

Successful execution of research and development under this topic will produce a quadruped robot and control architecture that enables faster-than-creep gait in deep muddy environments.
Adaptive off-road autonomous locomotion at speed promises disruptive technological overmatch for the future Army. This end state necessitates robust legged locomotion in unforgiving environments, particularly muddy terrain. Authority over legged locomotion in complex environments, particularly quadruped locomotion, has seen rapid acceleration in recent years. Various techniques for navigating uneven, intricate terrain using model predictive control (MPC) and/or reinforcement learning have been presented, energized by the blooming small autonomous robot market and continued interest in planetary exploration [1-4]. Investigators have even tackled the problem of grasses, vines, and entanglements [5]. It is common to describe the terrain that these novel controllers accommodate as “complex”, and authors will often decompose the definition of complex terrain into uneven, dynamic, uncertain, and deformable environments. Yet most of them fall short of claiming effective navigation of truly muddy terrain, and with good reason. Mud and cohesive soils produce widely varying mechanical behaviors under different conditions [6-8]. Attempts to produce legged robotic systems capable of navigating muddy environments typically rely heavily on feet with a large surface area, and using perception to inform a controller about the outcome of a given foot placement [9, 10]. With a clever choice of foot geometry, this design choice largely minimizes the challenge of modeling intrusion, suction, and slipping on even, shallow mud. Future Army applications will require robust control in uneven and deep muddy environments, the ability to overcome suction effects on the gait, and transition to other terrains. Only very recently have research and engineering groups published early efforts to produce a creep gait in a specific “deep mud” environment [11, 12]. As such, the current state of the art for robotic locomotion in mud propels the robot at a speed in the neighborhood of one body length per minute.
Many opportunities exist to use these young results as a springboard to a more comprehensive understanding of legged locomotion in cohesive soils, particularly in multidisciplinary spaces involving geophysicists, roboticists, and data scientists. Hybrid (nonlinear MPC and reinforcement learning) and hierarchical control leveraging perceptive and proprioceptive feedback must be brought to bear on this problem, employing new insights into the physics of these challenging terrains and the usefulness of data-driven control. Moreover, limb and robot modification via embodied intelligence informed design could offer insight into the morphologies and passive mechanics necessary to produce adaptability in such systems.
Candidate proposals under this topic should, by the end of Phase II, design and implement a novel controller on board a quadrupedal robot capable of traversing muddy environments that produce leg intrusions up to 20% of the leg length. The robot should travel along a path whose distance is at minimum 10 body lengths at an average speed of 8 body lengths per minute.
This topic is accepting Phase I proposals for a cost up to $250,000 for a 6-month period of performance.
Successful applicants should be able to produce a critical mass of preliminary simulation date to demonstrate a thorough and quantitative understanding of structure-soil and fluid-structure-soil interactions associated with gait events in deep mud. Phase I should culminate in a proposed physical robot design for Phase II as well as an in-silico demonstration of a proprioception-only controller rejecting unforeseen perturbations to an 8-body length per minute gait in 20% deep mud. Quantify the efficacy of the novel control architecture against two more traditional baselines.
Successful applicants should provide a physical demonstration of a custom autonomous quadruped robot traversing a minimum 10 body length course with up to 20% leg intrusion into deep mud at an average speed of 8 body lengths per minute. Demonstrate the superiority of the novel control and physical design of the robot against one traditional baseline.
For more information, and to submit your full proposal package, visit the DSIP Portal.
View the SBIR Component Instructions. View the STTR Component Instructions.
SBIR|STTR Help Desk: usarmy.sbirsttr@army.mil
References:
KEYWORDS: Autonomy; Legged Robotics; Human-Machine Integration; Soldier Support; Mechanics; Geophysics; Soil-Structure Interaction; Hybrid Dynamical Systems; Reinforcement Learning; Model Predictive Control; Embodied Intelligence
Successful execution of research and development under this topic will produce a quadruped robot and control architecture that enables faster-than-creep gait in deep muddy environments.
Adaptive off-road autonomous locomotion at speed promises disruptive technological overmatch for the future Army. This end state necessitates robust legged locomotion in unforgiving environments, particularly muddy terrain. Authority over legged locomotion in complex environments, particularly quadruped locomotion, has seen rapid acceleration in recent years. Various techniques for navigating uneven, intricate terrain using model predictive control (MPC) and/or reinforcement learning have been presented, energized by the blooming small autonomous robot market and continued interest in planetary exploration [1-4]. Investigators have even tackled the problem of grasses, vines, and entanglements [5]. It is common to describe the terrain that these novel controllers accommodate as “complex”, and authors will often decompose the definition of complex terrain into uneven, dynamic, uncertain, and deformable environments. Yet most of them fall short of claiming effective navigation of truly muddy terrain, and with good reason. Mud and cohesive soils produce widely varying mechanical behaviors under different conditions [6-8]. Attempts to produce legged robotic systems capable of navigating muddy environments typically rely heavily on feet with a large surface area, and using perception to inform a controller about the outcome of a given foot placement [9, 10]. With a clever choice of foot geometry, this design choice largely minimizes the challenge of modeling intrusion, suction, and slipping on even, shallow mud. Future Army applications will require robust control in uneven and deep muddy environments, the ability to overcome suction effects on the gait, and transition to other terrains. Only very recently have research and engineering groups published early efforts to produce a creep gait in a specific “deep mud” environment [11, 12]. As such, the current state of the art for robotic locomotion in mud propels the robot at a speed in the neighborhood of one body length per minute.
Many opportunities exist to use these young results as a springboard to a more comprehensive understanding of legged locomotion in cohesive soils, particularly in multidisciplinary spaces involving geophysicists, roboticists, and data scientists. Hybrid (nonlinear MPC and reinforcement learning) and hierarchical control leveraging perceptive and proprioceptive feedback must be brought to bear on this problem, employing new insights into the physics of these challenging terrains and the usefulness of data-driven control. Moreover, limb and robot modification via embodied intelligence informed design could offer insight into the morphologies and passive mechanics necessary to produce adaptability in such systems.
Candidate proposals under this topic should, by the end of Phase II, design and implement a novel controller on board a quadrupedal robot capable of traversing muddy environments that produce leg intrusions up to 20% of the leg length. The robot should travel along a path whose distance is at minimum 10 body lengths at an average speed of 8 body lengths per minute.
This topic is accepting Phase I proposals for a cost up to $250,000 for a 6-month period of performance.
Successful applicants should be able to produce a critical mass of preliminary simulation date to demonstrate a thorough and quantitative understanding of structure-soil and fluid-structure-soil interactions associated with gait events in deep mud. Phase I should culminate in a proposed physical robot design for Phase II as well as an in-silico demonstration of a proprioception-only controller rejecting unforeseen perturbations to an 8-body length per minute gait in 20% deep mud. Quantify the efficacy of the novel control architecture against two more traditional baselines.
Successful applicants should provide a physical demonstration of a custom autonomous quadruped robot traversing a minimum 10 body length course with up to 20% leg intrusion into deep mud at an average speed of 8 body lengths per minute. Demonstrate the superiority of the novel control and physical design of the robot against one traditional baseline.
For more information, and to submit your full proposal package, visit the DSIP Portal.
View the SBIR Component Instructions. View the STTR Component Instructions.
SBIR|STTR Help Desk: usarmy.sbirsttr@army.mil
References:
KEYWORDS: Autonomy; Legged Robotics; Human-Machine Integration; Soldier Support; Mechanics; Geophysics; Soil-Structure Interaction; Hybrid Dynamical Systems; Reinforcement Learning; Model Predictive Control; Embodied Intelligence