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Engineering
Marine Engineering

Obstacle avoidance using echo sounder sonar

Profile image of joao sousajoao sousa

2011, OCEANS 2011 IEEE - Spain

Abstract

Developing obstacle avoidance algorithms for low cost Autonomous Underwater Vehicles using single beam returned echo sounder sonar is a difficult task. In this paper, we propose an intelligent obstacle avoidance algorithm that maps the obstacle, avoids it with a guarantee that it will not get stuck and efficiently traverses a path towards the destination using navigation functions. We present a complete obstacle avoidance system with the help of hybrid automata, probabilistic mapping and navigation functions. Simulation results are presented showing the validity of our approach.

FAQs

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What novel features does the proposed echo sounder technique incorporate for obstacle avoidance?add

The study introduces an intelligent navigation algorithm that enables horizontal obstacle avoidance using only single point echo sounder data.

How does the presented method ensure the AUV avoids traps while navigating?add

The proposed hybrid controller guarantees non-trapping maneuvers by combining a safe controller with navigation functions.

What simulation parameters were used to test the AUV's obstacle avoidance capabilities?add

The AUV was simulated at a constant speed of 1 m/s with a maximum turn rate of 0.6.

How does the mapping function handle noisy sonar data in obstacle detection?add

The mapping technique employs Histogramic In-Motion Mapping (HIMM) to filter noise and construct robust obstacle maps.

What adaptations were made to navigation functions for dealing with partially known environments?add

Dipolar Navigation Functions were utilized to effectively direct the AUV in environments with stationary, partially known obstacles.

Figures (8)
Fig. 1. Block diagram of the complete control scheme. The AUV uses an echo sounder sonar to detect obstacles in the direction of its heading. The problem is that the typical sensor measurements are noisy. We use a mapping technique to filter out noise and to map the obstacle. Using this map, a supervisor triggers a safe controller to avoid obstacles. This controller does not ensure that the vehicle will reach the destination, but it ensures obstacle avoidance and guarantees that the AUV is not stuck in traps. A navigation function based controller drives the AUVs towards the destination. The safe controller and the navigation function controller work in tandem to avoid the obstacle and to reach the destination. Figure 1 describes our approach.
Fig. 1. Block diagram of the complete control scheme. The AUV uses an echo sounder sonar to detect obstacles in the direction of its heading. The problem is that the typical sensor measurements are noisy. We use a mapping technique to filter out noise and to map the obstacle. Using this map, a supervisor triggers a safe controller to avoid obstacles. This controller does not ensure that the vehicle will reach the destination, but it ensures obstacle avoidance and guarantees that the AUV is not stuck in traps. A navigation function based controller drives the AUVs towards the destination. The safe controller and the navigation function controller work in tandem to avoid the obstacle and to reach the destination. Figure 1 describes our approach.
— ee oe eC eee ee OE ae ee ee measurements (by valid range measurements, we mear range measurements that are below the maximum range returned by the sonar when there is no obstacle ahead) D = {d(ki), d(k2), ..., d(k,)} taken at time step kj, « = 1,...,n, and the set of vehicle’s pose x(ki) = [a(ki), y(ki), wW(k;)] at the same time in- stants that the range measurements were taken X = {x(k1), x(k2), ..., X(kn)}. From these two sets we construct a grid map using concepts of HIMM. The mag G is a grid filled with rectangular cells of length g, and width g,. Assume that there are G), cells along x direction and G, cells in the y direction. Each cell G(i,c) has an initial value of 0, VI = 1,...,Gn,Vw = 1,...,G@yw. As the vehicle moves around the environment and collect range measurements, we increase the corresponding values of the grid cells G(l, c). Similar to [12], we update the value of the cell G(I,c) which is in the acoustic axis (which is typical the same as the vehicle heading ~(k;)) and corresponds tc the measured distance d(k;) from the vehicle pose x(k;). as shown in Figure 2. Unlike proposed in [12], we do not decrement the value of any cells during the grid construction. Fig. 2. Construction of the grid G. One cell (shaded) is incremented given a pair of range d(k) and vehicle pose x(k) at time k.
— ee oe eC eee ee OE ae ee ee measurements (by valid range measurements, we mear range measurements that are below the maximum range returned by the sonar when there is no obstacle ahead) D = {d(ki), d(k2), ..., d(k,)} taken at time step kj, « = 1,...,n, and the set of vehicle’s pose x(ki) = [a(ki), y(ki), wW(k;)] at the same time in- stants that the range measurements were taken X = {x(k1), x(k2), ..., X(kn)}. From these two sets we construct a grid map using concepts of HIMM. The mag G is a grid filled with rectangular cells of length g, and width g,. Assume that there are G), cells along x direction and G, cells in the y direction. Each cell G(i,c) has an initial value of 0, VI = 1,...,Gn,Vw = 1,...,G@yw. As the vehicle moves around the environment and collect range measurements, we increase the corresponding values of the grid cells G(l, c). Similar to [12], we update the value of the cell G(I,c) which is in the acoustic axis (which is typical the same as the vehicle heading ~(k;)) and corresponds tc the measured distance d(k;) from the vehicle pose x(k;). as shown in Figure 2. Unlike proposed in [12], we do not decrement the value of any cells during the grid construction. Fig. 2. Construction of the grid G. One cell (shaded) is incremented given a pair of range d(k) and vehicle pose x(k) at time k.
Fig. 3. Hough transform generating line segment (green line) and line segments being converted into rectangular polygon (red). for some threshold value v. Since the navigation functions and safe controller requires feature based obstacles, we needed to convert the set C of point obstacles to a geometric representation. This is done by first using the classical Hough Transform [13] to extract a set Z = {z1, ..., 2m} of m line segments from C, and then each line segment z; is converted to a convex polygon 0; € R**? (convex obstacle representation required by the safe controller), represented by a set of four points describing a rectangle. This process is shown in Figure 3, for a set of points generating a single line. As the Hough transform can act only on a relatively large set of points, and not on individual or small set of points, the elements of the set S = {C(/,c): C(l,c) > u, Vl, Ve € Ch}, for some u > v are converted into small squared obstacles o, (the size of the square side is equal to the grid spacing). This is important as the vehicle, when traveling straight forward, will obtain a set of range measurements that will increment the value of a small set of cells or even of a single grid cell. The output of the mapping function is a set of obstacles O = {01, ..., Om+s} consisting of the m rectangular and the s squared obstacles found. Once the mapping function returns the set O, we c update the map M by making M = MU O. After th an is, the algorithm resets the sets D, X and the grid map G. This way we can save memory by keeping only the set of obstacles found by the mapping function, and not t entire grid G. It is also possible not to fully reset the sets and X, but erase only the oldest values, keeping the g < more recent data. After resetting the mapping variables, t algorithm starts accumulating new range measurements and vehicle poses on the sets D and X respectively, and once t M he D n he ne number of valid range measurements reaches n, the mapping function is called again.
Fig. 3. Hough transform generating line segment (green line) and line segments being converted into rectangular polygon (red). for some threshold value v. Since the navigation functions and safe controller requires feature based obstacles, we needed to convert the set C of point obstacles to a geometric representation. This is done by first using the classical Hough Transform [13] to extract a set Z = {z1, ..., 2m} of m line segments from C, and then each line segment z; is converted to a convex polygon 0; € R**? (convex obstacle representation required by the safe controller), represented by a set of four points describing a rectangle. This process is shown in Figure 3, for a set of points generating a single line. As the Hough transform can act only on a relatively large set of points, and not on individual or small set of points, the elements of the set S = {C(/,c): C(l,c) > u, Vl, Ve € Ch}, for some u > v are converted into small squared obstacles o, (the size of the square side is equal to the grid spacing). This is important as the vehicle, when traveling straight forward, will obtain a set of range measurements that will increment the value of a small set of cells or even of a single grid cell. The output of the mapping function is a set of obstacles O = {01, ..., Om+s} consisting of the m rectangular and the s squared obstacles found. Once the mapping function returns the set O, we c update the map M by making M = MU O. After th an is, the algorithm resets the sets D, X and the grid map G. This way we can save memory by keeping only the set of obstacles found by the mapping function, and not t entire grid G. It is also possible not to fully reset the sets and X, but erase only the oldest values, keeping the g < more recent data. After resetting the mapping variables, t algorithm starts accumulating new range measurements and vehicle poses on the sets D and X respectively, and once t M he D n he ne number of valid range measurements reaches n, the mapping function is called again.
Fig. 4. Isoclines of a navigation function and the path computed using it. The stability of the control law (2) was studied and proved in [16]. In figure 4, the isoclines of a navigation function are plotted using polygons defined by line segments. The computed path is depicted in a dotted line. The scenario is the same as in figure 7.
Fig. 4. Isoclines of a navigation function and the path computed using it. The stability of the control law (2) was studied and proved in [16]. In figure 4, the isoclines of a navigation function are plotted using polygons defined by line segments. The computed path is depicted in a dotted line. The scenario is the same as in figure 7.
Fig. 5. Hybrid automaton for the safe controller We are interested in finding a controller that keeps the vehicle inside a safe set, that is, a set for which we can guarantee that the vehicle does not enter any obstacle, as long as its trajectory remains in that set. In order to avoid collisions we introduce the two avoidance maneuvers that drive the vehicle away from the obstacle, turning it in the desired direction. These maneuvers are symmetric semi- circular trajectories, departing from the vehicle’s position, with radius equal to the minimum turning radius of the vehicle. Thus, when the vehicle is moving, we test its state continuously, analyzing the viability of the avoidance maneuvers starting from the state at the next time step (a trajectory is viable if and only if it does not collide with the obstacle). An avoidance maneuver should be engaged every time that, by not doing so, the vehicle will enter a collision course with the obstacle. Our control strategy is implemented through a hybrid automaton (see figure 5), covered in greater detail in [17]. The controller starts in a test mode, testing the viability of the left and right avoidance maneuvers separately. Should one of the maneuvers fail (condition R or L), there is still a way to avoid the obstacle, and so the controller transits to the correspondent fail mode. If both maneuvers become inviable simultaneously or, if in fail mode, the remaining maneuver becomes inviable, the controller starts the correspondent avoidance maneuver.
Fig. 5. Hybrid automaton for the safe controller We are interested in finding a controller that keeps the vehicle inside a safe set, that is, a set for which we can guarantee that the vehicle does not enter any obstacle, as long as its trajectory remains in that set. In order to avoid collisions we introduce the two avoidance maneuvers that drive the vehicle away from the obstacle, turning it in the desired direction. These maneuvers are symmetric semi- circular trajectories, departing from the vehicle’s position, with radius equal to the minimum turning radius of the vehicle. Thus, when the vehicle is moving, we test its state continuously, analyzing the viability of the avoidance maneuvers starting from the state at the next time step (a trajectory is viable if and only if it does not collide with the obstacle). An avoidance maneuver should be engaged every time that, by not doing so, the vehicle will enter a collision course with the obstacle. Our control strategy is implemented through a hybrid automaton (see figure 5), covered in greater detail in [17]. The controller starts in a test mode, testing the viability of the left and right avoidance maneuvers separately. Should one of the maneuvers fail (condition R or L), there is still a way to avoid the obstacle, and so the controller transits to the correspondent fail mode. If both maneuvers become inviable simultaneously or, if in fail mode, the remaining maneuver becomes inviable, the controller starts the correspondent avoidance maneuver.
The controllers used to keep the vehicle heading straight or to track a path given by the navigation function are not discussed here. Fig. 6. Block diagram showing how the supervisor controller works. when the mapping process builds its first object and the vehicle’s state is considered unsafe. This transition triggers the SC which steers the vehicle rig ht or left of the detected obstacle to acquire new data. Eventually, the mapping stops detecting obstacles in front of the condition U becomes false. The N computes a path around the mapp point between the vehicle’s current vehicle and the unsafe FC comes into play and ed obstacles to reach a position, q and the target qa. Once that path has been success ully computed, a simple path-tracking controller drives the AUV towards it. If new obstacles are detected or the distance to the closest detected obstacle becomes too small then U becomes true and the Supervisor switches SC back on. I f on the other hand, no new obstacles are detected nor does the vehicle travel close to an obstacle and reaches the end of the computed path, then P becomes false. Consequently, the Supervisor transitions back to the Straight Ahead state.
The controllers used to keep the vehicle heading straight or to track a path given by the navigation function are not discussed here. Fig. 6. Block diagram showing how the supervisor controller works. when the mapping process builds its first object and the vehicle’s state is considered unsafe. This transition triggers the SC which steers the vehicle rig ht or left of the detected obstacle to acquire new data. Eventually, the mapping stops detecting obstacles in front of the condition U becomes false. The N computes a path around the mapp point between the vehicle’s current vehicle and the unsafe FC comes into play and ed obstacles to reach a position, q and the target qa. Once that path has been success ully computed, a simple path-tracking controller drives the AUV towards it. If new obstacles are detected or the distance to the closest detected obstacle becomes too small then U becomes true and the Supervisor switches SC back on. I f on the other hand, no new obstacles are detected nor does the vehicle travel close to an obstacle and reaches the end of the computed path, then P becomes false. Consequently, the Supervisor transitions back to the Straight Ahead state.
Fig. 7. Vehicle’s behavior in a scenario with multiple obstacles. time, since its only goal is determining whether the vehicle should turn right or left. Additionally, note that the obstacles generated by the mapping process are an over-approximation of the real ones to prevent the vehicle from getting too close to them.
Fig. 7. Vehicle’s behavior in a scenario with multiple obstacles. time, since its only goal is determining whether the vehicle should turn right or left. Additionally, note that the obstacles generated by the mapping process are an over-approximation of the real ones to prevent the vehicle from getting too close to them.
‘ig. 8. Vehicle’s behavior in a scenario with a single obstacle.
‘ig. 8. Vehicle’s behavior in a scenario with a single obstacle.

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References (15)

  1. Y. Petillot, I. Tena Ruiz, and D. Lane, "Underwater vehicle obstacle avoidance and path planning using a multi-beam forward looking sonar," IEEE Journal of Oceanic Engineering, vol. 26, no. 2, pp. 240 -251, apr. 2001.
  2. F. Khorrami and P. Krishnamurthy, "A hierarchical path planning and obstacle avoidance system for an autonomous underwater vehicle," in Proc. of the American Control Conference, 2009, pp. 3579-3584.
  3. M. Eichhorn, "A new concept for an obstacle avoidance system for the auv and slocum glider operation under ice," in Proc. of the IEEE OCEANS, 2009, pp. 1-8.
  4. D. P. Horner, A. J. Healey, and S. P. Kragelund, "Auv experiments in obstacle avoidance," in Proc. of the MTS/IEEE OCEANS, 2005, pp. 1464-1470.
  5. X. Wu, Z. Feng, J. Zhu, and R. Allen, "Line of sight guidance with intelligent obstacle avoidance for autonomous underwater vehicles," in Proc. of the MST/IEEE OCEANS, 2006, pp. 1-6.
  6. H. Kawano, "Real-time obstacle avoidance for underactuated au- tonomous underwater vehicles in unknown vortex sea flow by the mdp approach," in Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, 2006, pp. 3024- 3031.
  7. L. E. Dubins, "On curves of minimal length with a constraint on average curvature and with prescribed initial and terminal positions and tangents," American Journal of Mathematics, vol. 79, pp. 497- 516, 1957.
  8. A. E. Johnson and M. Hebert, "Seafloor map generation for autonomous underwater vehicle nav- igation," pp. 3-145, 1996. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.4051
  9. M. Chitre, "A high-frequency warm shallow water acoustic communications channel model and measurements," The Journal of the Acoustical Society of America, vol. 122, no. 5, pp. 2580-2586, Nov. 2007, PMID: 18189549. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/18189549
  10. J. Borenstein, M. Ieee, Y. Koren, and S. M. Ieee, "Histogramic In-Motion mapping for mobile robot obstacle avoidance," IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, vol. 7, pp. 535-539, 1991. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.22.2998
  11. P. V. C. Hough, "Machine analysis of bubble chamber pictures," in International Conference on High Energy Accelerators and Instru- mentation, CERN, Geneva, Switzerland, 1959.
  12. E. Rimon and D. E. Koditschek, "Exact robot navigation using artificial potential functions," IEEE Transactions on Robotics and Automation, vol. 8, no. 5, October 1992.
  13. G. Roussos, G. Chaloulos, K. Kyriakopoulos, and J. Lygeros, "Control of multiple non-holonomic air vehicles under wind uncertainty using model predictive control and decentralized navigation functions," in Decision and Control, 2008. CDC 2008. Proc. of the 47th IEEE Conference on, dec. 2008, pp. 1225 -1230.
  14. G. Roussos, D. V. Dimarogonas, and K. J. Kyriakopoulos, "3d naviga- tion and collision avoidance for nonholonomic aircraft-like vehicles," International Journal of Adaptive Control and Signal Processing, vol. 24, no. 10, pp. 900-920, 2010.
  15. J. F. Cardoso, "Invariance and control of autonomous vehicles," Master's thesis, FEUP, 2010.

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Autonomous Robots, 2015

A robust obstacle detection and avoidance system is essential for long term autonomy of autonomous underwater vehicles (AUVs). Forward looking sonars are usually used to detect and localize obstacles. However, high amounts of background noise and clutter present in underwater environments makes it difficult to detect obstacles reliably. Moreover, lack of GPS signals in underwater environments leads to poor localization of the AUV. This translates to uncertainty in the position of the obstacle relative to a global frame of reference. We propose an obstacle detection and avoidance algorithm for AUVs which differs from existing techniques in two aspects. First, we use a local occupancy grid that is attached to the body frame of the AUV, and not to the global frame in order to localize the obstacle accurately with respect to the AUV alone. Second, our technique adopts a probabilistic framework which makes use of probabilities of detection and false alarm to deal with the high amounts of noise and clutter present in the sonar data. This local probabilistic occupancy grid is used to extract potential obstacles which are then sent to the command and control (C2) system of the AUV. The C2 system checks for possible collision and carries out an evasive maneuver accordingly. Experiments are carried out to show the viability of the proposed algorithm.

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Related topics

  • Geography
  • Computer Science
  • Autonomous Underwater Vehicle
  • Collision Avoidance
  • Obstacle Avoidance
  • Underwater vehicles
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