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Taking inspiration from local leg feedback control loops present in animal legs, a force threshold-based position (FTP) controller is presented to aid with legged locomotion over irregular terrain. The algorithm uses pre-planned position trajectories and force feedback to either elevate or depress the foot. The FTP controller isolates the control of each leg to use only localized feedback, which can result in greater responsiveness to the terrain when compared to a centralized controller arbitrating all of the joint positions in a high degree of freedom system. The controller is robust to terrain elevations without using visual sensors, a priori terrain information, inertial sensing or inter-leg communication. Results of the FTP controller applied to a hexapod system in simulation and on an experimental system are shown in this paper. The algorithm also has the potential for expansion to bipeds, quadrupeds and other biologically-inspired forms.

This article presents a 3D odometry algorithm for statically stable walking robots that only uses proprioceptive data delivered by joint angle and joint torque sensors embedded within the legs. The algorithm intrinsically handles each kind of emerging statically stable gait and is independent of predefined gait patterns. Additionally, the algorithm can be equally applied to stiff robots as well as to robots with compliant joints. Based on the proprioceptive information a 6 degrees of freedom (DOF) pose estimate is calculated in three steps. First, point clouds, represented by the foot positions with respect to the body frame at two consecutive time steps, are matched and provide a 6 DOF estimate for the relative body motion. The obtained relative motion estimates are summed up over time giving a 6 DOF pose estimate with respect to the start frame. Second, joint torque measurement based pitch and roll angle estimates are determined. Finally in a third step, these estimates are used to stabilize the orientation angles calculated in the first step by data fusion employing an error state Kalman filter. The algorithm is implemented and tested on the DLR Crawler, an actively compliant six-legged walking robot. For this specific robot, experimental data is provided and the performance is evaluated in flat terrain and on gravel, at different joint stiffness settings and for various emerging gaits. Based on this data, problems associated with the odometry of statically stable walking robots are identified and discussed. Further, some results for crossing slopes and edges in a complete 3D scenario are presented.

Autonomous use of legged robots in unstructured, outdoor settings requires dynamically dexterous behaviors to achieve sufficient speed and agility without overly complex and fragile mechanics and actuation. Among such behaviors is the relatively under-studied pronking (aka. stotting), a dynamic gait in which all legs are used in synchrony, usually resulting in relatively slow speeds but long flight phases and large jumping heights. Instantiations of this gait for robotic systems have been mostly limited to open-loop strategies, suffering from severe pitch instability for underactuated designs due to the lack of active feedback. However, both the kinematic simplicity of this gait and its dynamic nature suggest that the Spring-Loaded Inverted Pendulum model (SLIP) would be a good basis for the implementation of a more robust feedback controller for pronking. In this paper, we describe how template-based control, a controller structure based on the embedding of a simple dynamical "template" within a more complex "anchor" system, can be used to achieve very stable pronking for a planar, underactuated hexapod robot. In this context, high-level control of the gait is regulated through speed and height commands to the SLIP template, while the embedding controller ensures the stability of the remaining degrees of freedom. We use simulation studies to show that unlike existing open-loop alternatives, the resulting control structure provides explicit gait control authority and significant robustness against sensor and actuator noise. © 2010 Springer Science+Business Media, LLC.

Statically stable walking locomotion research has focused mainly on robot design and gait generation. However, there is a need to expand robots’ capabilities so that walking machines can accomplish the kinds of real tasks for which they are eminently suited. Many such tasks demand trajectory tracking, but researchers have traditionally ignored this subject. This article focuses on the tracking of predefined trajectories with hexapod robots walking on natural terrain with forbidden zones. The method presented herein, which relies on gait algorithms defined elsewhere, describes certain localization strategies and control techniques that have been employed to follow trajectories accurately and have been implemented in a real walking hexapod. Several experimental examples are included to assess the proposed algorithms. Funding for this work was provided by the Spanish Ministry of Science and Technology under Grants DPI2001-1595 and DPI2004-05824.

This paper presents a framework for going from specifications to implementations of decentralized control strategies for multi-robot systems. In particular, we show how the use of Embedded Graph Grammars (EGGs) provides a tool for characterizing local interaction and control laws. This paper highlights some key implementation aspects of the EGG formalism, and develops and discusses experimental results for a hexapod-based multi-robot system, as well as a multi-robot system of wheeled robots.

A six-legged robot system demonstrating reactive behaviors of simple organisms—walking between two walls, steering around obstacles, making turns at corners, and making U-turns at pathway dead-ends—is described. The system, named VisionBug, uses no active range sensor but only a stereo pair of cameras for sensing the surroundings. The system assumes the surroundings to be consisting of mostly a ground surface, although the surface could have a varying geometric relationship with the robot due to the jiggling nature of legged motion. By the use of the image-to-image mapping induced to the stereo images by the ground surface, the system is able to avoid explicit 3D reconstruction and the use of optical flow altogether in locating through-ways. Specifically, it regards image features not respecting the ground-induced mapping as obstacles, and express the obstacles in terms of a 2D distribution on the ground. Based on the distribution information, which is further time-delay compensated, a simple fuzzy control mechanism is used to command the legged motion. Experiments show that the system is effective for demonstrating the above-mentioned behaviors in textured environment, at a speed fast enough for many applications.

The “Rutgers Ankle” is a Stewart platform-type haptic interface designed for use in rehabilitation. The system supplies six-DOF resistive forces in response to virtual reality-based exercises running on a host PC. The Stewart platform uses double-acting pneumatic cylinders, linear potentiometers as position sensors, and a six-DOF force sensor. The Rutgers Ankle controller contains an embedded Pentium board, pneumatic solenoid valves, valve controllers, and associated signal conditioning electronics. Communication with the host PC is over a standard RS232 line. The platform movement and output forces are transparently recorded by the host PC in a database. This database can be accessed remotely over the Internet. Thus, the Rutgers Ankle Orthopedic Rehabilitation Interface will allow patients to exercise at home while being monitored remotely by therapists. A prototype was constructed, and proof-of-concept trials were conducted at the University of Medicine and Dentistry of New Jersey. The results indicate that the system works well as a diagnostic tool. The subjective evaluation by patients was very positive. Further medical trials are needed before the system clinical efficacy in rehabilitation can be established.

RHex is an untethered, compliant leg hexapod robot that travels at better than one body length per second over terrain few other robots can negotiate at all. Inspired by biomechanics insights into arthropod locomotion, RHex uses a clock excited alternating tripod gait to walk and run in a highly maneuverable and robust manner. We present empirical data establishing that RHex exhibits a dynamical (“bouncing”) gait—its mass center moves in a manner well approximated by trajectories from a Spring Loaded Inverted Pendulum (SLIP)—characteristic of a large and diverse group of running animals, when its central clock, body mass, and leg stiffnesses are appropriately tuned. The SLIP template can function as a useful control guide in developing more complex autonomous locomotion behaviors such as registration via visual servoing, local exploration via visual odometry, obstacle avoidance, and, eventually, global mapping and localization.

This paper outlines aspects of locomotor control in insects that may serve as the basis for the design of controllers for autonomous hexapod robots. Control of insect walking can be considered hierarchical and modular. The brain determines onset, direction, and speed of walking. Coordination is done locally in the ganglia that control leg movements. Typically, networks of neurons capable of generating alternating contractions of antagonistic muscles (termed central pattern generators, or CPGs) control the stepping movements of individual legs. The legs are coordinated by interactions between the CPGs and sensory feedback from the moving legs. This peripheral feedback provides information about leg load, position, velocity, and acceleration, as well as information about joint angles and foot contact. In addition, both the central pattern generators and the sensory information that feeds them may be modulated or adjusted according to circumstances. Consequently, locomotion in insects is extraordinarily robust and adaptable.

Classical engineering approaches to controlling a hexapod walker typically involve a central control instance that implements an abstract optimal gait pattern and relies on additional optimization criteria to generate reference signals for servocontrollers at all the joints. In contrast, the gait of the slow-walking stick insect apparently emerges from an extremely decentralized architecture with separate step pattern generators for each leg, a strong dependence on sensory feedback, and multiple, in part redundant, primarily local interactions among the step pattern generators. Thus, stepping and step coordination do not reflect an explicit specification based on a global optimization using a representation of the system and its environments instead they emerge from a distributed system and from the complex interaction with the environment. A similarly decentralized control at the level of single leg joints also may explain the control of leg dynamics. Simulations show that negative feedback for control of body height and walking direction combined with positive feedback for generation of propulsion produce a simple, extremely decentralized system that can handle a wide variety of changes in the walking system and its environment. Thus, there is no need for a central controller implementing global optimization. Furthermore, physiological results indicate that the nervous system uses approximate algorithms to achieve the desired behavioral output rather than an explicit, exact solution of the problem. Simulations and implementation of these design principles are being used to test their utility for controlling six-legged walking machines.

The purpose of this paper is twofold. First, we outline important issues in designing real-time controllers for robots with numerous sensors, actuators, and behaviors. We address these issues by implementing a behavior based controller on a sophisticated autonomous robot. Hence, this work provides a point of reference for the scalability, ease of design, and effectiveness of the behavior based control for complex robots. Second, we explore the viability of using cooperation among local controllers to achieve coherent global behavior. Our approach is to decompose a difficult control task for a complex robot into a multitude of simpler control tasks for robotic subsystems. We illustrate and examine the effectiveness of this approach via rough terrain locomotion using an autonomous hexapod robot. Traversing rough terrain is a good task to test the viability of this approach because it requires a considerable amount of leg coordination. We found that implementing a complicated global control task with cooperating local controllers can effectively control complex robots.