Animal robots in the African wilderness: Lessons learned and outlook for field robotics

Amphibious robotic dog: design, paddling gait planning, and experimental characterization

Mammal-inspired quadruped robots excel in traversing diverse terrestrial terrains but often lack aquatic mobility, limiting their effectiveness in amphibious environments. To address this challenge, an amphibious robotic dog (ARD) was developed, integrating efficient paddling gait in water with trotting capabilities on land. A canine-inspired paddling trajectory was first developed for a two-segment leg, and validated through theoretical modeling and experimental measurements of hydrodynamic forces. A waterproof ARD was then fabricated, with careful consideration of center-of-gravity and center-of-buoyancy relationships to ensure stable aquatic movement. Three distinct paddling gaits were developed and tested to evaluate the ARD's swimming speed and stability: two lateral sequence paddling gaits (LSPG) featuring 25% and 33% power phases (PP), and one trot-like paddling gait (TLPG) featuring a 50% PP. Theoretical modeling and numerical calculations were conducted to analyze the stability of different paddling gaits. Static water experiments measured gait-specific hydrodynamic forces, followed by dynamic swimming tests demonstrating that LSPG delivers superior propulsion and speed, while TLPG offers enhanced stability. The ARD achieved a maximum water speed of 0.16 m s-1(0.54 BL s-1) and a land speed of 0.35 m s-1(1.2 BL s-1). These findings provide theoretical and practical guidance for the development of mammal-inspired amphibious quadruped robots, particularly in structural design and paddling gait planning.

Embodied design for enhanced flipper-based locomotion in complex terrains

Robots are becoming increasingly essential for traversing complex environments such as disaster areas, extraterrestrial terrains, and marine environments. Yet, their potential is often limited by mobility and adaptability constraints. In nature, various animals have evolved finely tuned designs and anatomical features that enable efficient locomotion in diverse environments. Sea turtles, for instance, possess specialized flippers that facilitate both long-distance underwater travel and adept maneuvers across a range of coastal terrains. Building on the principles of embodied intelligence and drawing inspiration from sea turtle hatchings, this paper examines the critical interplay between a robot's physical form and its environmental interactions, focusing on how morphological traits and locomotive behaviors affect terrestrial navigation. We present a bioinspired robotic system and study the impacts of flipper/body morphology and gait patterns on its terrestrial mobility across diverse terrains ranging from sand to rocks. Evaluating key performance metrics such as speed and cost of transport, our experimental results highlight adaptive designs as crucial for multi-terrain robotic mobility to achieve not only speed and efficiency but also the versatility needed to tackle the varied and complex terrains encountered in real-world applications.

Self-rerouting sensor network for electronic skin resilient to severe damage

We propose a network architecture for electronic skin with an extensive sensor array-crucial for enabling robots to perceive their environment and interact effectively with humans. Fault tolerance is essential for electronic skins on robot exteriors. Although self-healing electronic skins targeting minor damages are studied using material-based approaches, substantial damages such as severe cuts necessitate re-establishing communication pathways, traditionally performed with high-functionality microprocessor sensor nodes. However, this method is costly, increases latency, and boosts power usage, limiting scalability for large, nuanced sensation-mimicking sensor arrays. Our proposed system features sensor nodes consisting of only a few dozen logic circuits, enabling them to autonomously reconstruct reading pathways. These nodes can adapt to topological changes within the sensor network caused by disconnections and reconnections. Testing confirms rapid reading times of only a few microseconds and power consumption of 1.88 μW/node at a 1 kHz sampling rate. This advancement significantly boosts robots' collaborative potential with humans.

Reinforcement learning-based framework for whale rendezvous via autonomous sensing robots

Rendezvous with sperm whales for biological observations is made challenging by their prolonged dive patterns. Here, we propose an algorithmic framework that codevelops multiagent reinforcement learning-based routing (autonomy module) and synthetic aperture radar-based very high frequency (VHF) signal-based bearing estimation (sensing module) for maximizing rendezvous opportunities of autonomous robots with sperm whales. The sensing module is compatible with low-energy VHF tags commonly used for tracking wildlife. The autonomy module leverages in situ noisy bearing measurements of whale vocalizations, VHF tags, and whale dive behaviors to enable time-critical rendezvous of a robot team with multiple whales in simulation. We conducted experiments at sea in the native habitat of sperm whales using an "engineered whale"-a speedboat equipped with a VHF-emitting tag, emulating five distinct whale tracks, with different whale motions. The sensing module shows a median bearing error of 10.55° to the tag. Using bearing measurements to the engineered whale from an acoustic sensor and our sensing module, our autonomy module gives an aggregate rendezvous success rate of 81.31% for a 500-meter rendezvous distance using three robots in postprocessing. A second class of fielded experiments that used acoustic-only bearing measurements to three untagged sperm whales showed an aggregate rendezvous success rate of 68.68% for a 1000-meter rendezvous distance using two robots in postprocessing. We further validated these algorithms with several ablation studies using a sperm whale visual encounter dataset collected by marine biologists.

Toward the Intelligent, Safe Exploration of a Biomimetic Underwater Robot: Modeling, Planning, and Control

Safe, underwater exploration in the ocean is a challenging task due to the complex environment, which often contains areas with dense coral reefs, uneven terrain, or many obstacles. To address this issue, an intelligent underwater exploration framework of a biomimetic robot is proposed in this paper, including an obstacle avoidance model, motion planner, and yaw controller. Firstly, with the aid of the onboard distance sensors in robotic fish, the obstacle detection model is established. On this basis, two types of obstacles, i.e., rectangular and circular, are considered, followed by the obstacle collision model's construction. Secondly, a deep reinforcement learning method is adopted to plan the plane motion, and the performances of different training setups are investigated. Thirdly, a backstepping method is applied to derive the yaw control law, in which a sigmoid function-based transition method is employed to smooth the planning output. Finally, a series of simulations are carried out to verify the effectiveness of the proposed method. The obtained results indicate that the biomimetic robot can not only achieve intelligent motion planning but also accomplish yaw control with obstacle avoidance, offering a valuable solution for underwater operation in the ocean.