Path Planning for Autonomous Soaring MAVs in Urban Environments

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1 Please select category below: Normal Paper Student Paper Young Engineer Paper Path Planning for Autonomous Soaring MAVs in Urban Environments C.S. Leung 1, M. Elbanhawi 1, A. Mohamed 1, R. Clothier 1, A. Fisher 1 and M. Simic 1. 1 Sir Lawrence Wackett Aerospace Research Centre, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia. Abstract This paper presents a path planning approach for Micro Aerial Vehicles (MAVs) in urban environments. We show that it is feasible to extend the endurance and range of an MAV by exploiting the orographic lifts from buildings in windy conditions. A sampling based rapidlyexploring random tree algorithm is implemented to generate feasible paths between the start and goal in a modelled urban environment. A cost function is proposed that leverages sampling based planner metric sensitivity to properly capture the aircraft s dynamics and utilize the environment for soaring. A three-degree of freedom aircraft model with a high fidelity control space is used to generate realistic reference trajectories. Multiple scenarios are setup to assess the behaviour of the planner and the effectiveness of the proposed metrics. The results obtained from the presented experiments validate the potential of utilising orographic lift for improving MAV endurance. This work also highlights the sensitive nature of sampling based planners to implementation parameters and emphasises the need for an adaptive parameter scheme. Keywords: Path-planning, Autonomous Micro-Air-Vehicles, Updraft, Energy-harvesting, Orographic lift, Urban environment, Soaring Glider. Autonomous Soaring The range and endurance of Micro Aerial Vehicles is limited by the available size, weight, and power of the air vehicle [1]. Extending the MAV endurance is important for Intelligence Surveillance and Reconnaissance (ISR) missions. One bio-inspired technique to extend the endurance is through exploitation of energy from the surrounding environment; and in particular, energy from wind. Birds commonly exploit updrafts (regions of positive wind flow) to minimise energy expenditure. Updrafts can occur due to convection at the base of clouds or changes in air density (thermals). Birds are also well adept at exploiting the updrafts surrounding terrain features (i.e. orographic lift) for long range migration [2] Taking inspiration from nature, a number of researchers have explored various aspects to the challenge of designing Unmanned Aircraft Systems (UAS) capable of exploiting thermals [3-5]. Researchers have theoretically explored the potential for MAVs to harvest energy in regions of orographic lift [6-8] but a practical demonstration of the concept has yet to be achieved. There are a number of challenges in sensing, control, navigation, and guidance required to achieve autonomous orthographic soaring of MAVs in urban environments. This paper describes the development and simulation testing of a mission planning system designed specifically for off-line planning of energy-efficient flight paths for MAVs operating in urban environments. The objective of the planner is to manage the energy of the MAV (potential and kinetic) and avoid obstacles, to reach a desired waypoint as quickly as possible. Where

2 energy is insufficient to meet a goal, energy must be harvested from the surrounding wind flow (i.e., orographic flow). The presented planner generates energy efficient flight paths with a bias towards probable updraft regions, which can be later implemented in parallel to realtime path planning [9, 10]. Such a planner must incorporate a priori knowledge of the complex and interacting wind flow structures present in urban environments (e.g., around trees and tall buildings, etc.). This paper presents a simplified model of the wind environment; to enable development and validation of the behaviour of the mission planner. Follow on work will test the performance of the planner using higher fidelity wind models. Methodology The RMIT Bundoora West campus was chosen as a representative urban environment; it is ideally suited for this study due to the abundance and strength of updrafts [6-8]. A commercially available soaring model aircraft known as the Alula (produced by Dream- Flight) was selected as a representative MAV due to its known flight characteristics [6] which are summarised in Table 1. Furthermore, Table 1 also summarises the state space constraints and control input used in planning. Based on these parameters a rigid 3DOF model was computed using the approach outlined by Liu, et al. [11]. A horizontal wind gradient was defined within the configuration space (C-space) in accordance with [7]. The aircraft s weight-specific total energy was defined as presented in Eqn. 1 where h is the altitude of the aircraft above ground level, v represents the velocity of the aircraft, and g is gravitational acceleration. Table 1: Summary of MAV specifications, state space constraints and control input Specifications State constraints Control inputs Parameter Value State (units) Lower limit Upper limit Input variable Value Mass 0.248kg Glide path angle ( ) Pitch ±20 Wing span 0.9m Heading angle ( ) - δpitch 5 Wing area 0.167m 2 Angle of attack ( ) Yaw ±20 Length 0.48m Velocity (m/s) 0 10 δyaw 5 E total = h + v2 2g (1) Path Planner Planning is defined as the process of generating a sequence of collision free and feasible actions from start to a goal state. Based on previous studies in path planning for ground and air robots [5, 12-16], the Rapidly-exploring Random Tree (RRT) algorithm by LaValle [17] was implemented. RRTs are effective in capturing the configuration space and searching the environment. RRT expand by randomly nominating a state in the state space and connecting it to the nearest existing state in the tree. The nearest node is nominated using a nearest neighbour search. This search attempts to minimise the predefined search metric. Sampling based planners in general have been shown to be sensitive to the implementation parameters, particularly, the metric function [18]. The selection of an appropriate metric function has been shown to be intractable. Three cost functions (i.e. metrics) containing the variables outlined in Table 2 were developed. Weighting factors were applied to each variable to scale their significance (see Fig. 1). The influence of each metric and sensitivity of their weightings are discussed in the results section. Metric 1 only considers the equally-weighed Euclidean distance, which is commonly used in path planning. Metric 2 considers all of the cost variables (equally-weighted) given in Table 2. Finally, Metric 3 considers all of the cost variables given in Table 2 with the weightings determined based on the observation of the planner performance over a sample number of simulations. The specific combination of weightings chosen was that which achieved a balance between goal biasing, updraft biasing, distance minimisation and energy maximisation.

3 The proposed planner takes into consideration the MAV s altitude and velocity (i.e. potential and kinetic energy respectively) throughout the planning space. Using experimental data from [8], the glide polar of the MAV was used to determine the maximum distance the MAV could travel for a given sink rate. The planner would terminate once an energy sufficient state with a collision free goal path had been reached. In situations where those states were not feasible, the RRT planner would explore the surrounding state-space to determine feasible paths to the goal. Table 2: Variables in metrics Type Variables Description Cost Euclidian distances Connect to a node closer to it to increase the randomness of the path whilst also considering the distance to the goal to subtly bias the path Cost Number of connections Decrease the possibility of being stuck in a significantly low cost node Reward Updraft magnitude Increase the density of nodes in updraft regions for more possible high updraft paths Cost Failure rate of the nodes Reduce the chance of selecting a node with high failure rate due to obstacles Reward Energy level Connect nodes to high energy nodes to increase the possibility of gaining more energy Fig. 1: Variable weightings Simulation Setup Three flight environments (i.e. scenarios) were created to test the performance of the planner for each of the three metrics (see Table 3 and Fig. 2). The environmental parameters for each scenario are summarised in Fig. 2. Scenario 1 was designed to determine the sensitivity of the planner to small but strong regions of updrafts, which commonly form around tall buildings. The magnitude of the updraft regions were selected based on the strongest updraft speed estimated in [7]. Scenario 2 was designed to determine the sensitivity of the planner to lower energy environments (smaller and weaker updrafts). Lastly, Scenario 3 was used to test the performance of the planner in the presence of regions of undesireable flow (e.g, regions of downdraft or wake). The downdraft magnitude was set at -2.5m/s to ensure that the aircraft would not reach the updraft region if it tried to fly through it. Fig. 3 shows the maximum range of the candidate MAV for its initial conditions (h= 20m, v= 5ms -1 ) assuming the MAV glides at its best glide ratio without any energy harvesting from the surrounding environment. The results depicted in Fig. 3 are used as a performance baseline. The initial conditions chosen represent the altitude and velocity conditions when hand-launching the glider through a technique known as discus launching [8]. Discus launching involves gripping the glider from the wing-tip and accelerating it in a circular path (similar to that of the discus-throw in athletic events). Table 3: Environmental parameters of scenarios Scenario 1 Scenario 2 Scenario 3 Updraft magnitude 2.5 m/s 0.5 m/s 2.5 m/s Updraft area m m m 2 Downdraft magnitude 0 m/s 0 m/s -2.5 m/s Downdraft area 0 m 2 0 m 2 2 x m 2

4 (a) (b) (c) Fig. 2: Simulation environments: (a) Scenario 1, (b) Scenario 2 (c) Scenario 3 Fig. 3: MAV s reachability region without updrafts Results All three metrics were tested for each of the three scenarios. Due to the superior performance of the planner when using Metric 3, the associated flight paths are illustrated in Fig. 4. The performance of all metrics, for 100 simulation runs each, are shown as boxplots in Fig. 5. The following paragraphs describe results for each tested scenario. For Scenario 1, Metric 1 is the standard Euclidean distance, which ignored the updrafts. This resulted in the MAV taking a longer route to the goal and spent the least amount of time in the updraft, which resulted in an extended flight time to reach the goal in contrast to Metric 2 and 3. Metric 2 enabled the aircraft to stay in the updraft the longest time in order to gain maximum height, consequently increasing flight time and distance travelled. This is due to the inability of the planner to consider available energy. Out of all the metrics tested, Metric 3 resulted in the shortest flight time, as it consistently utilised the strong updraft region to gain energy from a linear glide (time not wasted by circling in updraft) and successfully navigate to the goal (see Fig. 4a). By efficiently utilising the small and concentrated updraft region, the planner resulted in more successful runs. Conversely, the use of Metric 2 was particularly sensitive to updrafts such that the MAV was unable to exit the region of high updraft. This can be observed through the relatively high planning failure rate for Metric 2 in Fig. 5. Visualised flight paths computed using Metric 3 (Fig. 4a) show two main routes towards the updraft region, which converge to one main route when exiting the updraft towards the goal point. The potential energy gained from the updraft was sufficient to allow the MAV to reach its final destination. This gain in potential energy can be seen in the travelled distances subplot of Fig. 5, which is compared to the baseline performance.

5 In Scenario 2 (all metrics), the MAV reached the goal but did not gain as much altitude due to the lower magnitude of updraft available. Circling within the updraft to gain energy was expected, and observed, for Metrics 2 and 3. However, the majority of the computed flight paths travelled along the longitudinal direction of the updraft to get as close as possible to the final goal alleviating the need to circle. This is apparent from comparing results of Scenarios 1 and 2 for Metric 3 (Fig. 4). Due to the low updraft energy defined in Scenario 2, the mean mission time was longer and a significant proportion of the flight time was spent in the updraft region, in which the MAV was unable to gain height (due to insufficient vertical velocity). Flight plans developed using Metric 2 had the longest flight time within the updraft similar to Scenario 1, which further reinforced the influences that the metric has on the mission planner performance. The paths generated in Scenario 3 (Metric 2 and 3) are observed to avoid the undesirable downdraft regions. The path planning around the downdrafts resulted in the MAV having to take a longer route, thus requiring it to harvest more energy. To do so, the MAV circles inside the updraft region to gain potential energy. The increased time spent in the updraft for Metric 3 subsequently increased the flight time (compared to results for Scenario 1 and 2). The standard Euclidean metric (Metric 1) led to the aircraft flying into downdraft regions and spending less time in updrafts. This reduced the maximum height reached, however, the time/distance airborne and mission planning failure rate are similar in comparison to Metric 3. Metric 2 had a very high planning failure rate of 83%. This is largely due to the high sensitivity of Metric 2 to downdrafts regions; the planner could not exit the updraft regions. In contrast, Metric 3 considered the number of connections within a certain region and thus exited the updraft region. (a) (b) (c) Fig. 4: Simulated results of metric 3 (a) Scenario 1 (b) Scenario 2 (c) Scenario 3

6 Fig. 5: Comparison of simulation data Discussion Sensitivity analysis was conducted to determine the significance of the metric weightings in the performance of the mission planner. Overall, Metric 3 performed the best in terms of consistency in path performance; producing updraft-biased flight paths with a low flight time and low failure rate. Although Metric 2 was consistently gaining more height and spending more time in updraft regions it was unreliable due to the high failure rate. Performance of Metric 2 was too random owing to the equal weighting of all the cost/rewards. Metric 1 did not utilise the updrafts and where included for reference. It is apparent that the performance of the path planning algorithm is highly dependent on the Metric and weightings used. This tuning is highly mission-dependent. An optimal balance between the period when the MAV is gaining energy in the updraft and the time it takes to complete its mission is required. Developing a metric that accurately captures the aircraft behaviour is a challenging task [19]. In a realistic wind scenario, the intensity of the updraft varies between buildings in a medium density urban area. It may be disadvantageous to exit the updraft prematurely as there may not be another strong updraft along the way. On the other hand, the MAV can utilise the orographic lift to gain altitude from updrafts at a building until a change in wind conditions enabling it to reach its goal. Real time sensing of wind conditions, and models for predicting the wind condition at various other locations based on sensor data are needed to enable such a capability.

7 Concluding Remarks This paper described the development of an offline mission planning tool to enable MAVs to autonomously harvest wind energy within an urban environment. The presented simulations utilised a simple 3DOF dynamic model, simplified wind flows, and a modified RRT pathplanning algorithm. Simulated results demonstrate the capability of the planner to increase the endurance and success rate of MAV missions. Testing for three different planning metrics and flight environments provided an insight into the behaviour of the path planning algorithm and the sensitivity of the weightings. The overall performance of the path planner was found to be metric sensitive, with a difference in planning failure rate of up to 80% between metrics and environments. The extension of this work is to create a realistic wind model of the environment to further increase the realism of the model. Monte Carlo simulations will be conducted using the presented mission planning algorithm to determine an optimal set of weightings. Potential for commercial applications of this work could involve long-range ISR urban operations for law enforcement agencies. It can also be expanded to MAV operations in coastal regions (e.g., surf lifesaving or shark patrol) where orographic updrafts are also common. Acknowledgments This research was undertaken as part of the RMIT Unmanned Aircraft Systems Research Team, within the Sir Lawrence Wackett Aerospace Research Centre, at RMIT University. This research was made possible, in part, through support and funding from the Defence Science Institute and the Defence Science and Technology Organisation. References [1] A. Mohamed, K. Massey, S. Watkins, and R. Clothier, "The attitude control of fixed-wing MAVS in turbulent environments," Progress in Aerospace Sciences, vol. 66, pp , [2] J. T. Mandel, G. Bohrer, D. W. Winkler, D. R. Barber, C. S. Houston, and K. L. Bildstein, "Migration path annotation: cross-continental study of migration-flight response to environmental conditions," Ecological Applications, vol. 21, pp , [3] M. J. Allen, "Autonomous soaring for improved endurance of a small uninhabited air vehicle," in Proceedings of the 43rd Aerospace Sciences Meeting, AIAA, [4] I. Cowling, S. Willcox, Y. Patel, P. Smith, and M. Roberts, "Increasing persistence of UAVs and MAVs through thermal soaring," Aeronautical Journal, vol. 113, pp , [5] J. W. Langelaan, "Gust energy extraction for mini and micro uninhabited aerial vehicles," Journal of guidance, control, and dynamics, vol. 32, pp , [6] C. White, S. Watkins, E. W. Lim, and K. Massey, "The soaring potential of a micro air vehicle in an urban environment," International Journal of Micro Air Vehicles, vol. 4, pp. 1-14, [7] A. Mohamed, C. White, and S. Watkins, "A Numerical Study of the Updrafts over a Building, with Comparison to Wind-Tunnel Results," in 15th Australasian Wind Engineering Society Workshop. Sydney, Australia, 2012, pp [8] C. White, E. Lim, S. Watkins, A. Mohamed, and M. Thompson, "A feasibility study of micro air vehicles soaring tall buildings," Journal of Wind Engineering and Industrial Aerodynamics, vol. 103, pp , [9] O. Ariff and T. Go, "Waypoint navigation of small-scale UAV incorporating dynamic soaring," Journal of Navigation, vol. 64, pp , [10] K. Turkoglu, Y. J. Zhao, and B. Capozzi, "Real-Time Insitu Strategies for Enhancing UAV Endurance by Utilizing Wind Energy," [11] Y. Liu, S. Longo, and E. C. Kerrigan, "Nonlinear predictive control of autonomous soaring UAVs using 3DOF models," in Control Conference (ECC), 2013 European, 2013, pp [12] M. Elbanhawi and M. Simic, "Sampling-Based Robot Motion Planning: A Review," Access, IEEE, vol. 2, pp , [13] C. Goerzen, Z. Kong, and B. Mettler, "A survey of motion planning algorithms from the perspective of autonomous UAV guidance," Journal of Intelligent and Robotic Systems, vol. 57, pp , [14] K. Yang, S. Keat Gan, and S. Sukkarieh, "A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV," Advanced Robotics, vol. 27, pp , 2013/04/

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