Obstacle avoidance for redundant robots using configuration control

A redundant robot control scheme is provided for avoiding obstacles in a workspace during the motion of an end effector along a preselected trajectory by stopping motion of the critical point on the robot closest to the obstacle when the distance between is reduced to a predetermined sphere of influence surrounding the obstacle. Algorithms are provided for conveniently determining the critical point and critical distance

Neural Architectures for Control

The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs

Safety system for non-interventional flexible robotic arm of Orthopaedic Robot (OTOROB) using fuzzy logic

One of the main and recent problem in developing countries like Malaysia is lack of surgeon or specialists, especially in rural areas. Insufficient specialized surgeons in such regions particularly in the niche of orthopedic, causes more fatalities and loss of limbs due to time and distance constrain in attending the patients. A mobile robotic system known as OTOROB (Orthopedic Robot) is designed and developed to aid surgeons to virtually present at such areas for attending patients in order to make life saving decisions. The developed mobile robotic platform is integrated with a flexible robotic arm vision system to be controlled remotely by the remote surgeon to obtain visual inspection on the patients. Fuzzy logic control is implemented in the control system as Artificial intelligence (AI) to provide safety features for the robotic arm articulation. The safety system of the robotic arm consists of Danger Monitoring System (DMS) and Obstacle Avoidance System (OAS). The experiments conducted on DMS shows that the DMS capable of conveying danger level surrounding the robotic arm to the user through GUI with warning indication and obstacle position. While, OAS developed, responded to the mobile and static obstacle around the robotic arm. The robotic arm is capable of avoiding approaching obstacle autonomously via fuzzy control. The smooth control of robotic arm coupled with safety routines improved the overall articulation of the robotic arm. The safety oriented flexible robotic arm system of OTOROB able to deliver reliable and convenient for both remote doctor and patient in real time emergency circumstances

A Hybrid Systems Model Predictive Control Framework for AUV Motion Control

A computationally efficient architecture to control formations of Autonomous Underwater Vehicles (AUVs) is presented and discussed in this article. The proposed control structure enables the articulation of resources optimization with state feedback control while keeping the onboard computational burden very low. These properties are critical for AUVs systems as they operate in contexts of scarce resources and high uncertainty or variability. The hybrid nature of the controller enables different modes of operation, notably, in dealing with unanticipated obstacles. Optimization and feedback control are brought in by a novel Model Control Predictive (MPC) scheme constructed in such a way that time-invariant information is used as much as possible in a priori off-line computation

4-dimensional trajectory generation algorithms for RPAS mission management systems

This paper presents the algorithms enabling real-time 4-Dimensional Flight Trajectory (4DT) functionalities in Next Generation Mission Management Systems (NG-MMS), which are the core element of future Remotely Piloted Aircraft Systems (RPAS) avionics. In particular, the algorithms are employed for multi-objective optimisation of 4DT intents in various operational scenarios spanning from online strategic to tactical and emergency tasks. The adopted formulation of the multi-objective 4DT optimisation problem includes a number of environmental objectives and operational constraints. In particular, this paper describes the algorithm for planning of 4DT based on a multi-objective optimisation approach and the generalised expression of the cost function adopted for penalties associated with specific airspace volumes, accounting for weather, condensation trails and noise models

Fractional Order State Feedback Control for Improved Lateral Stability of Semi-Autonomous Commercial Heavy Vehicles

With the growing development of autonomous and semi-autonomous large commercial heavy vehicles, the lateral stability control of articulated vehicles have caught the attention of researchers recently. Active vehicle front steering (AFS) can enhance the handling performance and stability of articulated vehicles for an emergency highway maneuver scenario. However, with large vehicles such tractor-trailers, the system becomes more complex to control and there is an increased occurrence of instabilities. This research investigates a new control scheme based on fractional calculus as a technique that ensures lateral stability of articulated large heavy vehicles during evasive highway maneuvering scenarios. The control method is first implemented to a passenger vehicle model with 2-axles based on the well-known “bicycle model”. The model is then extended and applied onto larger three-axle commercial heavy vehicles in platooning operations. To validate the proposed new control algorithm, the system is linearized and a fractional order PI state feedback control is developed based on the linearized model. Then using Matlab/Simulink, the developed fractional-order linear controller is implemented onto the non-linear tractor-trailer dynamic model. The tractor-trailer system is modeled based on the conventional integer-order techniques and then a non-integer linear controller is developed to control the system. Overall, results confirm that the proposed controller improves the lateral stability of a tractor-trailer response time by 20% as compared to a professional truck driver during an evasive highway maneuvering scenario. In addition, the effects of variable truck cargo loading and longitudinal speed are evaluated to confirm the robustness of the new control method under a variety of potential operating conditions

Visual servoing sequencing able to avoid obstacles.

International audienceClassical visual servoing approaches tend to constrain all degrees of freedom (DOF) of the robot during the execution of a task. In this article a new approach is proposed. The key idea is to control the robot with a very under-constrained task when it is far from the desired position, and to incrementally constrain the global task by adding further tasks as the robot moves closer to the goal. As long as they are sufficient, the remaining DOF are used to avoid undesirable configurations, such as joint limits. Closer from the goal, when not enough DOF remain available for avoidance, an execution controller selects a task to be temporary removed from the applied tasks. The released DOF can then be used for the joint limits avoidance. A complete solution to implement this general idea is proposed. Experiments that prove the validity of the approach are also provided