Mathematical Modelling and Control System Development of a Remote Controlled, IMU Stabilised Hexapod Robot

Walking robots are useful in search and rescue applications due to their ability to navigate uneven and complex terrain. A hexapod robot has been developed by the Robotics and Agents Research Lab at UCT, however multiple inadequacies have become evident. This work aims to produce a mathematical model of the hexapod and using this model, implement an effective control algorithm to achieve a smooth walking motion and overcome the original flaws. The mathematical model was integrated with the mechanical structure of the hexapod and controlled by a micro-controller. This micro-controller allows for a rapid start-up and low power consumption when compared to previous iterations of the hexapod. Using a path generation algorithm sets of foot positions and velocities are generated. Generating these points in real time allows for walking in any direction without any pre-defined foot positions. To enable attitude control of the hexapod body, an inertial measurement unit was added to the hexapod. By using a PID controller the IMU pitch and roll data was used to control a height offset of each foot of the hexapod, allowing for stabilisation of the hexapod body. An improved wireless remote control was developed to facilitate communication with a host computer. The remote system has a graphical user interface allowing for walking control and status information feedback, such as error information and current battery voltage. Walking tests have shown that the hexapod walks successfully with a smooth tripod gait using the path generation algorithm. Stabilisation tests have shown that the hexapod is capable of stabilising itself after a disturbance to its pitch and/or roll in ±2.5 seconds with a steady state error of ±0.001 radians. This proves that the hexapod robot can be controlled wirelessly while walking in any direction with a stabilised body. This is beneficial in search and rescue as the hexapod has a high degree of manoeuvrability to access areas too dangerous for rescuers to access. With cameras mounted on the stabilised body, it can be used to locate survivors in a disaster area and assist rescuers in recovering them with speed

Computing Substrates and Life

Alive matter distinguishes itself from inanimate matter by actively maintaining a high degree of inhomogenous organisation. Information processing is quintessential to this capability. The present paper inquires into the degree to which the information processing aspect of living systems can be abstracted from the physical medium of its implementation. Information processing serving to sustain the complex organisation of a living system faces both the harsh reality of real-time requirements and severe constraints on energy and material that can be expended on the task. This issue is of interest for the potential scope of Artificial Life and its interaction with Synthetic Biology. It is pertinent also for information technology. With regard to the latter aspect, the use of a living cell in a robot control architecture is considered

Evolving Gaits for Damage Control in a Hexapod Robot

Autonomous robots are increasingly used in remote and hazardous environments, where damage to sensory-actuator systems cannot be easily repaired. Such robots must therefore have controllers that continue to function effectively given unexpected malfunctions and damage to robot morphology. This study applies the Intelligent Trial and Error (IT&E) algorithm to adapt hexapod robot control to various leg failures and demonstrates the IT&E map-size parameter as a critical parameter in influencing IT&E adaptive task performance. We evaluate robot adaptation for multiple leg failures on two different map-sizes in simulation and validate evolved controllers on a physical hexapod robot. Results demonstrate a trade-off between adapted gait speed and adaptation duration, dependent on adaptation task complexity (leg damage incurred), where map-size is crucial for generating behavioural diversity required for adaptation

A Bio-Inspired Model for Visual Collision Avoidance on a Hexapod Walking Robot

Meyer HG, Bertrand O, Paskarbeit J, Lindemann JP, Schneider A, Egelhaaf M. A Bio-Inspired Model for Visual Collision Avoidance on a Hexapod Walking Robot. In: Lepora FN, Mura A, Mangan M, Verschure FMJP, Desmulliez M, Prescott JT, eds. Biomimetic and Biohybrid Systems: 5th International Conference, Living Machines 2016, Edinburgh, UK, July 19-22, 2016. Proceedings. Cham: Springer International Publishing; 2016: 167-178.While navigating their environments it is essential for autonomous mobile robots to actively avoid collisions with obstacles. Flying insects perform this behavioural task with ease relying mainly on information the visual system provides. Here we implement a bioinspired collision avoidance algorithm based on the extraction of nearness information from visual motion on the hexapod walking robot platform HECTOR. The algorithm allows HECTOR to navigate cluttered environments while actively avoiding obstacles

Harmonic Versus Chaos Controlled Oscillators in Hexapedal Locomotion

The behavioural diversity of chaotic oscillator can be controlled into periodic dynamics and used to model locomotion using central pattern generators. This paper shows how controlled chaotic oscillators may improve the adaptation of the robot locomotion behaviour to terrain uncertainties when compared to nonlinear harmonic oscillators. This is quantitatively assesses by the stability, changes of direction and steadiness of the robotic movements. Our results show that the controlled Wu oscillator promotes the emergence of adaptive locomotion when deterministic sensory feedback is used. They also suggest that the chaotic nature of chaos controlled oscillators increases the expressiveness of pattern generators to explore new locomotion gaits

Experimental Validation of a Sliding Mode Control for a Stewart Platform Used in Aerospace Inspection Applications

The authors introduce a new controller, aimed at industrial domains, that improves the performance and accuracy of positioning systems based on Stewart platforms. More specifically, this paper presents, and validates experimentally, a sliding mode control for precisely positioning a Stewart platform used as a mobile platform in non-destructive inspection (NDI) applications. The NDI application involves exploring the specimen surface of aeronautical coupons at different heights. In order to avoid defocusing and blurred images, the platform must be positioned accurately to keep a uniform distance between the camera and the surface of the specimen. This operation requires the coordinated control of the six electro mechanic actuators (EMAs). The platform trajectory and the EMA lengths can be calculated by means of the forward and inverse kinematics of the Stewart platform. Typically, a proportional integral (PI) control approach is used for this purpose but unfortunately this control scheme is unable to position the platform accurately enough. For this reason, a sliding mode control (SMC) strategy is proposed. The SMC requires: (1) a priori knowledge of the bounds on system uncertainties, and (2) the analysis of the system stability in order to ensure that the strategy executes adequately. The results of this work show a higher performance of the SMC when compared with the PI control strategy: the average absolute error is reduced from 3.45 mm in PI to 0.78 mm in the SMC. Additionally, the duty cycle analysis shows that although PI control demands a smoother actuator response, the power consumption is similar.This research was funded by the Basque Government through the project SMAR3NAK (ELKARTEK KK-2019/00051), by the Ministerio de Economía y Competitividad (RTI2018-094669-B-C31) and by Aernnova and the Diputación Foral de Álava (DFA) through the project CONAVAUTIN 2 (Collaboration Agreement)

Review article: locomotion systems for ground mobile robots in unstructured environments

Abstract. The world market of mobile robotics is expected to increase substantially in the next 20 yr, surpassing the market of industrial robotics in terms of units and sales. Important fields of application are homeland security, surveillance, demining, reconnaissance in dangerous situations, and agriculture. The design of the locomotion systems of mobile robots for unstructured environments is generally complex, particularly when they are required to move on uneven or soft terrains, or to climb obstacles. This paper sets out to analyse the state-of-the-art of locomotion mechanisms for ground mobile robots, focussing on solutions for unstructured environments, in order to help designers to select the optimal solution for specific operating requirements. The three main categories of locomotion systems (wheeled - W, tracked - T and legged - L) and the four hybrid categories that can be derived by combining these main locomotion systems are discussed with reference to maximum speed, obstacle-crossing capability, step/stair climbing capability, slope climbing capability, walking capability on soft terrains, walking capability on uneven terrains, energy efficiency, mechanical complexity, control complexity and technology readiness. The current and future trends of mobile robotics are also outlined

Automation and Robotics: Latest Achievements, Challenges and Prospects

This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections

Fast biped walking with a neuronal controller and physical computation

Biped walking remains a difficult problem and robot models can greatly {facilitate} our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network, which is governed mainly by local sensor signals. This study shows that human-like gaits emerge without {specific} position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (AEA=Anterior Extreme Angle and GC=Ground Contact) which operate at the inter-joint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e.g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motorneurons in our reflexive controller are directly driving the motors of the joints, rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuro-mechanical system and this study emphasises that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using two real robot as well as by a Poincar\'{e} map analysis applied on a model of the robot in order to assess its stability. In addition, this neuronal control structure allows the use of a policy gradient reinforcement learning algorithm to tune the parameters of the neurons in real-time, during walking. This way the robot can reach a record-breaking walking speed of 3.5 leg-lengths per second after only a few minutes of online learning, which is even comparable to the fastest relative speed of human walking