Centipedes exhibit great maneuverability in diverse environments due to their many legs and body-driven control. By leveraging similar morphologies, their robotic counterparts also demonstrate effective terrestrial locomotion. However, the success of these multi-legged robots is largely limited to forward locomotion; steering is substantially less studied, in part due to the challenges in coordinating their many body joints. Furthermore, steering behavior is complex and can include different combinations of desired rotational/translational displacement. In this paper, we explore steering strategies in multi-legged robots based on tools derived from geometric mechanics (GM). We characterize the steering motion in the plane by the rotation angle, the steering radius, and the heading direction angle. We identify an effective turning strategy by superimposing two traveling waves in the lateral body undulation and further explore variations of the "turning wave" to enable a broad spectrum of steering behaviors. By combining an amplitude modulation and a phase modulation, we develop a control strategy for steering behaviors that enables steering with a range of rotation angles (from 0{\deg} to 20{\deg}) and steering radius (from 0.28 to 0.38 body length) while keeping the heading direction angle close to 0. Lastly, we test our control framework on an elongate multi-legged robot model to verify the effectiveness of our proposed strategy. Our work demonstrates the generality of the two-wave template for effective steering of multi-legged elongate robots.

Abstract--In this paper, we propose a novel strategy for a snake robot to move straight up a cylindrical surface. Prior works on pole-climbing for a snake robot mainly utilized a rolling helix gait, and although proven to be efficient, it does not reassemble movements made by a natural snake. We take inspiration from nature and seek to imitate the Arboreal Concertina Locomotion (ACL) from real-life serpents. In order to represent the 3D curves that make up the key motion patterns of ACL, we establish a set of parametric equations that identify periodic functions, which produce a sequence of backbone curves. We then build up the gait equation using the curvature integration method, and finally, we propose a simple motion estimation strategy using virtual chassis and non-slip model assumptions.

Limbless robots have the potential to maneuver through cluttered environments that conventional robots cannot traverse. As illustrated in their biological counterparts such as snakes and nematodes, limbless locomotors can benefit from interactions with obstacles, yet such obstacle-aided locomotion (OAL) requires properly coordinated high-level self-deformation patterns (gait templates) as well as low-level body adaptation to environments. Most prior work on OAL utilized stereotyped traveling-wave gait templates and relied on local body deformations (e.g., passive body mechanics or decentralized controller parameter adaptation based on force feedback) for obstacle navigation, while gait template design for OAL remains less studied. In this paper, we explore novel gait templates for OAL based on tools derived from geometric mechanics (GM), which thus far has been limited to homogeneous environments. Here, we expand the scope of GM to obstacle-rich environments. Specifically, we establish a model that maps the presence of an obstacle to directional constraints in optimization. In doing so, we identify novel gait templates suitable for sparsely and densely distributed obstacle-rich environments respectively. Open-loop robophysical experiments verify the effectiveness of our identified OAL gaits in obstacle-rich environments. We posit that when such OAL gait templates are augmented with appropriate sensing and feedback controls, limbless locomotors will gain robust function in obstacle rich environments.

Multi-Agent Path Finding (MAPF) finds conflict-free paths for multiple agents from their respective start to goal locations. MAPF is challenging as the joint configuration space grows exponentially with respect to the number of agents. Among MAPF planners, search-based methods, such as CBS and M*, effectively bypass the curse of dimensionality by employing a dynamically-coupled strategy: agents are planned in a fully decoupled manner at first, where potential conflicts between agents are ignored; and then agents either follow their individual plans or are coupled together for planning to resolve the conflicts between them. In general, the number of conflicts to be resolved decides the run time of these planners and most of the existing work focuses on how to efficiently resolve these conflicts. In this work, we take a different view and aim to reduce the number of conflicts (and thus improve the overall search efficiency) by improving each agent's individual plan. By leveraging a Visual Transformer, we develop a learning-based single-agent planner, which plans for a single agent while paying attention to both the structure of the map and other agents with whom conflicts may happen. We then develop a novel multi-agent planner called LM* by integrating this learning-based single-agent planner with M*. Our results show that for both "seen" and "unseen" maps, in comparison with M*, LM* has fewer conflicts to be resolved and thus, runs faster and enjoys higher success rates. We empirically show that MAPF solutions computed by LM* are near-optimal. Our code is available at https://github.com/lakshayvirmani/learning-assisted-mstar .

Howie Choset is a robotics professor at Carnegie Mellon University and a serial entrepreneur. With his students, Howie has formed several companies including Medrobotics, for surgical systems, Hebi Robotics, for modular robots, and Bito Robotics for autonomous guided vehicles.   Further, Choset co-lead the formation of the Advanced Robotics for Manufacturing Institute, which is $250m national institute advancing both technology development and education for robotics in manufacturing. He is aksi a founding Editor of the journal ‘Science Robotics.   In this conversation, Howie and Aaron discuss the series of startups he has founded, how to delegate & develop teams, and the patience required to see technological innovation turn from idea to reality.     Howie Choset’s Challenge; Have good students actively teach students that are further behind. Go into fields that seem too difficult. Find applications for snake robots.   Connect with  Howie Choset   If you liked this interview, check out where we discuss raising billions of dollars to develop self-driving cars.   Text Me What You Think of This Episode 412-278-7680 Underwritten by Piper Creative Piper Creative makes creating podcasts, vlogs, and videos easy.    How? .   We work with Fortune 500s, medium-sized companies, and entrepreneurs.   Follow Piper as we grow Subscribe on | | |

A few days after a 7.1-magnitude earthquake struck Mexico City last month, Carnegie Mellon University roboticists were contacted to see if their snake robots could help with search-and-rescue efforts. Mexican rescuers were still trying find people in the rubble of collapsed buildings, and even though several days had passed, they thought it'd be worth trying to bring in the snakebots. Within 24 hours, a team of CMU roboticists had packed their gear and headed out to the disaster site. We spoke with Matt Travers, who was on the ground in Mexico City operating the robots, along with Howie Choset, who heads CMU's Biorobotics Lab where the snake robots are developed, about their experience with using robots in a real disaster and how, although no survivors were found during the rescue missions they assisted with, they learned an enormous amount being on-site. IEEE Spectrum: Were you and your robots ready for a real disaster? Howie Choset: Since the beginning of my adventure into snake robots, I've been interested in search and rescue.