Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 3451864 | 17 pages | https://doi.org/10.1155/2019/3451864

Artificial Potential Function Safety and Obstacle Avoidance Guidance for Autonomous Rendezvous and Docking with Noncooperative Target

Academic Editor: Sajad Azizi
Received25 Apr 2019
Revised22 Jul 2019
Accepted31 Jul 2019
Published21 Aug 2019

Abstract

The problem of artificial potential function (APF) safety and obstacle avoidance guidance for autonomous rendezvous and docking of chaser spacecraft with noncooperative spacecraft is studied. The relative motion equation of the chaser and the target is established based on the line-of-sight coordinate system, the reference state is designed, and the corresponding state error is deduced. The attitude motion equation of the noncooperative target spacecraft in space is established. The safety and obstacle avoidance guidance problem of autonomous rendezvous and docking with noncooperative target is transformed into a path planning problem in a dynamic environment. The attractive potential function is designed according to the state error. In order to ensure that the chaser can safely approach the noncooperative target spacecraft, a safe corridor with ellipse cissoid is designed in the final approaching stage of autonomous rendezvous and docking. The obstacle is assumed to be a sphere with a certain radius to avoid its influence in the approach, and the obstacle potential function is designed based on the Gaussian function method. The total potential function of the system is designed according to the attractive potential function, the safe potential function, and the obstacle potential function. The total potential function of the system is modified to ensure that the reference state is the minimum of the total potential function of the system. The stability of the system is proven according to the Lyapunov stability principle, and the conditions for satisfying the monotonic decrease in the total potential function of the system are deduced. Finally, the effectiveness of the proposed method is verified by three sets of numerical simulations.

1. Introduction

In recent years, with the rapid development of space technology, extensive attention has been drawn to autonomous on-orbit service technology, such as on-orbit assembly, fuel injection, component replacement, and on-orbit repair for uncontrolled spacecraft [16]. Compared with the cooperative target spacecraft with stable attitude in space, the uncontrolled target spacecraft generally performs free-tumbling attitude motion, showing strong noncooperative characteristics. Unlike the cooperative situations, when approaching the noncooperative target, the desired position of the chaser varies with time. To fulfill these tasks, autonomous rendezvous and docking with noncooperative target lays as the cornerstone for unmanned chaser spacecraft [79]. However, the space environment is complex because of space debris and accessories of noncooperative targets such as antennas, which are overwhelming obstacles for autonomous rendezvous and docking. Therefore, a guidance system is crucial to avoid collision with space debris and ensure chaser’s safe approach to the noncooperative target spacecraft.

Some typical robust adaptive control methods are applied to the autonomous rendezvous and docking problem. Aiming at the measurement uncertainty and input saturation of spacecraft’s rendezvous and docking, Singla et al. designed an adaptive output feedback controller [10]; Lee and Vukovich proposed a robust adaptive terminal sliding mode control with finite-time convergence [11]; Xia and Huo designed a robust adaptive backstepping neural network controller [12]; Sun et al. proposed a robust saturation control strategy [13]; Kasaeian et al. conceived sliding mode prediction guidance method [14]; Park et al. and Zhu et al. [15, 16] proposed the model predictive control method; and Yan et al. used the inverse push scheme to design the control law [17]. Considering the total coupling uncertainty dynamics and thrust eccentricity of the rendezvous and docking process, Sun designed an adaptive sliding mode controller [18, 19]. In addition, some traditional optimization methods are also applied in this process. Harris studied the minimum rendezvous time between multiple spacecraft based on the differential drag method [20]. Boyarko et al. studied the minimum time and minimum energy optimal trajectory for the rendezvous and docking of two spacecraft in a circular orbit [21]. Oghim et al. studied the minimum velocity change and wait time for noncoplanar and general orbits to achieve optimal rendezvous [22]. Duan et al. used fuel consumption as a constraint and designed a fuel optimal controller for rendezvous and docking of noncooperative targets [23]. Ciarcià et al. designed a trajectory planner based on inverse dynamics with minimum energy as optimization objective [24]. Huang and Jia studied the finite-time rendezvous and docking problem with noncooperative targets [25]. Breger and How proposed an active safety method, which regarded the safety of rendezvous and docking process as a constraint and guaranteed safety without additional fuel consumption [26]. Jacobsen et al. used genetic algorithms to optimize the safety and fuel, and since the safety is only considered as a goal rather than a constraint, its rendezvous trajectory is actually unsafe [27].

The artificial potential function (APF) guidance method and trajectory curvature guidance method [28] show superior obstacle avoidance performance in path planning applications. The APF guidance method derived from robot path planning has the advantages of theoretically proven stability and high computational efficiency. It has successfully been applied to the spacecraft autonomous rendezvous and docking and autonomous path planning problems. Cao et al. conceived a suboptimal APF sliding mode control method [29], and Feng et al. designed a new sliding mode control strategy based on APF [30]. Dong et al. proposed a real-time adaptive control law, which introduced the potential function method to constrain the path of the chaser [31] and then drew into a special artificial potential field method based on double quaternion to constrain the track of the chaser [32]. Chen et al. used the artificial potential field method to perform on-orbit assembly of four flexible spacecraft, and the proposed control strategy can protect the chaser spacecraft against collision before reaching the desired position [33]. Bloise designed the obstacle avoidance guidance algorithm by combining artificial potential field theory with sliding mode technology [34]. In terms of safety research on rendezvous and docking with noncooperative target, Zhang et al. concentrated on obstacle avoidance using artificial potential field guidance [35].

However, the above research is not without flaws. First, most relative motion equations are based on the circular orbits of the Clohessy–Wiltshire (CW) equation, and the relative motion between spacecraft is described by the orbital coordinate system, which requires high-precision telemetry and filtering on the ground. Those methods often result in poor real-time performance and large position error. Secondly, the relevant research focuses more on the cooperative target but less on the noncooperative target. Besides, risky debris as the cause of collisions in the space is often ignored since research concerning rendezvous and docking safety centers on avoiding collisions with the accessories of the target.

Therefore, based on the above considerations, the relative motion equation is established in line-of-sight coordinate system in this paper, which is applicable to any orbit. The relative motion between the chaser and the target mainly depends on the sensor of the chaser itself, which delivers better real-time performance and smaller position error. Secondly, the method proposed in this paper is aimed at noncooperative target and is more widely used. Finally, the method proposed here can not only achieve safe rendezvous and docking but also avoid the influence of space debris.

The rest sections are as follows: Section 2 introduces the relative motion equation between the chaser and the target and deduces the attitude motion equation of the target. Section 3 establishes the attractive potential function, the safe potential function, and the obstacle potential function, respectively. The time derivative of each potential function is deduced, and the total potential function of the system is established and modified. The stability of the system is analyzed by using Lyapunov theory, and the control acceleration of the chaser is deduced in Section 4. Simulation studies performed on autonomous rendezvous and docking with noncooperative target are presented in Section 5. Section 6 draws conclusions.

2. Relative Dynamics and Attitude Motion

2.1. Relative Dynamics

The relative motion between the chaser and the target is shown in Figure 1, where is the Earth-centered inertial coordinate system, is the line-of-sight coordinate system, and is the body coordinate system of the target. and are the position vectors of the chaser’s centroid and target’s centroid in the frame , respectively. is the position vector of the target relative chaser described in the frame , and the relative distance between the two spacecraft is . is the azimuth angle, is the altitude angle, and and are collectively referred to as position angles.

The relative motion equation between two spacecraft described in the frame is [36]where and are the acceleration vectors of the target and the chaser in the frame , respectively. is the gravitational difference between the two spacecraft. When is small, can be ignored. It can be seen from equation (1) that the relative motion equation is only related to the relative position , the azimuth angle , and the altitude angle and is independent of the target’s orbit, so the relative motion equation is applicable to any orbit. The attitude transfer matrix from the frame to the frame is , and its expression is

Equation (1) shows that there is a strong coupling between and , and both and are positive numbers. The state variables are defined as , , , , , and . Then, equation (1) yields

The reference values of relative distance and position angle are set as , , , , , and . Then, the error variables can be defined as

The error vectors are set as and . The current state are and , and the reference state is . Then, the following expression can be obtained:

The time derivatives of equations (5) and (6) givewhere

2.2. Attitude Motion

The attitude motion model of the frame relative to the frame of the uncontrolled tumbling target spacecraft under the condition of no external torque is [11]where is the inertial matrix of target and is the angular velocity vector of the target in the frame . represents the skew-symmetric matrix of the vector’s cross multiplication. In this paper, for any vector , there is

In order to facilitate the analysis, the quaternion is used to describe the attitude of spacecraft. The attitude kinematics equation described by the quaternion iswhere and . The attitude transfer matrix from the frame to the frame is

The docking position is in the frame , and then the desired position of chaser in the frame is

Since the target is in an uncontrolled tumbling state in space, the desired position is continuously changing, and the , , , and are also continuously changing.

3. Artificial Potential Function

The artificial potential function (APF) developed in recent years was originally used for path planning of robots. APF has theoretically been proven of its stability and high computational efficiency [37, 38], which can be applied to path planning of autonomous rendezvous and docking process [2834]. The principle of APF guidance is to define an APF reflecting global motion of the chaser. In this paper, the APF is the total potential function of the system, which is composed of the attractive potential function, the safe potential function, and the obstacle potential function. Represented by the attractive potential function, the reference position applies gravity to the chaser. The APF has a global minimum at the reference position. Represented by the safe potential function, the safe corridor exerts repulsive force on the chaser. The obstacle potential function is used to represent the obstacle exerting repulsive force on the chaser. Safe corridor and obstacles constitute the path restricted area of the chaser. The APF has a global large value in the path restricted area. The Lyapunov stability theory is used to analyze the APF, and the appropriate control law is found to be guaranteeing the time derivative of the APF is negative, thus keeping the APF monotonously decreasing. The chaser can be ensured to effectively avoid the path restricted area while reaching the reference position, thus achieving the desired guidance.

3.1. Attractive Potential Function

The attractive potential function representing reference position is designed aswhere and are positive definite symmetric matrices. Equation (15) shows that when and , , otherwise . The reference position ( and ) is the minimum value of the attractive potential function.

3.2. Safe Potential Function

During autonomous rendezvous and docking with noncooperative targets, the chaser must avoid colliding with accessories such as solar sails and antennas. Therefore, it is necessary to construct a safe corridor to restrict the chaser’s path to the entire corridor. In this paper, the safe corridor is defined as the conical interior formed by rotating around the docking axis of the target at a safe angle . Within the safe distance of the target, the angle between the chaser’s line-of-sight and the docking axis of the target must satisfy the following condition:

In order to leave a safe margin, the elliptical cissoid surface rotated by the elliptical vine curve shown in Figure 2 around the docking axis of the target is used as the path restricted of the chaser during the rendezvous and docking process. The elliptic vine curve equation is as follows:where can be considered as the representation of the safe distance, while represents the safe angle. In order to facilitate the analysis, the -axis of the frame is used as the docking axis. The elliptical cissoid surface is obtained by rotating the elliptical vine curve around the -axis, and the inside of the surface is the safe corridor. As shown in Figure 3, the surface equation is

The Gaussian function is used to represent the path restriction area. Therefore, the expression of the safe potential function based on the elliptical cissoid surface is [39]where indicates the height of the Gaussian function and indicates the width of the Gaussian function. is the position vector of the chaser relative to the target described in the frame , and the expression of is

Since the attitude of the target is continuously changing, the safe corridor surface fixed to the target is also continuously changing, which is a dynamic safe corridor. At the same time, changes with the change in . As can be seen from equation (19), when the chaser is away from the curved surface, is larger and is smaller. When the chaser approaches the surface, is smaller and is larger. The appropriate is set to ensure that the chaser is constrained to move inside the curved surface to satisfy the requirements of safe rendezvous and docking.

The time derivative of equation (19) gives

Since the design requires that the chaser cannot pass through the point of the highest safe potential function, i.e., is required, when calculating the time derivative of the potential function in equation (19), cannot take absolute value, i.e.,

The other partial time derivatives in equation (21) are solved as follows:

and yield

Equations (13) and (20) yieldwhere

3.3. Obstacle Potential Function

The obstacle is regarded as a sphere with a certain radius, and the expression of the obstacle potential function iswhere represents the height of the potential function and can be considered as an approximate representation of the obstacle’s radius. is the position vector of the chaser relative to the spherical obstacle described in the frame , and the expression of iswhere is the position vector of the target relative to the chaser described in the frame and is the position vector of the spherical obstacle relative to the target described in the frame . When is unchanged, the obstacle remains hovering with the target. When changes, the obstacle is in relative motion with the target.

The time derivative of equation (27) giveswhere

Equation (13) and the expression of yieldwhere

3.4. Total Potential Function

The total potential function is the sum of the attractive potential function , the safe potential function , and the obstacle potential function , and the expression of is

It can be concluded from equation (33) that the minimum value of is no longer precisely located in the reference position state. To avoid the above situation, the safe potential function of equation (19) and the obstacle potential function of equation (27) are redefined, each adding a quadratic term, which yieldswhere and are positive definite symmetric matrices. Then, the modified system total potential function expression is

As can be seen from equation (36), the total potential function has a global minimum at the reference position ( and ). It should be pointed out that, in order to facilitate the analysis, equation (36) only considers the case of single obstacle. When there are multiple obstacles, it only needs to change in equation (35) to .

4. Proof of System Stability

Proof. The modified total potential function is taken as Lyapunov function , i.e.,As can be obtained from equation (37), . only when and , otherwise . When and , the chaser has reached the desired position. The time derivative of equation (37) givesSubstituting equations (7) and (8) into equation (38) yieldsLetwhere is positive definite symmetric matrix. is the generalized inverse matrix of , and the expression of isEquation (40) yieldswherewhere represents the gravitational acceleration of the reference position potential field to the chaser, represents the repulsive acceleration of the safe corridor potential field to the chaser, and represents the repulsive acceleration of the obstacle potential field to the chaser. Substituting equation (40) into (39) yieldsThis case completes the proof.
The control acceleration vector of the system in the frame is derived from equations (46) and (3):Due to the limited thrust of the chaser, its control acceleration is limited, and the actual control acceleration in each direction iswhere is the maximum acceleration that can be provided by the chaser in a single direction and is the acceleration obtained by using equation (46).

5. Simulation Example

In this paper, the numerical simulation of the autonomous rendezvous and docking mission of the chaser spacecraft relative to the noncooperative target spacecraft in super close range is carried out. In order to visually analyze the effect of attractive potential function, safe potential function, and obstacle potential function, three sets of simulation scenarios are set up in this paper:(1)The action of the attractive potential function alone is considered(2)The actions of both the attractive potential function and safe potential function are considered(3)The actions of all he attractive potential function, safe potential function, and obstacle potential function are considered

It is assumed that the target spacecraft is in free-tumbling state without external torque in space, and there is no maneuver in the target. The initial states of the target and chaser in the frame are

The docking position in the frame is . The initial attitude quaternion and angular velocity of the target are and . The maximum control acceleration in each direction provided by the chaser is . The parameters of the attractive potential function are , , and . The parameters of the safe potential function are , , and . The parameters of the obstacle potential function are , , and . The parameters of the elliptical vine curve function are and . The safe distance of the target’s safe corridor is , and the safe angle is . The moment of inertia of the target is

The simulation time is 200 s, and the simulation step is 0.05 s.

Scenario 1. The action of the attractive potential function alone is considered. and in equation (42) are set as and , respectively. The simulation results are presented in Figures 410.

Figure 4 shows the attitude and angular velocity of the target spacecraft, and the attitude and angular velocity of the target vary with time. Figure 5 shows the relative position and relative velocity of the chaser and the desired position. Figure 6 shows the errors of states. After 146 s, the chaser reaches the desired position, and the relative velocity of the chaser to the desired position is , after which the relative position and relative velocity of the chaser to the desired position remain unchanged.

Figure 7 shows the relative motion of the chaser and target in the frame . Figure 8 shows the safe profile. From Figures 7 and 8, it can be conclude that the chaser crosses the safe corridor during the autonomous rendezvous and docking, and the chaser may collide with the accessories of the noncooperative target.

Figure 9 shows the control acceleration in the frame . In order to keep up with the desired position, the chaser’s line-of-sight direction (x-direction) always needs to provide a control acceleration, which changes back and forth between −0.2 m/s2 and 0.2 m/s2. The control acceleration in the y direction and the z direction changes dramatically at the initial time. After 81 s, the control acceleration in the y direction and the z direction maintained at an extremely small value of less than 0.002 m/s2.

Scenario 1 shows that when the safe requirements are not considered, the autonomous rendezvous and docking with the noncooperative target by the chaser can be realized by the guidance of the attractive potential function. When considering the safe requirements, it is obvious that the attractive potential function alone cannot satisfy the requirements of safe rendezvous and docking.

Scenario 2. The action of the attractive potential function and safe potential function are both considered, and the other simulation conditions are the same as scenario 1. in equation (42) is set as . The simulation results are presented in Figures 1014.

It can be concluded from Figure 10 that the relative position of the chaser and the desired position is after 103 s, and the relative velocity is . Figures 12 and 13 indicate that when considering the safe potential function, the chaser first approaches the target’s docking direction, and then slowly approaches the target along the docking direction without colliding with the safe corridor. Figure 14 shows that, after , the control acceleration in the y direction and z direction tends to , and the control acceleration in the x direction changes back and forth between and . Scenario 2 indicates that when considering the action of the safe potential function, the APF guidance can realize rendezvous and docking with noncooperative targets along the safe corridor. During the rendezvous and docking process, collision with the accessories of the target can be avoided, and the safe requirements can be guaranteed.

Scenario 3. Adding an obstacle to the path of autonomous rendezvous and docking in Scenario 2, the action of the attractive potential function, safe potential function, and obstacle potential function are all considered. The simulation results are presented in Figures 1519.

Figure 15 shows that the chaser has reached the desired position, the relative position is reduced to , and the relative speed is reduced to . Figures 17 and 18 indicate that the chaser can effectively avoid obstacle and achieve safe rendezvous and docking with noncooperative target when considering the action of the obstacle potential function. Figure 19 shows that, after a period of time, the control acceleration in the y direction and z direction tends to , and the control acceleration in the x direction changes back and forth between and .

Scenario 3 indicates that when considering the action of the obstacle potential function, the APF guidance can ensure the chaser effectively avoids the obstacles. The chaser approaches the noncooperative target along the safe corridor, which ensures the guidance requirements of safe rendezvous and docking and obstacle avoidance.

6. Conclusions

The problem of the APF safe approach and obstacle avoidance for autonomous rendezvous and docking of chaser to noncooperative target spacecraft is studied. The relative motion equation of the chaser relative to the noncooperative target spacecraft is established, and the attitude motion characteristics of the target spacecraft are analyzed. The attractive potential function, safe potential function, and obstacle potential function are designed. The time derivatives of the safe potential function and the obstacle potential function are obtained, respectively. The total potential function of the system is derived and modified. The stability of the system is analyzed according to Lyapunov theory, and the control acceleration of the chaser is derived. Finally, the numerical simulation is carried out, and the conclusions are as follows:(1)The autonomous rendezvous and docking with the noncooperative target by the chaser can be realized by the guidance of the attractive potential function. When considering the safe requirements, it is obvious that the attractive potential function alone cannot satisfy the requirements of safe rendezvous and docking.(2)When considering the action of the safe potential function, the APF guidance can realize rendezvous and docking with noncooperative targets along the safe corridor. During the rendezvous and docking process, collision with the accessories of the target can be avoided, and the safety requirements can be guaranteed.(3)When considering the action of the obstacle potential function, the APF guidance can ensure the chaser effectively avoids the obstacles. The chaser approaches the noncooperative target along the safe corridor, which ensures the guidance requirements of safe rendezvous and docking and obstacle avoidance.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interests.

Acknowledgments

The authors are grateful to Prof. Li and Scholar Hao for discussions. This paper was supported by the National Natural Science Foundation of China (no. 11472301).

References

  1. S. Bandyopadhyay, S.-J. Chung, and F. Y. Hadaegh, “Nonlinear attitude control of spacecraft with a large captured object,” Journal of Guidance, Control, and Dynamics, vol. 39, no. 4, pp. 1–16, 2016. View at: Publisher Site | Google Scholar
  2. L. Zhang, S. Qian, S. Zhang, and H. Cai, “Research on angles-only/SINS/CNS relative position and attitude determination algorithm for a tumbling spacecraft,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 231, no. 2, pp. 218–228, 2017. View at: Publisher Site | Google Scholar
  3. Y. She, J. Sun, S. Li, and T. Song, “Quasi-model free control for the post-capture operation of a non-cooperative target,” Acta Astronautica, vol. 147, pp. 50–70, 2018. View at: Publisher Site | Google Scholar
  4. G. Zhao, S. Xu, and Y. Bo, “LiDAR-based non-cooperative tumbling spacecraft pose tracking by fusing depth maps and point clouds,” Sensors, vol. 18, no. 10, p. 3432, 2018. View at: Publisher Site | Google Scholar
  5. M. Wang, J. J. Luo, J. P. Yuan, and U. Walter, “Detumbling strategy and coordination control of kinematically redundant space robot after capturing a tumbling target,” Nonlinear Dynamics, vol. 92, no. 3, pp. 1023–1043, 2018. View at: Publisher Site | Google Scholar
  6. Z. Ma, Y. Wang, Y. Yang, Z. Wang, L. Tang, and S. Ackland, “Reinforcement learning-based satellite attitude stabilization method for non-cooperative target capturing,” Sensors, vol. 18, no. 12, p. 4331, 2018. View at: Publisher Site | Google Scholar
  7. C. Zagaris and M. Romano, “Reachability analysis of planar spacecraft docking with rotating body in close proximity,” Journal of Guidance, Control, and Dynamics, vol. 41, no. 6, pp. 1416–1422, 2018. View at: Publisher Site | Google Scholar
  8. K.-B. Li, W.-S Su, and L. Chen, “Performance analysis of differential geometric guidance law against high-speed target with arbitrarily maneuvering acceleration,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 233, no. 10, pp. 3547–3563, 2018. View at: Publisher Site | Google Scholar
  9. M. Ning, S. Zhang, and S. Wang, “A non-cooperative satellite feature point selection method for vision-based navigation system,” Sensors, vol. 18, no. 3, p. 854, 2018. View at: Publisher Site | Google Scholar
  10. P. Singla, K. Subbarao, and J. L. Junkins, “Adaptive output feedback control for spacecraft rendezvous and docking under measurement uncertainty,” Journal of Guidance, Control, and Dynamics, vol. 29, no. 4, pp. 892–902, 2006. View at: Publisher Site | Google Scholar
  11. D. Lee and G. Vukovich, “Robust adaptive terminal sliding mode control on SE (3) for autonomous spacecraft rendezvous and docking,” Nonlinear Dynamics, vol. 83, no. 4, pp. 2263–2279, 2015. View at: Publisher Site | Google Scholar
  12. K. Xia and W. Huo, “Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with uncertainties,” Nonlinear Dynamics, vol. 84, no. 3, pp. 1683–1695, 2016. View at: Publisher Site | Google Scholar
  13. L. Sun, W. Huo, and Z. Jiao, “Disturbance observer-based robust relative pose control for spacecraft rendezvous and proximity operations under input saturation,” IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 4, pp. 1605–1617, 2018. View at: Publisher Site | Google Scholar
  14. S. A. Kasaeian, N. Assadian, and M. Ebrahimi, “Sliding mode predictive guidance for terminal rendezvous in eccentric orbits,” Acta Astronautica, vol. 140, pp. 142–155, 2017. View at: Publisher Site | Google Scholar
  15. H. Park, S. D. Cairano, and I. Kolmanovsky, “Model predictive control for spacecraft rendezvous and docking with a rotating/tumbling platform and for debris avoidance,” in Proceedings of the 2011 American Control Conference, IEEE, San Francisco, CA, USA, June-July 2011. View at: Publisher Site | Google Scholar
  16. S. Zhu, R. Sun, J. Wang et al., “Robust model predictive control for multi-step short range spacecraft rendezvous,” Advances in Space Research, vol. 62, no. 1, pp. 111–126, 2018. View at: Publisher Site | Google Scholar
  17. H. Yan, S. Tan, and Y. Xie, “Integrated translational and rotational control for the final approach phase of rendezvous and docking,” Asian Journal of Control, vol. 20, no. 5, pp. 1967–1978, 2018. View at: Publisher Site | Google Scholar
  18. L. Sun and Z. Zheng, “Adaptive sliding mode control of cooperative spacecraft rendezvous with coupled uncertain dynamics,” Journal of Spacecraft and Rockets, vol. 54, no. 3, pp. 652–661, 2017. View at: Publisher Site | Google Scholar
  19. L. Sun and Z. Zheng, “Adaptive relative pose control for autonomous spacecraft rendezvous and proximity operations with thrust misalignment and model uncertainties,” Advances in Space Research, vol. 59, no. 7, pp. 1861–1871, 2017. View at: Publisher Site | Google Scholar
  20. M. W. Harris and B. Açıkmeşe, “Minimum time rendezvous of multiple spacecraft using differential drag,” Journal of Guidance Control & Dynamics, vol. 2, no. 2, pp. 365–373, 2014. View at: Publisher Site | Google Scholar
  21. G. Boyarko, M. O. Yakimenko, and M. Romano, “Optimal rendezvous trajectories of a controlled spacecraft and a tumbling object,” Journal of Guidance, Control, and Dynamics, vol. 34, no. 4, pp. 1239–1252, 2011. View at: Publisher Site | Google Scholar
  22. S. Oghim, S.-H. Mok, and H. Leeghim, “Optimal spacecraft rendezvous by minimum velocity change and wait time,” Advances in Space Research, vol. 60, no. 6, pp. 1188–1200, 2017. View at: Publisher Site | Google Scholar
  23. G. R. Duan, D. K. Gu, and B. Li, “Optimal control for final approach of rendezvous with non-cooperative target,” Pacific Journal of Optimization, vol. 6, no. 3, pp. 521–532, 2010. View at: Google Scholar
  24. M. Ciarcià, J. Ventura, M. Romano et al., “An inverse dynamics-based trajectory planner for autonomous docking to a tumbling target,” in Proceedings of the Aiaa Guidance, Navigation, & Control Conference, San Diego, California, USA, January 2016. View at: Publisher Site | Google Scholar
  25. Y. Huang and Y. Jia, “Adaptive fixed-time relative position tracking and attitude synchronization control for non-cooperative target spacecraft fly-around mission,” Journal of the Franklin Institute, vol. 354, no. 18, pp. 8461–8489, 2017. View at: Publisher Site | Google Scholar
  26. L. S. Breger and J. P. How, “Safe trajectories for autonomous rendezvous of spacecraft,” Journal of Guidance, Control, and Dynamics, vol. 31, no. 5, pp. 1478–1489, 2008. View at: Publisher Site | Google Scholar
  27. S. Jacobsen, C. Lee, C. Zhu, and S. Dubowsky, “Planning of safe kinematic trajectories for free flying robots approaching an uncontrolled spinning satellite,” in Proceedings of the ASME 27th Annual Biennial Mechanisms and Robotics Conference, American Society of Mechanical Engineers (ASME), Montreal, Canada, September-October 2002. View at: Google Scholar
  28. P. Cui, T. Qin, S. Zhu, Y. Liu, R. Xu, and Z. Yu, “Trajectory curvature guidance for Mars landings in hazardous terrains,” Automatica, vol. 93, pp. 161–171, 2018. View at: Publisher Site | Google Scholar
  29. L. Cao, D. Qiao, and J. Xu, “Suboptimal artificial potential function sliding mode control for spacecraft rendezvous with obstacle avoidance,” Acta Astronautica, vol. 143, pp. 133–146, 2018. View at: Publisher Site | Google Scholar
  30. L. Feng, Q. Ni, Y. Bai, X. Chen, and Y. Zhao, “Optimal sliding mode control for spacecraft rendezvous with collision avoidance,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC), IEEE, Vancouver, BC, Canada, July 2016. View at: Publisher Site | Google Scholar
  31. H. Dong, Q. Hu, and M. R. Akella, “Safety control for spacecraft autonomous rendezvous and docking under motion constraints,” Journal of Guidance, Control, and Dynamics, vol. 40, no. 7, pp. 1640–1692, 2017. View at: Publisher Site | Google Scholar
  32. H. Dong, Q. Hu, and M. R. Akella, “Dual-quaternion-based spacecraft autonomous rendezvous and docking under six-degree-of-freedom motion constraints,” Journal of Guidance, Control, and Dynamics, vol. 41, no. 5, pp. 1150–1162, 2017. View at: Publisher Site | Google Scholar
  33. T. Chen, H. Wen, H. Hu, and D. Jin, “On-orbit assembly of a team of flexible spacecraft using potential field based method,” Acta Astronautica, vol. 133, pp. 221–232, 2017. View at: Publisher Site | Google Scholar
  34. N. Bloise, E. Capello, M. Dentis, and E. Punta, “Obstacle avoidance with potential field applied to a rendezvous maneuver,” Applied Sciences, vol. 7, no. 10, p. 1042, 2017. View at: Publisher Site | Google Scholar
  35. D. W. Zhang, S. M. Song, R. Pei et al., “Ellipse cissoid-based potential function guidance for autonomous rendezvous and docking with non-cooperative target,” Journal of Astronautics, vol. 31, no. 10, pp. 2259–2268, 2010. View at: Google Scholar
  36. H.-S. Shin, A. Tsourdos, and K.-B. Li, “A new three-dimensional sliding mode guidance law variation with finite time convergence,” IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 5, pp. 2221–2232, 2017. View at: Publisher Site | Google Scholar
  37. D. Park, H. Hoffmann, P. Pastor, and S. Schaal, “Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields,” in Proceedings of the 8th IEEE-RAS International Conference on Humanoid Robots, pp. 91–98, Daejeon, South Korea, December 2008. View at: Google Scholar
  38. S. Yang and J. Yu, “Stability analysis and variational integrator for real-time formation based on potential field,” Mathematical Problems in Engineering, vol. 2014, Article ID 602424, 13 pages, 2014. View at: Publisher Site | Google Scholar
  39. L. T. Luh, “The shape parameter in the Gaussian function,” Computers & Mathematics with Applications, vol. 63, no. 3, pp. 687–694, 2010. View at: Publisher Site | Google Scholar

Copyright © 2019 Jianghui Liu and Haiyang Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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