Humans possess a remarkable ability to react to unpredictable perturbations through immediate mechanical responses, which harness the visco-elastic properties of muscles to maintain balance. Inspired by this behavior, we propose a novel design of a robotic leg utilizing fibre jamming. The research highlights the potential of these structures for enhancing legged locomotion and adaptability in unpredictable environments.

Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 h) were conducted based on the DARPA Subterranean Challenge. Missions involved deploying a heterogeneous team of robots to locate and identify artefacts such as climbing rope, drills and a mannequin representing a human survivor. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human interventions included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items ( \(+\) 10.52%), traversed more distance ( \(+\) 12.71%), covered more unique ground ( \(+\) 10.56%), and longer time between safety-related events (34%). In routine conditions, both condition scores were comparable for artefacts, distance and coverage. Human-Robot Teams were faster at finding the first artefact but slower to respond to information from the robot team. Overall, operators contribute to mission-based outcomes, help to overcome environmental situations that can impede progress, and can assist robots to recover faster from difficult events.

This article surveys recent progress and discusses future opportunities for simultaneous localization and mapping (SLAM) in extreme underground environments. SLAM in subterranean environments, from tunnels, caves, and man-made underground structures on Earth, to lava tubes on Mars, is a key enabler for a range of applications, such as planetary exploration, search and rescue, disaster response, and automated mining, among others. SLAM in underground environments has recently received substantial attention, thanks to theDARPA Subterranean (SubT) Challenge, a global robotics competition aimed at assessing and pushing the state of the art in autonomous robotic exploration and mapping in complex underground environments. This article reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT competition. In particular, the article has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on light detection and ranging (LIDAR)-centric SLAM solutions (the go-to approach for virtually all teams in the competition), heterogeneous multirobot operation (including both aerial and ground robots), and real-world underground operation (from the presence of obscurants to the need to handle tight computational constraints). We do not shy away from discussing the “dirty details” behind the different SubT SLAM systems, which are often omitted from technical papers. Second, we discuss the maturity of the field by highlighting what is possible with the current SLAM systems and what we believe is within reach with some good systems engineering. Third, we outline what we believe are fundamental open problems, which are likely to require further research to break through. Finally, we provide a list of open-source SLAM implementations and datasets that have been produced during the SubT challenge and related efforts and constitute a useful resource for researchers and practitioners.

Humans possess a remarkable ability to react to unpredictable perturbations through immediate mechanical responses, which harness the visco-elastic properties of muscles to maintain balance. Inspired by this behavior, we propose a novel design of a robotic leg utilizing fibre jammed structures as passive compliant mechanisms to achieve variable joint stiffness and damping. We developed multimaterial fibre jammed tendons with tunable mechanical properties, which can be 3-D printed in one-go without need for assembly. Through extensive numerical simulations and experimentation, we demonstrate the usefulness of these tendons for shock absorbance and maintaining joint stability. We investigate how they could be used effectively in a multijoint robotic leg by evaluating the relative contribution of each tendon to the overall stiffness of the leg. Further, we showcase the potential of these jammed structures for legged locomotion, highlighting how morphological properties of the tendons can be used to enhance stability in robotic legs.

Navigating natural environments with deformable terrain is a difficult challenge in robotics. Understanding the interaction dynamics between robots and such terrain is an important first step in enabling them to explore these environments. Terramechanics models are largely developed and tested on wheeled and tracked platforms, but with the advent of readily available lightweight legged robots, developing an understanding of how robot feet interact with the terrain becomes increasingly important. Works on estimating terramechanical properties of deformable sands and soils use an underlying assumption that translation of the robot foot along the surface of the terrain is due to internal shear deformation of the soil. We show that for lightweight legged robots, this is not the case. Shear forces acting on the foot of a robot during a stride are not accurately predicted by the widely-used Janosi-Hanamoto formula. We propose a new model in which two forces acting on the foot dominate the foot-terrain interaction - gross sliding friction and bulldozing resistance - and propose a model of how these forces act on the foot. We test this model on multiple soil types with different foot materials. Experimental data, collected on a testbench equipped with actuators and sensors identical to those deployed on a robot in the field, is used to validate our proposed model.

Multi-legged robots are effective at traversing rough terrain. However, terrains that include collapsible footholds (i.e. regions that can collapse when stepped on) remain a significant challenge, especially since such situations can be extremely difficult to anticipate using only exteroceptive sensing. State-of-the-art methods typically use various stabilisation techniques to regain balance and counter changing footholds. However, these methods are likely to fail if safe footholds are sparse and spread out or if the robot does not respond quickly enough after a foothold collapse. This paper presents a novel method for multi-legged robots to probe and test the terrain for collapses using its legs while walking. The proposed method improves on existing terrain probing approaches, and integrates the probing action into a walking cycle. A follow-the-leader strategy with a suitable gait and stance is presented and implemented on a hexapod robot. The proposed method is experimentally validated, demonstrating the robot can safely traverse terrain containing collapsible footholds.

In this paper we present the design and control strategies of a novel quadruped climbing robot (named Magneto) with three degrees of freedom (3-DOF) actuated limbs and a 3-DOF compliant magnetic foot. By exploiting its high degrees of freedom, Magneto is able to deform its body shape to squeeze through gaps of 23cm, which is smaller than standard human entry portholes of industrial confined spaces. Its compact foot design of footprint 4cm allows Magneto to walk on narrow beams of thickness less than 5cm, even at varying separation. The inherent high dimensional system design enables the body to be positioned in a wide range of orientations and seamlessly switch a limb function from locomotion to manipulation mode mid-climb. This capability enables access to confined space openings and occluded pockets and navigation through complex 3-D structures previously not demonstrated on legged climbing robots.

In automatic bioacoustic monitoring it is important to do continuous observations to capture rare events, but storage and communication overheads typically prevent continuous real-time monitoring. To overcome this limitation, this paper presents a low complexity local processing method for acoustic signals targeting resource constrained nodes and preprocessing and segmentation techniques in line with the proposed local processing technique for effective and continuous identification of bird calls. This paper also focuses on designing of overall automatic bioacoustic monitoring system including feature extraction and classification. The proposed system with Two-windows method shows maximum accuracy of 93.85% when trained and tested using SVM classifier with 214 real world recordings containing calls of 5 bird species. Having local processing at node level has shown 43% reduction of space requirement at node level and 24% reduction of processing time.

This work introduces a novel hybrid control architecture for a hexapod platform (Weaver), making it capable of autonomously navigating in uneven terrain. The main contribution stems from the use of vision-based exteroceptive terrain perception to adapt the robot's locomotion parameters. Avoiding computationally expensive path planning for the individual foot tips, the adaptation controller enables the robot to reactively adapt to the surface structure it is moving on. The virtual stiffness, which mainly characterizes the behavior of the legs' impedance controller is adapted according to visually perceived terrain properties. To further improve locomotion, the frequency and height of the robot's stride are similarly adapted. Furthermore, novel methods for terrain characterization and a keyframe based visual-inertial odometry algorithm are combined to generate a spatial map of terrain characteristics. Localization via odometry also allows for autonomous missions on variable terrain by incorporating global navigation and terrain adaptation into one control architecture. Autonomous runs on a testbed with variable terrain types illustrate that adaptive stride and impedance behavior decreases the cost of transport by 30 % compared to a non-adaptive approach and simultaneously increases body stability (up to 88 % on even terrain and by 54 % on uneven terrain). Weaver is able to freely explore outdoor environments as it is completely free of external tethers, as shown in the experiments.

To address the goal of locomotion in very complex and difficult terrains, the authors are developing a new class of Ultralight Legged Robots. This paper presents the Multilegged Autonomous eXplorer (MAX), an ultralight, six-legged robot for traversal and exploration of challenging indoor and outdoor environments. The design of MAX emphasizes a low mass/size ratio, high locomotion efficiency, and high payload capability compared to total system mass. MAX is 2.25 m tall at full height and has a mass of approximately 60 kg, which makes it 5 to 20 times lighter than robots of comparable size. MAX is a research vehicle to explore modelling and control of Ultralight Legged Robots subject to flexing, oscillations and swaying; algorithms for gait planning and motion planning under uncertainty; and navigation planning for traversal of complex 3D terrains. This paper presents the design of MAX, provides an overview of the control system developed, summarizes results from indoor and outdoor tests, discusses system performance and outlines the challenges to be addressed next.

Evolutionary algorithms have previously shown promise in generating controllers for legged robots. Multiple evaluations across many evolutionary generations are typically required - simulators are frequently used to accommodate this. However, performance degradation is frequently observed when transferring controllers from simulation to reality due to inconsistencies between the two. In this paper we demonstrate a testbed that permits repeated, direct evolution of hexapod controllers as a closed-loop system. The testbed uses a two-stage evolutionary process. In stage 1, a multi-objective evolutionary algorithm spreads a population of controllers across a space of desirable criteria. The second stage allows for specific criteria to be selected for on a per-mission basis, with promising initial controller parameters taken from the first stage. As the optimisation occurs directly on the robot, performance is guaranteed. Furthermore, controllers can be made specific to irregularities in e.g., motor wear, and robot mass distribution, creating controllers that are sensitive to the hardware state of the individual robot.

Legged robots offer a more versatile solution to traversing outdoor uneven terrain compared to their wheeled and tracked counterparts. They also provide a unique opportunity to perceive the terrain-robot interactions by listening to the sounds generated during locomotion. Legged robots such as hexapod robots produce rich acoustic information for each gait cycle which includes the foot fall sounds and feet pushing on the terrain (support phase), as well as the sounds produced when the feet travel through the air (stride phase). Interpreting this information to perceive the terrain it is traversing makes available another valuable sensing modality which can feed in to higher level systems to facilitate robust and efficient navigation through unknown terrain.We present an online realtime terrain classification system for legged robots that utilise features from the acoustic signals produced during locomotion. A 32-dimensional feature vector extracted from acoustic data recorded using an on-board microphone was fed in to a multiclass Support Vector Machine (SVM). The SVM was trained on 7 different terrain types and the results of the experimental evaluations are presented. The system was implemented using the Robotic Operating System (ROS) for real-time terrain classification. A classification time-resolution of 1 s was achieved by capturing acoustic signals of two steps, and the results show a true positive rate (sensitivity) of up to 92.9%. We also present a noise subtraction technique which removes servo noise and improves the sensitivity up to 95.1%.

Legged robots such as hexapods have the potential to traverse unstructured terrain. This paper introduces a novel hexapod robot (Weaver) using a hierarchical controller, with the ability to efficiently traverse uneven and inclined terrain. The robot has five joints per leg and 30 degrees of freedom overall. The two redundant joints improve the locomotion of the robot by controlling the body pose and the leg orientation with respect to the ground. The impedance controller in Cartesian space reacts to unstructured terrain and thus achieves self-stabilizing behavior without prior profiling of the terrain through exteroceptive sensing. Instead of adding force sensors, the force at the foot tip is calculated by processing the current signals of the actuators. This work experimentally evaluates Weaver with the proposed controller and demonstrates that it can effectively traverse challenging terrains and high gradient slopes, reduce angular movements of the body by more than 55% and reduce the cost of transport (up to 50% on uneven terrain and by 85% on a slope with 20 °). The controller also enables Weaver to walk up inclines of up to 30 °, and remain statically stable on inclines up to 50 °. Furthermore, we present a new metric for legged robot stability performance along with a method for proprioceptive terrain characterization.

Legged robots offer the potential of locomotion across various types of terrains. Different terrains require different gait patterns to enable greater traversal efficiency. Consequently, as a legged robot transitions from one type of terrain to another, the gait pattern should be adapted so as to maximise traction and energy efficiency. This paper explores the use of power consumption as estimated by the robot in real-time for guiding this gait transition in the case of statically-stable locomotion. While moving, the robot autonomously assesses its power consumption, relates it to the traction, and switches between gaits so as to maximise efficiency. In this way, the robot only needs proprioceptive sensors and consequently does not require velocity estimation, ground imaging or profiling to maintain efficient locomotion across different terrains. The approach has been tested on a hexapod robot traversing a variety of terrain types and stiffness, including concrete, grass, mulch and leaf litter. The experimental results show that gait switching on energetics alone enables traction maintenance and efficient locomotion across different terrains. We also present comparisons between the power consumption metric used in this work and cost of transport which is used in the literature for characterising energetics for legged locomotion.

Long-term outdoor localisation with battery-powered devices remains an unsolved challenge, mainly due to the high energy consumption of GPS modules. The use of inertial sensors and short-range radio can reduce reliance on GPS to prolong the operational lifetime of tracking devices, but they only provide coarse-grained control over GPS activity. In this paper, we introduce our feature-rich lightweight Camazotz platform as an enabler of Multimodal Activity-based Localisation~(MAL), which detects activities of interest by combining multiple sensor streams for fine-grained control of GPS sampling times. Using the case study of long-term flying fox tracking, we characterise the tracking, connectivity, energy, and activity recognition performance of our module under both static and 3-D mobile scenarios. We use Camazotz to collect empirical flying fox data and illustrate the utility of individual and composite sensor modalities in classifying activity. We evaluate MAL for flying foxes through simulations based on retrospective empirical data. The results show that multimodal activity-based localisation reduces the power consumption over periodic GPS and single sensor-triggered GPS by up to 77% and 14% respectively, and provides a richer event type dissociation for fine-grained control of GPS sampling.

Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments, where, typically, there is a need for a higher degree of accuracy. In this article, we focus on robust range estimation , an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation, and evaluation of acoustic (both ultrasound and audible) ranging systems. We distill the limitations of acoustic ranging and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler's effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20m (outdoor) and an average accuracy ≈2cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance.

Fine-grained location information at long range can benefit many applications of embedded sensor networks and robotics. In this paper, we focus on range estimation - an important prerequisite for fine-grained localization - in the ultrasonic domain for both indoor and outdoor environments, and make three contributions. First, we evaluate the characteristics of broadband signals, and provide useful statistics in their design and engineering to achieve a good trade-off between range and accuracy. Second, to overcome the inaccuracy due to correlation sidelobes, we propose a signal detection technique that estimates the envelope of the correlated pulse using a simple least-square approximation approach, and undertake a simulation study to verify its ranging efficiency on linear chirps. Third, leveraging on the insights obtained from our initial study, we present the design and implementation of TWEET: a mote-based ultrasonic broadband ranging system based on linear chirps using the CSIRO Audio nodes, which comprises of a Fleck-3z mote along with audio codecs and a Blackfin DSP. Our evaluation results indicate that the system is precise enough to support source localization applications: a reliable operational range of 20m (outdoor) and an average accuracy of < 2 cm with a 95% confidence interval of 2 cm.