In limbed robotics, end-effectors must serve dual functions, such as both feet for locomotion and grippers for grasping, which presents design challenges. This paper introduces a multi-modal end-effector capable of transitioning between flat and line foot configurations while providing grasping capabilities. MAGPIE integrates eight-axis force sensing using proposed mechanisms with Hall effect sensors, enabling both contact and tactile force measurements. We present a computational design framework for our sensing mechanism that accounts for noise and interference, allowing for desired sensitivity and force ranges and generating ideal inverse models. The hardware implementation of MAGPIE is validated through experiments, demonstrating its capability as a foot and verifying the performance of the sensing mechanisms, ideal models, and gated network-based models.

While recent work explores novel tools to make electronics and device design easier and more accessible, these tend to be either highly automated (great for novices, but limiting for more advanced users) or highly manual (suitable for experts, but imposes a higher skill barrier to entry). In this work, we examine a middle ground: user-guided design space exploration to bridge an intuitive-but-ambiguous high-level representation to a fully-specified, fabrication-ready circuit. Our system helps users understand and make design choices by sweeping the design space of alternatives for electronics parts (e.g., choice of microcontroller), marking invalid options, and plotting points to visualize trade-offs (e.g., for power and size). We discuss the overall system and its structure, report on the results of a small but in-depth user study with participants from a wide range of electronics backgrounds, and draw insights on future directions for improving electronics design for everyone.

This work explores a new concept of operations for observation of Earth events by a satellite sensor web that is able to reason about its capabilities and plan observations based on detected or requested events on Earth’s surface. An intelligent agile satellite sensor web is shown to produce more than twice the number of observations of a nadir-looking sensor web.

Monitoring of short-lived and highly-dynamic processes and events such as floods or forest fires has gained an increasing interest in Earth Observation, particularly as global climate change is affecting these processes. The observation of such dynamic events is often limited by the response time of human operation of Earth-Observing satellites or UAVs.To address this bottleneck, this paper presents a Modified Asynchronous Consensus Constraint-Based Bundle Algorithm (MACCBBA) for observation task allocation in Sensor Web mission concepts for Earth Observation. This algorithm allows for the decentralized allocation of observation tasks amongst a network of Satellites and UAVs based on recently measured data processed on board, or on messages received from other sensors or from the ground. This algorithm is also capable of reaching a feasible plan in a dynamic communications network such as the ones present in some Sensor Web mission concepts and allows for complex temporal constraints and dependencies between tasks to model the value of near-simultaneous co-observations by complementary or synergistic sensors.

Spatial formations can give many social cues, such as illustrating a group of people are having a conversation (social affiliation), or that they are trying to move swiftly through a space (functional goal). This work explored how people perceive varied robots formations while navigating through a space and approaching people. Evaluation occurred across four different geometric formations: wedge, v-shape, vertical line, and horizontal line (Fig 3). Two studies were conducted: the first being an exploratory study of three robots navigating through a public space, and the second being a controlled user study of the same robots approaching humans in different formations. Results showed that triangle shapes were generally received more positively than lines, with wedge being the viewed as harmless, polite, welcoming, and encouraging the human to join the robot group, whereas horizontal line was seen as threatening and unwelcoming. From a path planning perspective, v-shape and wedge were also more robust to controller variance. Results from this work show that formation impacts how people perceive robots, and as a result may impact task success. Future researchers can use these results to inform their behavior design for multi-robot groups to increase task success and desired communication effects.

Modern autonomous systems are purposed for many challenging scenarios, where agents will face unexpected events and complicated tasks. The presence of disturbance noise with control command and unknown inputs can negatively impact robot performance. Previous research of joint input and state estimation separately studied the continuous and discrete cases without any prior information. This paper combines the continuous and discrete input cases into a unified theory based on the Expectation-Maximum (EM) algorithm. By introducing prior knowledge of events as the constraint, inequality optimization problems are formulated to determine a gain matrix or dynamic weights to realize an optimal input estimation with lower variance and more accurate decision-making. Finally, statistical results from experiments show that our algorithm owns 81% improvement of the variance than KF and 47% improvement than RKF in continuous space; a remarkable improvement of right decision-making probability of our input estimator in discrete space, identification ability is also analyzed by experiments.

In this paper we present BOEM-SLAM, a backend for visual-inertial SLAM systems capable of creating a globally consistent trajectory and map without retaining the entire history of data. By leveraging the hidden Markov model structure, BOEM-SLAM can summarize historical data into sufficient statistics and then discard it. As a data-efficient algorithm, BOEM-SLAM addresses the growing computational costs and storage requirements of the SLAM backend. To demonstrate the performance of our algorithm we compare BOEM-SLAM to other fundamental approaches on both synthetic data and the EuRoC dataset. For evaluation on the EuRoC dataset, we use the open source okvis frontend and apply the Lie group state space representation and visual outlier removal. Overall, BOEM-SLAM shows a considerably lower computation time with comparable estimation performance. For example, the processing time of BOEM-SLAM is 50 times smaller than the optimization-based method using simulated data and 5 times smaller than the optimization-based method in the EuRoC dataset experiments.

Cooperative localization is fundamental to autonomous multirobot systems, but most algorithms couple interrobot communication with observation, making these algorithms susceptible to failures in both communication and observation steps. To enhance the resilience of multirobot cooperative localization algorithms in a distributed system, we use covariance intersection to formalize a localization algorithm with an explicit communication update and ensure estimation consistency at the same time. We investigate the covariance boundedness criterion of our algorithm with respect to communication and observation graphs, demonstrating provable localization performance under even sparse communications topologies. We substantiate the resilience of our algorithm as well as the boundedness analysis through experiments on simulated and benchmark physical data against varying communications connectivity and failure metrics. Especially when interrobot communication is entirely blocked or partially unavailable, we demonstrate that our method is less affected and maintains desired performance compared to existing cooperative localization algorithms.

Orthogonally assembled double-layered corrugated (OADLC) mechanisms are a class of foldable structures that harness origami-inspired methods to enhance the structural stiffness of resulting devices; these mechanisms have extensive applications due to their lightweight, compact nature as well as their high strength-to-weight ratio. However, the design of these mechanisms remains challenging. Here, we propose an efficient method to rapidly design OADLC mechanisms from desired behavioral specifications, i.e. in-plane stiffness and out-of-plane stiffness. Based on an equivalent plate model, we develop and validate analytical formulas for the behavioral specifications of OADLC mechanisms; the analytical formulas can be described as expressions of design parameters. On the basis of the analytical expressions, we formulate the design of OADLC mechanisms from behavioral specifications into an optimization problem that minimizes the weight with given design constraints. The 2D folding patterns of the optimized OADLC mechanisms can be generated automatically and directly delivered for fabrication. Our rapid design method is demonstrated by developing stiffness-enhanced mechanisms with a desired out-of-plane stiffness for a foldable gripper that enables a blimp to perch steadily under air disturbance and weight limit.

Although Trajectory Optimization (TO) is one of the most powerful motion planning tools, it suffers from expensive computational complexity as a time horizon increases in cluttered environments. It can also fail to converge to a globally optimal solution. In this paper, we present Lazy Trajectory Optimization (LTO) that unifies local short-horizon TO and global Graph-Search Planning (GSP) to generate a long-horizon global optimal trajectory. LTO solves TO with the same constraints as the original long-horizon TO with improved time complexity. We also propose a TO-aware cost function that can balance both solution cost and planning time. Since LTO solves many nearly identical TO in a roadmap, it can provide an informed warm-start for TO to accelerate the planning process. We also present proofs of the computational complexity and optimality of LTO. Finally, we demonstrate LTO’s performance on motion planning problems for a 2 DOF free-flying robot and a 21 DOF legged robot, showing that LTO outperforms existing algorithms in terms of its runtime and reliability.

Monocular depth inference has gained tremendous attention from researchers in recent years and remains as a promising replacement for expensive time-of-flight sensors, but issues with scale acquisition and implementation overhead still plague these systems. To this end, this work presents an unsupervised learning framework that is able to predict at-scale depth maps and egomotion, in addition to camera intrinsics, from a sequence of monocular images via a single network. Our method incorporates both spatial and temporal geometric constraints to resolve depth and pose scale factors, which are enforced within the supervisory reconstruction loss functions at training time. Only unlabeled stereo sequences are required for training the weights of our single-network architecture, which reduces overall implementation overhead as compared to previous methods. Our results demonstrate strong performance when compared to the current state-of-the-art on multiple sequences of the KITTI driving dataset and can provide faster training times with its reduced network complexity.

Origami robots–often called "printable" robots– created using folding processes have gained extensive attention due to their potential for rapid and accessible design and fabrication through simple structures with complex functionalities. However, almost all origami robots require conventional rigid electronics for control, which may hinder the integration and restrict the potential of these origami systems. Here we introduce origami logic gates that can be built through folding. The major enabling technology is a bistable switch that can switch between two different circuits to control the electrical flow. Based on the origami switch, we develop NOT, AND, and OR logic gates (showing functional completeness) and demonstrate these logic gates through sufficiently powering low-current LEDs. These logic gates are fabricated using cut-and-fold manufacturing and offer a potential way of integrating logic functions directly into origami machines without electronics.

Under line-of-sight (LOS) network conditions, multi-input multi-output (MIMO) wireless communications can increase the channel capacity between a team of robots and a multi-antenna array at a stationary base station. This increased capacity can result in greater data throughput, shortening the time necessary to complete channel-limited data aggregation tasks. To take advantage of this higher capacity channel, the robots in the team must be positioned to maximize complex channel orthogonality between each robot and receiver antenna. Using geometrically motivated assumptions, we derive transmitter spacing rules that can be easily be added on to existing path plans to improve backhaul throughput for data offloading from the robot team, with minimal impact on other system objectives. We demonstrate the effectiveness of the approach- both in ideal as well as realistic channels outside the domain of our simplifying assumptions-with numerical examples of robot-coordinated path plans in two example environments, achieving up to 42% improvement in task completion times.

In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates through each step of our MPC's finite time horizon. The RNN model is trained on a dataset that comprises of robot and landmark poses generated from camera images and inertial measurement unit (IMU) readings via a state-of-the-art visualinertial odometry framework. To detect and extract object locations for avoidance, we use a custom-trained convolutional neural network model in conjunction with a feature extractor to retrieve 3D centroid and radii boundaries of nearby obstacles. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms, demonstrating autonomous behaviors that can plan fast and collision-free paths towards a goal point.

This paper describes CoLo - a performance evaluation system for two-dimensional cooperative localization algorithms. The system consists of a physical experiment (CoLo-PE) for data collection and a software analysis tool (CoLo-AT) using real-world datasets to evaluate the performances of users’ cooperative localization algorithms. This paper details the design and operation of the physical experiment (CoLo-PE) and discusses the functionalities and uses of the software analysis tool (CoLo-AT) for algorithm evaluation. Specifically, CoLo allows researchers to conveniently add their cooperative localization algorithms and test them extensively on different real-world datasets with various settings. CoLo is available at https://git.uclalemur.com/billyskc/CoLo.

Mechanical logic is a class of dynamic electromechanical mechanisms which leverages carefully designed mechanical structures to generate programmed control actions from a constant electrical power supply; thus, it can be employed as a control method for fully printable autonomous robots. Composed of a bistable buckled beam driven by conductive super-coiled polymer (CSCP) actuators, this type of electromechanical system features non-trivial relationships between its design parameters and resulting behavioral characteristics. In this paper we present an efficient method to rapidly design mechanical logic structures from desired behavioral specifications. We describe this dynamic system with a simplified, quasi-static model, whose validity is verified by time constant comparison. An analytical formula of the mechanical logic's behavioral characteristics, i.e. its oscillation period, is then derived as a simplified expression of the design parameters. Based on this expression, we formulate the design of mechanical logic from behavioral specifications into an optimization problem that maximizes the robustness to manufacturing tolerances, as demonstrated by an example case study.

Autonomous vehicles promise safer roads, energy savings, and more efficient use of existing infrastructure, among many other benefits. Although the effect of autonomous vehicles has been studied in the limits (near-zero or full penetration), the transition range requires new formulations, mathematical modeling, and control analysis. In this article, we study the ability of small numbers of autonomous vehicles to stabilize a single-lane system of human-driven vehicles. We formalize the problem in terms of linear string stability, derive optimality conditions from frequency-domain analysis, and pose the resulting nonlinear optimization problem. In particular, we introduce two conditions which simultaneously stabilize traffic while imposing a safety constraint on the autonomous vehicle and limiting degradation of performance. With this optimal linear controller in a system with typical human driver behavior, we can numerically determine that only a 6% uniform penetration of autonomously controlled vehicles (i.e. one per string of up to 16 human-driven vehicles) is necessary to stabilize traffic across all traffic conditions.

Reducing the time the radio is on in wireless devices results in lower power consumption, so sending the same data in fewer bytes can greatly extend the lifetime of a network. In this paper, we explore the use of protocol-agnostic packet compression, a technique orthogonal to current explicit compaction techniques. Because it functions as a transparent layer inside a communication stack and makes no assumption about the specific protocols used, it is generic enough to be used on multiple technologies. Compression is performed by identifying patterns in recent packets and replacing those patterns with bit flags in the transmitted packet. We present the results of compressing actual packet traces collected from several commercial networks using this algorithm and discuss the resource trade-offs of the algorithm. Results indicate compression ratios between 40% and 80%, yielding predicted energy savings of 30-70% in a typical time- synchronized network.

Wireless communication between a pair of nodes can suffer from self interference arising from multipath propagation reflecting off obstacles in the environment. In the event of a deep fade, caused by destructive interference, no signal power is seen at the receiver, and so communication fails. Multipath fading can be overcome by shifting the location of one node, or by switching the communication carrier frequency. The effects of such actions can be characterized by the coherence length (L) and coherence bandwidth (B), respectively, given as the amount of shift necessary to transition from a deep fade to a region of average signal strength. Experimental results for a representative 2.4GHz wireless link indicate L = 5.5cm and B can vary from 5MHz at long ranges up to 15MHz for short links. For wireless sensor networks (WSNs), typically operating under the IEEE802.15.4 standard, multipath effects are therefore best handled by a channel hopping scheme in which successive communication attempts are widely spread across available carrier frequencies.

The electronics packages for many robot control systems have very similar requirements, yet are often redesigned for each custom application. To reduce wasted time and effort, the project presented in this paper (the Wireless Autonomous Robot Platform with Inertial Navigation and Guidance, WARP-WING) is intended to create a complete and easily customizable general purpose control system for miniature robotic systems, in particular micro air vehicles. In its default configuration, hardware designs, firmware, and software are all available to deliver an out-of-the-box robot control solution comprising 6 degree-of-freedom inertial sensors, a microprocessor, and wireless communication, along with general purpose input/output pins, serial ports, and control outputs for interfacing to additional sensors and actuators. The entire project is open source and a process is in place to enable modification of any component, allowing for easy adaptation to any need. WARPWING is already in use in a number of labs, with each research group contributing its expertise to enhance the platform and make such modifications available to others as well.

Reducing the number of transmitted bytes in a wireless sensor network reduces the time the radio is on, resulting in a significant increase in battery lifetime. Toward this end we have developed a compression technique that is independent of the protocols used in the network, acts as a transparent layer, and consumes minimal computing resources. Patterns in recent packets are identified and replaced in the transmitted packet by bit flags. This algorithm was tested on packet traces collected from commercial wireless sensor networks for 40-80% compression, yielding comparable energy savings in a time-synchronized network.

The use of variable data rate can reduce network latency and average power consumption, and automatic rate selection is critical for improving scalability and minimizing network overhead. In the IEEE 802.15.4 standard the SNR can be inferred through the radio reported link quality or received signal strength, and an extension to the standard leads to highly dynamic and accurate rate selection. Using data from an experimental study of 44 IEEE 802.15.4 nodes in an industrial mesh network, SNR is extracted to show sufficient margin exists for higher data rate communication. A variable rate signaling scheme with automatic rate selection is proposed to provide links at the standard 250 kb/s as well as 500 kb/s, 1000 kb/s and 2000 kb/s with a minimum of hardware changes. Using the experimental data to generate a model of the real world system, total network energy is compared using legacy and variable rate signaling showing over 40% savings.