LOGISTICS


An autonomous tugger with a load of packages rounds a corner at a FedEx distribution centre


device is the onboard Vecna Autonomy Stack navigation technology. As Daniel Teobald, chief innovation officer at Vecna Robotics, explained, the company’s robots employ a multi-modal approach to sensing, and use data from stereovision, time-of-flight cameras, structured light cameras, lidar, radar, ultrasonic, infrared, GPS, ultra-wide band, Bluetooth, Wi-Fi, accelerometers, gyros, compass, pressure sensors and current sensors. ‘Tere is no one perfect sensor, and choosing


the appropriate sensor suite depends on the specific operating environment. Safety is always the first priority and this technology analyses billions of data points per second, enabling robots to react in real time to their surroundings,’ he said. In the warehouse environment, Teobald


said that Vecna robots can also re-route around obstacles, identify static and moving objects, read barcodes to identify specific items, and adjust speed based on environmental conditions. Data from the various sensors employed in Vecna’s devices are calibrated together and then filtered to make sure that all the sensor data is consistent with the believed current location of the robot. If there are outliers or sensor data that conflicts, Teobald explained that the system safely stops and asks for help if the robot cannot resolve the discrepancy on its own. At present, Teobald


‘With the right vision technology, vehicles


There is no one perfect sensor, and choosing the appropriate sensor suite depends on the specific operating environment


said that Vecna’s self-driving vehicles are active in a wide array of global distribution centres and regional third party logistic sites, as well as manufacturing facilities and warehouses. ‘Our robots are one of the only self-driving


vehicles on the market to identify and safely deal with moving objects, such as people or other equipment, and locate and pick up materials outside of pre-determined locations. For instance, if a human drops off a pallet a few inches from its expected position, most AGVs [automated guided vehicles] would not be able to retrieve it,’ he remarked. ‘Our implementations within FedEx, for


example, have led to a significant reduction in damage to facilities, equipment and materials, and increased safety for staff,’ Teobald added. For Teobald, the key benefits of robotic


vision are safety and flexibility – and, among other things, he is keen to stress that devices equipped with such technology are able to confidently manoeuvre through dynamic environments and be agile as demand fluctuates.


20 Imaging and Machine Vision Europe • December 2018/January 2019


can swiſtly adapt to facility changes, be added to new operations, or change work zones without retraining, remapping, or installing reflectors or guiding wires. Tis allows companies to scale automation, as their needs evolve and business grows,’ he said. In the coming years, Teobald predicts that


vision and machine learning will continue to be the most crucial technologies for logistics automation, particularly because he believes they are the key to remaining competitive as customer expectations rise and labour shortages continue to put a strain on the competitiveness of businesses. ‘Te goal is to supercharge human


productivity, allowing humans to get more done while having safer, more enjoyable work. Vision and machine learning provide self-driving vehicles with the ability to operate safely, to self-correct, and most importantly to learn – generating a continuous cycle of improvement,’ he said. ‘Tese technologies allow organisations


to harness the fundamental strengths of robots: tireless precision in heavy-duty work. Combining these strengths with human expertise and real-time intelligence will create flexible processes that effectively tackle changing fulfilment requirements,’ he concluded. O


@imveurope www.imveurope.com


Vecna Robotics


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