No abstract

Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs. INDEX TERMS autonomous aerial vehicles, autonomous vehicles, collision avoidance, active and passive sensors, optimisation-based, force-field based, sense and avoid, geometry based

The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, selfcontained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed.

Power management of NoC-based many-core systems with runtime application mapping becomes more challenging in the dark silicon era. It necessitates a multi-objective control approach to consider an upper limit on total power consumption, dynamic behaviour of workloads, processing elements utilization, per-core power consumption, and load on networkon-chip. In this paper, we propose a multi-objective dynamic power management method that simultaneously considers all of these parameters. Fine-grained voltage and frequency scaling, including near-threshold operation, and per-core power gating are utilized to optimize the performance. In addition, a disturbance rejecter is designed that proactively scales down activity in running applications when a new application commences execution, to prevent sharp power budget violations. Simulations of dynamic workloads and mixed time-critical application profiles show that our method is effective in honoring the power budget while considerably boosting the system throughput and reducing power budget violation, compared to the state-of-the-art power management policies.

Power management of networked many-core systems with runtime application mapping becomes more challenging in the dark silicon era. It necessitates considering network characteristics at runtime to achieve better performance while honoring the peak power upper bound. On the other hand, power management has a direct effect on chip temperature, which is the main driver of the aging effects. Therefore, alongside performance fulfillment, the controlling mechanism must also consider the current cores' reliability in its actuator manipulation to enhance the overall system lifetime in the long term. In this paper, we propose a multiobjective dynamic power management technique that uses current power consumption and other network characteristics including the reliability of the cores as the feedback while utilizing fine-grained voltage and frequency scaling and per-core power gating as the actuators. In addition, disturbance rejecter and reliability balancer are designed to help the controller to better smooth power consumption in the short term and reliability in the long term, respectively. Simulations of dynamic workloads and mixed criticality application profiles show that our method not only is effective in honoring the power budget while considerably boosting the system throughput, but also increases the overall system lifetime by minimizing aging effects by means of power consumption balancing

No abstract

Two important aspects in dealing with autonomous navigation of a swarm of drones are collision avoidance mechanism and formation control strategy; a possible competition between these two modes of operation may have negative implications for success and efficiency of the mission. This issue is exacerbated in the case of distributed formation control in leader-follower based swarms of drones since nodes concurrently decide and act through individual observation of neighbouring nodes' states and actions. To dynamically handle this duality of control, a plan of action for multi-priority control is required. In this paper, we propose a method for formation-collision co-awareness by adapting the thin-plate splines algorithm to minimize deformation of the swarm's formation while avoiding obstacles. Furthermore, we use a non-rigid mapping function to reduce the lag caused by such maneuvers. Simulation results show that the proposed methodology maintains the desired formation very closely in the presence of obstacles, while the response time and overall energy efficiency of the swarm is significantly improved in comparison with the existing methods where collision avoidance and formation control are only loosely coupled. Another important result of using non-rigid mapping is that the slowing down effect of obstacles on the overall speed of the swarm is significantly reduced, making our approach especially suitable for time critical missions.

No abstract