Embodiments of the present invention are an unmanned aerial vehicle (UAV) severe low-power protection method and a UAV. The method includes: first acquiring ground environment information when the UAV is in a severe low-power protection state, and then obtaining landing safety judgment information according to the ground environment information, and further controlling a flight state of the UAV according to the landing safety judgment information to realize a safe landing of the UAV. The foregoing method reduces the probability of explosion of the UAV, avoids injury accidents, and improves flight safety when the UAV is in a severe low-power state.

Embodiments of the present invention are an unmanned aerial vehicle (UAV) severe low-power protection method and a UAV. The method includes: first acquiring ground environment information when the UAV is in a severe low-power protection state, and then obtaining landing safety judgment information according to the ground environment information, and further controlling a flight state of the UAV according to the landing safety judgment information to realize a safe landing of the UAV. The foregoing method reduces the probability of explosion of the UAV, avoids injury accidents, and improves flight safety when the UAV is in a severe low-power state.

Sensor data that includes or more of the following: (1) aircraft state information associated with an aircraft or (2) parachute canopy state information associated with a parachute canopy is received. The parachute canopy is coupled to the aircraft at a point aft of a center of mass of the aircraft. It is determined, based at least in part on the sensor data, whether to generate a control signal associated with maneuvering the aircraft into a nose-up position. A recovery action is performed, including by deploying the parachute canopy; wherein a load on the parachute canopy is reduced in the event the aircraft is in the nose-up position compared to the aircraft being in a nose-down position.

A control system that is operable to control a vehicle in an autonomous or semi-autonomous mode includes a processor that processes data captured by a plurality of exterior sensing sensors. When the control system is operating in the autonomous or semi-autonomous mode and responsive to a determination of an upcoming event that requires the system to hand over control of the vehicle to a driver before the vehicle encounters the event, the control determines (i) a total action time until the vehicle encounters the event, (ii) an estimated time for the driver to take over control and (iii) an estimated handling time for the vehicle to be controlled before the vehicle encounters the event. Responsive to the determinations, the control system (i) allows the driver to take over control of the vehicle or (ii) controls the vehicle to slow down and stop the vehicle before the vehicle encounters the event.

A piloting device arranged to be integrated in a pre-existing aircraft that includes original systems comprising both a flight control system and an autopilot system is distinct from and autonomous relative to the original systems and includes a positioning unit and a control unit. The positioning unit is arranged to produce positioning data for the pre-existing aircraft. The control unit is arranged to perform a geofencing function from the positioning data produced by the positioning unit and to produce an alternative piloting setpoint for the pre-existing aircraft. The alternative piloting setpoint is adapted to supplement both a manual piloting setpoint produced by a pilot of the pre-existing aircraft via the flight control system and also an autopilot setpoint produced by the autopilot system.

A motion planner of an autonomous vehicle's computer system uses reconfigurable collision detection architecture hardware to perform a collision assessment on a planning graph for the vehicle prior to execution of a motion plan. For edges on the planning graph, which represent transitions in states of the vehicle, the system sets a probability of collision with a dynamic object in the environment based at least in part on the collision assessment. Depending on whether the goal of the vehicle is to avoid or collide with a particular dynamic object in the environment, the system then performs an optimization to identify a path in the resulting planning graph with either a relatively high or relatively low potential of a collision with the particular dynamic object. The system then causes the actuator system of the vehicle to implement a motion plan with the applicable identified path based at least in part on the optimization.

Systems and methods are provided for collecting anonymized drive information. A processing device may be configured to receive outputs from one or more sensors; determine at least one motion representation for the host vehicle based on the outputs; receive at least one image representative of an environment of the host vehicle; analyze the at least one image to determine at least one road characteristic associated with a road section; assemble first road segment information relative to a first portion of the road section, wherein the first portion of the road section is separated from a starting point associated with a route traveled by the host vehicle; assemble second road segment information relative to a second portion of the road section; and cause transmission of the first road segment information and the second road segment information to a server for assembly of an autonomous vehicle road navigation model.

To effectively enhance the operation efficiency of an autonomous mobile robot, an autonomous mobile robot control system includes a processor and a plurality of environmental cameras. The processor estimates a moving route of each of a plurality of moving bodies on the basis of characteristics of each of the plurality of moving bodies and sets a subset of the plurality of moving bodies whose moving routes overlap among the detected moving bodies as avoidance processing target moving bodies. The processor generates an avoidance procedure for the avoidance processing target moving bodies so the motion of the avoidance processing target moving bodies does not interfere with the motion of other avoidance target moving bodies.

Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. For instance, a first location corresponding to a location where the vehicle is to pick up or drop off a passenger is received. A first cost for the vehicle to reach the first location is determined. A second location based on the first location is identified, and a second cost is determined based on a cost for the vehicle to reach the second location and a cost for the passenger to reach the second location. The first cost is compared to the second cost, and a notification is sent based on the notification. In response to sending the notification, instructions to proceed to the second location are received, and in response to receiving the instructions, the vehicle is controlled in the autonomous driving mode to the second location to pick up or drop off the passenger.

A circular grazing system for poultry and/or livestock. The circular grazing system including a center pivot structure installed at a field. The center pivot structure may have a center pivot axis. The field may be a poultry and/or livestock grazing field. The circular grazing system for poultry and/or livestock may include an enclosure for containing poultry and/or livestock. The enclosure may extend generally radially from the center pivot structure to a circumference of the field. The enclosure may be rotably coupled to the center pivot structure such that the enclosure rotates around the center pivot axis.