--We introduce CoinFT, a capacitive 6-axis force / torque (F / T) sensor that is compact, light, low-cost, and robust with an average mean-squared error of 0.11 N for force and 0.84 mNm for moment when the input ranges from 0 10 N and 0 4 N in normal and shear directions, respectively. CoinFT is a stack of two rigid PCBs with comb-shaped electrodes connected by an array of silicone rubber pillars. The microcontroller interrogates the electrodes in different subsets in order to enhance sensitivity for measuring 6-axis F / T . The combination of desirable features of CoinFT enables various contact-rich robot interactions at a scale, across different embodiment domains including drones, robot end-effectors, and wearable haptic devices. We demonstrate the utility of CoinFT on drones by performing an attitude-based force control to perform tasks that require careful contact force modulation. RECISE force and torque measurement is vital for robots to perform contact-rich tasks safely and effectively. Tasks such as table wiping [1], assembly [2], or palpating soft tissue [3] require the application of force and torque within a specific range--sufficient to complete the task but not so excessive as to cause damage or waste energy. Depending on the application and interaction type, robots performing contact-rich tasks come in various forms, including robotic arms [4], grippers [5], drones [6], and wearable devices [7]. Therefore, equipping these diverse robotic platforms with sensors that can accurately measure force and torque is essential. Extensive research has been dedicated to developing 6-axis force / torque (F / T) sensors using various transduction methods [8]. Commercially available sensors also exist, such as the Gamma (A TI Industries), and 6-axis F / T sensors from MinebeaMitsumi or ReSense.

Abstract-- As natural access points to the subsurface, lava tubes and other caves have become premier targets of planetary missions for astrobiological analyses. Few existing robotic paradigms, however, are able to explore such challenging environments. ReachBot is a robot that enables navigation in planetary caves by using extendable and retractable limbs to locomote. This paper outlines the potential science return and mission operations for a notional mission that deploys ReachBot to a martian lava tube. In this work, the motivating science goals and science traceability matrix are provided to guide payload selection.

ReachBot is a robot concept for the planetary exploration of caves and lava tubes, which are often inaccessible with traditional robot locomotion methods. It uses extendable booms as appendages, with grippers mounted at the end, to grasp irregular rock surfaces and traverse these difficult terrains. We have built a partial ReachBot prototype consisting of a single boom and gripper, mounted on a tripod. We present the details on the design and field test of this partial ReachBot prototype in a lava tube in the Mojave Desert. The technical requirements of the field testing, implementation details, and grasp performance results are discussed. The planning and preparation of the field test and lessons learned are also given.

We present the design and operation of tadpole-mimetic robots prepared for a study of the parenting behaviors of poison frogs, which pair bond and raise their offspring. The mission of these robots is to convince poison frog parents that they are tadpoles, which need to be fed. Tadpoles indicate this need, at least in part, by wriggling with a characteristic frequency and amplitude. While the study is in progress, preliminary indications are that the TadBots have passed their test, at least for father frogs. We discuss the design and operational requirements for producing convincing TadBots and provide some details of the study design and plans for future work.

Motion planning for a multi-limbed climbing robot must consider the robot's posture, joint torques, and how it uses contact forces to interact with its environment. This paper focuses on motion planning for a robot that uses nontraditional locomotion to explore unpredictable environments such as martian caves. Our robotic concept, ReachBot, uses extendable and retractable booms as limbs to achieve a large reachable workspace while climbing. Each extendable boom is capped by a microspine gripper designed for grasping rocky surfaces. ReachBot leverages its large workspace to navigate around obstacles, over crevasses, and through challenging terrain. Our planning approach must be versatile to accommodate variable terrain features and robust to mitigate risks from the stochastic nature of grasping with spines. In this paper, we introduce a graph traversal algorithm to select a discrete sequence of grasps based on available terrain features suitable for grasping. This discrete plan is complemented by a decoupled motion planner that considers the alternating phases of body movement and end-effector movement, using a combination of sampling-based planning and sequential convex programming to optimize individual phases. We use our motion planner to plan a trajectory across a simulated 2D cave environment with at least 95% probability of success and demonstrate improved robustness over a baseline trajectory. Finally, we verify our motion planning algorithm through experimentation on a 2D planar prototype.