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14 Platform one Dr Donough Wilson Dr Wilson is innovation lead at aviation, defence, and homeland security innovation consultants, VIVID/ futureVision. His defence innovations include the cockpit vision system that protects military aircrew from asymmetric high-energy laser attack. He was first to propose the automatic tracking and satellite download of airliner black box and cockpit voice recorder data in the event of an airliner’s unplanned excursion from its assigned flight level or track. For his ‘outstanding and practical contribution to the safer operation of aircraft’ he was awarded The Sir James Martin Award 2018/19, by the Honourable Company of Air Pilots. Paul Weighell Paul has been involved with electronics, computer design and programming since 1966. He has worked in the real-time and failsafe data acquisition and automation industry using mainframes, minis, micros and cloud-based hardware on applications as diverse as defence, Siberian gas pipeline control, UK nuclear power, robotics, the Thames Barrier, Formula One and automated financial trading systems. Ian Williams-Wynn Ian has been involved with unmanned and autonomous systems for more than 20 years. He started his career in the military, working with early prototype unmanned systems and exploiting imagery from a range of unmanned systems from global suppliers. He has also been involved in ground-breaking research including novel power and propulsion systems, sensor technologies, communications, avionics and physical platforms. His experience covers a broad spectrum of domains from space, air, maritime and ground, and in both defence and civil applications including, more recently, connected autonomous cars. Unmanned Systems Technology’s consultants Researchers at Northwestern University in the US have developed the first decentralised algorithm with a collision- free and deadlock-free guarantee (writes Nick Flaherty). The researchers tested the algorithm in a simulation of 1024 robots and on a swarm of 100 real robots in the laboratory. The robots reliably, safely and efficiently converged to form a predetermined shape in less than a minute. This can be used for swarms of airborne craft or to prevent collisions between driverless cars without requiring a lot of roadside comms infrastructure. “If you have many autonomous vehicles on the road, you naturally don’t want them to collide with one another or get stuck in a deadlock,” said Prof Michael Rubenstein at Northwestern’s Engineering Department, who led the study. “Understanding how to control swarm robots to form shapes means we can understand how to control fleets of vehicles as they interact with each other.” The advantage of a swarm of small robots – versus one large robot or a swarm with one lead robot – is the lack of centralised control, but that can quickly become a central point of failure. On the other hand, a decentralised algorithm acts as a fail-safe but can struggle with deadlock conditions. “If the system is centralised and a robot stops working, the entire system fails,” Prof Rubenstein said. “In a decentralised system, there is no leader telling all the other robots what to do; each one makes its own decisions. If one robot fails in a swarm, the swarm can still accomplish the task.” However, the robots still need to coordinate in order to avoid collisions and deadlock. To do that, the algorithm views the ground beneath the robots as a grid. Before deciding on where to move, each robot uses sensors to communicate with its neighbours, determining whether nearby spaces within the grid are vacant or occupied. “The robots refuse to move to a spot until that spot is free and until they know that no other robots are moving to that same spot,” Prof Rubenstein said. “They are careful and reserve a space ahead of time.” A key part of the algorithm is actually limiting how far ahead each robot can see. “Each robot can only sense three or four of its closest neighbours,” said Prof Rubenstein. “They can’t see across the whole swarm, which makes it easier to scale the system. The robots interact locally to make decisions without global information.” In the test swarm, for example, 100 robots can coordinate to form a shape within a minute. In some previous approaches, it could take a full hour. Swarming No-collision code April/May 2020 | Unmanned Systems Technology A non-blocking algorithm for autonomous systems has enabled a 100-robot swarm to converge in a reliable and safe manner