating flexible maneuvers with safety guarantees. As demonstrated from a series of simulations in CARLA, it is noteworthy that the proposed framework admits real-time performance and high generalizability.