avoidance, autonomous robots nowadays normally rely on active sensors (e.g. laser range ﬁnders [ 13 ]) or extensive computations (e.g. Lucas-Kanade optic ﬂow computation [ 10 ]), insects are able to do so with minimal energetic and computational expenditure by relying mainly on visual information. We imple- mented a bio-inspiredmodel of collisionavoidance in simulations of the hexa- pod walkingrobot HECTOR solely based on the processing of optic ﬂow by correlation-type elementary motion detectors (EMDs). EMDs have previously been accounted for playing a key role in the processing of visual motion infor- mation in insects [ 3 ]. As could be shown, although the responses of EMDs to visual motion are entangled with the textural properties of the environment [ 5 ], the relative nearness information obtained from optic ﬂow estimation via EMDs is suﬃcient to direct HECTOR to a goal location in cluttered environments with- out colliding with obstacles. This holds true either for artificially generated envi- ronments as well as for a reconstructed natural environment, which substantially diﬀer in their textural pattern properties. Moreover, by employing behavioural strategies such as (a) an active-gaze strategy and (b) active head stabilisation – both also found in insects – the inﬂuence of rotational optic ﬂow components which potentially obfuscate the estimation of relative nearness information from optic ﬂow is reduced. Hence, on the physical robot a prototype for mechanical gaze-stabilisation has been implemented and is currently compared to a software implementation.
Contemporary hardware solutions for collision sensing and avoidance, for instance, a planar laser range finder (LIDAR) is much heavier than the available payload on a small category unmanned aircraft. On the other hand, active range finders such as the Microsoft Kinect 2 are incapable of operating outdoors, hence one of the most feasible solutions remaining, could be the fusion of a lightweight camera and a robust computer vision algorithm. Salient features of vision-based solutions specifically for aerial robotic applications include its simplic- ity, reduced weight, and cost, justifying researchers’ focus on this technique. Ground robot application of a vision-based machine learning algorithm to avoid obstacles autonomously at a processing frequency of 7 Hz and a forward velocity of 5 ms -1 in a complex cluttered environment produced satisfactory results (<2% errors)  which was improved further in aerial applications of on-board depth computation and learning range classifiers in real-time, facilitating autonomous collision-free flight over 100 m . Although, these monocular vision- based solutions satisfy the hardware limitations, they fail to cope with the curbed processing power as they are computa- tionally expensive, thus a simple biologically inspired vision- based model is proposed, whose principle of operation is based on difference frame processing, a rather appealing behavior observed in the lobula layer of the locust visual neuron that performs an extremely simple process of image segmentation by computing absolute gray scale image difference over two consecutive frames –.
Nowadays the robotics is the developing and emerging fields. Especially the insect’s kind of robot is much fabricated. This kind of robot is very useful for many purpose. At present Hexapod is the one of the recent research area. The hexapod is used in various application. The bot used in the agricultural fields and uneven surface walking area where human intervention is not possible. The mechanical design is the important parameter for controlling the bot. The bot stability is based on the mechanical design . To sensing the outdoor environment the sensors are inbounded in the robot. Compass sensor is used for outdoor navigation. The bioinspired compass sensor uses only two pixel to determine the ultraviolet rays . The hexapod has a six dof and compliant legs used to move the unstable terrain. The open loop controller used for small robot . The dynamically stable plate form is designed in the paper. The simulation and open loop control system RHex is achieved . The actuator is another important parameter for deciding the robot movement. The various actuator like pneumatic and hydraulic is compared and pneumatic actuator is suited for the best one. Using joint actuated method used for great strength. In Matlab simmechanics used to analyze the model. It’s a very simpler method to analysis. The model is imported using the simmechanics module in the modelling software then the model is imported into the Matlab software. From the Matlab software the various inputs is given to the system to check the stability of the model. Based on this out put the joint torque is calculated this torque compared to the theoretical torque calculation . The trajectory generation and work volume of the robot is also discussed in the paper.
The system of traditional methods for avoiding vessel-bridge collision cannot meet the security needs for bridge, which just focused on strengthen the bridge’s own structure, such as casting large-scale bridges cushion caps or building anti -impact steel casing box or crash pile groups to minimize the loss caused by the ship collision, this facilities really protect the bridge pier in fact, but it also occupy the navigational water area, which would increase the risk of vessel-bridge collision to some extent.
The environment where the ship sails is changed, so the system does not generate the whole route initially, but only generates the part of the route closest to the current position. At the next time step this manoeuvring requirement may have to be altered owing to changing circumstances and diffi- culty in implementing the manoeuvre. This proce- dure can thus be called ‘dynamic route generation’. The ship states updated at every time step are input into the automatic collision system to calculate the desired route immediately in front of the ship in the form of waypoints. This can be translated into a heading requirement. The command is analogous to a rudder command that would be issued by heading autopilot. Thus the system can automatically control ship to sail to destination.
A single I/O pin is used to trigger ultrasonic waves (of frequency higher than 20kHz) and then “listen” for the echo return pulse. The sensor measures the time required for the echo return and returns this value to the microcontroller as a variable-width pulse via the same I/O pin. Ultrasonic sensors are generally used for anti-collision and rangefinder purposes by measuring the distance to an obstacle [M. Ishihara et al, 2009 .Few more areas where these sensors can be used are: security systems, parking assistant systems, interactive animated exhibits and robotic navigation.
Communication is a key issue in traffic management and tracking and is not an exception in railroad traffic control. With most of the communication and signaling equipment in Southern African Region are now obsolete and dilapidated, accidents are prone to occur which in most cases are fatal claiming a lot of life. Rail accidents are inevitable where communication is inefficient hence the need to device systems which will enable trains to communicate automatically, if we are to harness these problems. Currently, organizations like NRZ are implementing the radio communication system where the train driver would get information about the rail status from a control officer at a nearby station. The status is reflected on the point circuit, where points are track switches which are completed when a train passes a certain section. As the train progresses in its track, the driver is continuously reminded to check his/her points and to drive with caution when approaching points whose status cannot be clearly determined. In case the radio communication system fails, there is no collisionavoidance measure being implemented to curb these tragedies; hence this project would greatly improve safety in rail traffic.
A new kind of quadruped-imitating walkingrobot is designed, which is composed of a body bracket, leg brackets and walking legs. The walking leg of the robot is comprised of a first swiveling arm, a second swiveling arm and two striding leg rods. Each rod of the walking leg is connected by a rotary joint, and is directly controlled by the steering gear. The walking motion is realized by two striding leg rods alternately contacting the ground. Three assumptions are put forward according to the kinematic characteristics of the quadruped-imitating walkingrobot, and then the centroid equation of the robot is established. On this basis, this paper simplifies the striding process of the quadruped-imitating walkingrobot into an inverted pendulum model with a constant fulcrum and variable pendulum length. According to the inverted pendulum model, the stability of the robot is not only related to its centroid position, but also related to its centroid velocity. Takes two typical movement cases for example, such as walking on flat ground and climbing the vertical obstacle, the centroid position, velocity curves of the inverted pendulum model are obtained by MATLAB simulations. The results show that the quadruped-imitating walkingrobot is stable when walking on flat ground. In the process of climbing the vertical obstacle, the robot also can maintain certain stability through real-time control adjusted by the steering gears.
The paper presents an approach to the design of an optimal collisionavoidance maneuver under model uncertainty. The dynamical model is assumed to be only partially known and the missing components are modeled with a polynomial ex- pansion whose coefficients are recovered from sparse observations. The resulting optimal control problem is then translated into a robust reachability problem in which a controlled object has to avoid the region of possible collisions, in a given time, with a given target. The paper will present a solution for a circular orbit in the case in which the reachable set is given by the level set of an artificial potential function.
Next step for project methodology is the most crucial for Hexapod development. The algorithm for each leg to move is carefully design and constructed. Movements were insect-inspired where three crucial aspects are look into. Stability, speed and synchronization of all servos motor are coming into place. This is to ensure the
In order to design a leg structure of hexapodrobot, cockroach used as a model for biological inspirations. Three segments, coxa, femur and tibia, divided the cockroach leg. The coxa-femur and femur-tibia were joints together and connected by soft tissue. This implies that each joint works much like a ball and socket joint, and it can contribute to the leg compliance for the purpose of shock absorbing.
ing, accelerating etc.) and large (free following, turning etc.) scale modeling of the vehicle dynamics. These are coupled into a Markov Chain and investigated for im- plementing Cooperative Adaptive Cruise Control. The Markov Chain is used for achieving Cruise control goals using a predictive method. The vehicles use DSRC radio for communication, which transmits the vehicles’ speed, position and heading direction over a wireless broadcast link to all the neighboring cars. The movement model is transmitted over the DSRC and the receiving vehicle uses this model to develop a situational awareness model. With this information, a stochastic hybrid model is created with a set of discrete and continuous states. It is modeled in such a way that the each state uses ARX or pice-wise polynomial to track the designated functions and create the mathematical model. The authors consider a system model of n vehicles to implement the CACC and that all the vehicles are equipped with DSRC radio and the sensors capable of calculating the relative speed, distance and velocity from neighboring vehicles in its lane. The stochastic model is then devel- oped from the discrete time state space model of the CACC. The algorithm uses a stochastic model predictive optimization approach to evaluate the optimal speed of the vehicle. Again, the lane change factor has been neglected in this paper.
where is the stimulus of resource (as calculated by (1)), is the response threshold of activity with respect to skill (calculated by (3)). with values close to 0 representing low probability, while values close to 1 standing for high probability for allocating resource to activity . is calculated for each developer . Equation (4) is inspired by cosine similarity  which is a measure of similarity between two vectors. The proposed formula ensures that the most “suitable” resource will be selected for each task, since the higher the variance between the two sets, stimulus and threshold, the lower the probability. This ensures that the most suitable and not the “best” human resource will be selected for each task, as finding the “best” is not always related with the optimal decision .
process and normally operates within a restricted area at the factory. On that day, the worker was grabbed by robot and crushed him against a metal plate when he was standing in the safety cage. According to US government data, the fatality rate in the US in the years 2013 was about 2.1 for every 100,000 full time equivalent employees in manufacturing, while for the transportation equipment industry the fatality rate was just 0.9 per 100,000 employees which shown that it is more dangerous to work in the manufacturing area .
Many of the rules and procedures that apply to flight in con- trolled airspace are the legacy of MACs. In 1956, all 128 people aboard lost their lives when a DC-7 and a Lockheed Constellation collided over the Grand Canyon in VFR condi- tions. This tragedy led to public outcry for the modernization of the air traffic control system, resulting in the more effec- tive system that we have today. A 1978 collision over San Diego involving a Boeing 727 and a Cessna 172—both under radar control—caused the deaths of 144 people and resulted in even tighter restrictions on flights in heavily traf- ficked areas. Congress passed the Airport and Airway Safety Expansion Act after the 1986 collision of a DC-9 and a sin- gle-engine Piper over Cerritos, California, which claimed the lives of 82 people in the aircraft and 15 on the ground. The Airport and Airway Safety Expansion Act now requires all civil air carrier aircraft to be equipped with Traffic Alert and CollisionAvoidance Systems (TCAS).
approach in building a four-legged walking truck at General Electric in the mid-1960s.The project was part of a decade- long campaign to build advanced operators, capable of providing better dexterity through high-fidelity force feedback. The machine Mosher built stood 11 feet tall, weighed 3000 pounds, and was powered hydraulically. Each of the driver’s limbs was connected to a handle or pedal that controlled one of the truck’s four legs. Whenever the driver caused a truck leg to push against an obstacle, force feedback let the driver feel the obstacle as though it were his or her own arm or leg doing the pushing. After about 20 hours of training, Mosher was able to handle the machine with surprising agility. Films of the machine operating under his control show it ambling along at about 5 MPH, climbing a stack of railroad ties, pushing a foundered jeep out of the mud, and maneuvering a large drum onto some hooks. Despite its dependence on a well-trained human for control, this walking machine was a landmark in legged technology. D. 1970-‘Robert McGhee’
The motion planning layers is responsible computing safe, comfortable, and dynamically feasible trajectory from the vehicle current configurations to the goal configuration provide by the behavioral layer of the decision making hierarchy. Depending on context, the goal configuration may different. For example, the goal location may be the center point of the current lane a number of meters ahead in the direction of travels, the center of the stopped lines in next intersections, or the next desired parking spots. The motion planning components accepts information about statics and dynamics obstacle rounded vehicle and generate a collision-free trajectory that satisfies dynamics and kinematic constraint on the motion of vehicles. The motion planner also minimize a given objective functions. In addition to travel times, the objective function may penalize hazardous motions or motions that cause passenger discomfort. In a typical setup, the output of the motion planner is then passed to the local feedback control layers. Feedback controller generates input signals to regulated the vehicle to follow this given motion plan. A motion plan for the vehicle can take the form of a path or a trajectory. Note that such a solution does not prescribe how this path should be follow, ones can either choose a velocity profile for path or delegate this task to lower layers of the decision hierarchy. Within the trajectory planning framework. Control execution time is explicitly considered. This consideration allows for direct modeling of vehicle dynamics and dynamic obstacles.
There’s been quite a volume of research pertaining to autonomous indoor navigation of UAS, as evidenced by the work in [SMK11], [FHHLMTP12], [WCPCL13] and [BHR09]. This is a different approach to collisionavoidance than the one taken in this project. With autonomous navigation, collisions are avoided by the path planning algorithm. This approach leads to never ending up in a situation where the UAS has to actively take action to avoid an imminent collision. This type of research, however, has a lot in common with this project in the type of sensing that it requires. The team in [ CLDLCL14 ] built a UAS to navigate in a forest environment. This shares a lot of similarities with our plan in that it uses a 2D laser scanner for obstacle detection, and an IMU combined with SLAM for motion estimation. The group in [ SMK11 ] use SLAM along with data from an IMU to create a sophisticated self location and mapping system. The system used loop closure to reduce the error from SLAM as the UAS traverses the environment. Both in [WCPCL13] and [BHR09] location of the UAS is estimated using Kalman Filter fused data from SLAM, Optical flow and an IMU, while in [ CLDLCL14 ] SLAM and IMU readings are used. The teams in [ CLDLCL14 ] , [ SMK11 ] and [ WCPCL13 ] used advanced motion capture and characterization systems to characterize the dynamics of their UAS, and determine the parameters of their respective controllers. This allowed them to develop a model relating the controllable inputs pitch,roll,throttle,yaw to the motion of their platform. With this model they could then safely simulate most facets of the algorithm and tune off-line, before testing on a live platform. The system in [ WCPCL13 ] handles everything on-board and live, while the system in [ BHR09 ] handles most processing at a ground station, and has a 1-2 second delay for SLAM scan-matching which makes navigation slow. That system, however, was developed over 5 years ago and given current technology, the same processing could most likely be handled on-board and closer to real-time.
A novel real-time obstacle avoidance and line follower approach for mobile robots has been developed and tested in both simulation and real-time experiments. The proposed technique allows the simultaneous detection of obstacles with the control of the mobile robot to eliminate collisions and recover the path again. The novelty of this technique arises from the integration of data fusion techniques, along with the proposed algorithm for steering the mobile robot. This combination is extremely helpful for obtaining more accurate sensor data, thus enabling the robot to react more efficiently in case of obstacle detection. The performance of the proposed technique has been evaluated using simulated and experimental data. Our framework aims for higher efficiency and accuracy using data fusion, while ensuring overcoming obstacles along the path. In this work, we are mainly concerned about extracting necessary fused sensor data from multi-modality sensors for vigorous collision free trajectory planning approach.